OpenVINO Blog

Welcome back to our blog series on"Intel Edge AI in the Realm of Biopharma and Drug Development." Inthe first installment, we discussed the importance of Cell Analytics forAntibody Production in biopharmaceutical technology and drug development. Wehighlighted how AI pipelines are used to process brightfield images of cells,providing insights and addressing challenges in this field. Specifically, weexplored the CHO Cell Segmentation Use Case and noted that Intel has developeda reference implementation for deploying the CHO Cell Segmentation pipelineusing Intel edge AI software solutions.

Now, let's delve deeper into thespecifics of these Edge AI solutions: the OpenVINO toolkit, OpenVINO ModelServer, and AI Connect for Scientific Data. We'll explore how each of thesetools can play a crucial role in advancing biopharma and drug development.

OpenVINO™Toolkit

optimizes, tunes, and runscomprehensive deep learning inferencing on general-purpose Intel architecture. Itis an open-source toolkit that accelerates AI inference with lower latency andhigher throughput while maintaining accuracy, reducing model footprint,and optimizing hardware use. It streamlines AI development and integration ofdeep learning in domains like computer vision, large language models (LLM), andgenerative AI.​

At the core of the OpenVINOtoolkit, we have the OpenVINO Runtime that loads and runs the models.The run-time employs plugins that are responsible for efficiently executing low-leveloperations that the deep learning model has on Intel HW. We have differentplug-ins for different HW, like CPU plugins, GPU plugins, and heterogeneousplugins.

The CPU plugin achieves a highperformance of neural networks on the CPU, using the Intel® Math Kernel Libraryfor Deep Neural Networks (Intel® MKL-DNN).

The GPU plugin uses the Intel® ComputeLibrary for Deep Neural Networks (clDNN) to infer deep neural networks on GPUs.

The heterogeneous plugin enablescomputing the inference of one network on several devices. The purposes ofexecuting networks in heterogeneous mode are to:

·        Utilize the power of accelerators to processthe heaviest parts of the network and to execute unsupported layers on fallbackdevices like the CPU.

·        Utilize all available hardware moreefficiently during one inference.

Another part of the OpenVINO toolkitis the model optimizer which optimizes and converts the model frompopular deep learning frameworks like TensorFlow, PyTorch, and ONNX, to OpenVINOintermediate representation format. The models are optimized withtechniques such as quantization, freezing, fusion, and more. Models can bedeployed across a mix of Intel® hardware and environments, on-premise andon-device, in the browser, or the cloud.

Besidesinference, OpenVINO provides the NeuralNetwork Compression Framework (NNCF) tool for implementing compressionalgorithms on models during training.

Figure1: OpenVINO™ overview. For detailed documentation about OpenVINO™ see: https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html.

OpenVINO™ Model Server (OVMS)

When it comes to deployment, youcan use OpenVINO Runtime, or you can use OpenVINO Model Server orOVMS for short.

OVMS is a scalable,high-performance tool for serving AI models and pipelines. It centralizes AI modelmanagement, ensuring consistent AI models across numerous devices, clouds,or compute nodes. Simply put, OVMS is a microservice that loads yourmodels, manages them, and exposes their capabilities through a network API,allowing other system components to interact with and utilize these models.OVMS supports two types of APIs—TensorFlow Serving and KServe compatible—whichprovide inference, model status, and model metadata services via gRPC orRESTful API².

Why choose OVMS over OpenVINORuntime? There are several scenarios where OVMS is the better option. OpenVINOis a C++ project with an official Python binding, but what if your softwarestack is in another language? Implementing your own interface can be challenging.OVMS simplifies this by integrating OpenVINO into your system with pre-existingcapabilities. Additionally, if your system already operates in a microserviceparadigm, OVMS is an obvious choice. You might also prefer not to integrateOpenVINO directly into the business logic of other components or deal with thecomplexities of building the system. Moreover, if some applications run on lesspowerful devices, such as mobile phones, and you want to offload heavyinferencing to more powerful machines, OVMS can handle this by exposing anetwork API. Your components can run on multiple devices, sending data requeststo OVMS and receiving model outputs in response.

OVMS is ideal for scaling yoursolution. For instance, in a multi-node Kubernetes cluster, you can createmultiple replicas and set a load balancer in front of them, achieving highavailability and throughput beyond the capability of a single node. Thisaggregation is easily managed by OVMS.

For security and privacy, OVMSallows you to host your model server on a trusted machine, ensuring that otherapplications accessing it cannot see the model itself—only the exposed interface.

Besides these, for security andprivacy purposes OVMS enables you to host your model server on a machinethat you trust, and all the other applications that access it from inside oroutside cannot see your model. you just expose its interface with otherapplications and they can’t access or see the model.

Figure2. OpenVINO Model Server

Let’s examine the OVMS structure (Figure2). At the top, we have a network interface with gRPC and RESTful endpointssupporting TF Serving API and KServe API for inference and metadata calls.Metadata provides information on expected model inputs and outputs.

At next level, we haveconfiguration monitoring, scheduler, and model management. OVMS can servemultiple models simultaneously, specified in a configuration file, withbuilt-in model management and versioning. The model files don’t need to resideon a local file system; OVMS supports remote storage systems likeGoogle Cloud, AWS S3, and Azure. For learning more about OVMS please visit here.

AIConnect for Scientific Data (AiCSD)

AI Connect for Scientific Data (AiCSD)is an open-source software sample that connects data from scientificinstruments to AI pipelines and runs workloads at the edge.

It also manages pipelines for imageprocessing and automated image comparisons. AiCSD is a containerizedmicroservices-based solution utilizing open-source EdgeX Services and connectedby a secure Redis Message Broker and various communication APIs, whichmakes it adaptable for different use cases and settings. Figure 3 shows theservices created for this reference implementation.

The architectural components of AiCSDinclude:

·        Microservices: Provided by Intel, themicroservices include a user interface and applications for managing files andjobs.

·        EdgeX Application Services: AiCSDuses the APIs from the EdgeX Applications Services to communicate and transferinformation.

·        EdgeX Services:The services include the database, message broker, and security services.

·        Pipeline Execution: AiCSDfurnishes an example pipeline for pipeline management.

·        File System: AiCSD stores and manages inputand output files.

·        Third-party Input Devices: The devicessupply the images that will be processed. Examples include an opticalmicroscope or conveyor belt camera.

The reference architecture lets imagesbe processed using assigned jobs. The job tracks the movement of the file, thestatus, and any results or outputs from the pipeline. To process a job, sometasks help match information about a job to the appropriate pipeline to run.

The process can be elaborated asbelow:

1.       The InputDevice/Imager writes the file to the OEM file system in a directorythat is watched by the File Watcher. When the File Watcher detects thefile, it sends the job (JSON struct of particular fields) to the DataOrganizer via HTTP Request.

2.       The DataOrganizer sends the job to the Job Repository to create a new job inthe Redis Database. The job information is then sent to the TaskLauncher to determine if there is a task that matches the job. If there is,the job proceeds to the File Sender (OEM).

3.       The FileSender (OEM) is responsible for sending both the job and the file to the FileReceiver (Gateway). Once the File Receiver (Gateway) has written thefile to the Gateway file system, the job is then sent on to the TaskLauncher.

4.       The TaskLauncher verifies that there is a matching task for the job before sendingit to the appropriate pipeline using the EdgeX Message Bus (via Redis).The ML pipeline subscribes to the appropriate topic and processes the file inits pipeline. The output file (if there is one) is written to the file systemand the job is sent back to the Task Launcher.

5.       The TaskLauncher then decides if there is an output file or if there are just results.In the case of only results and no output file, the Task Launcher marks the jobas complete. If there is an output file, the Task Launcher sends the jobonward to the File Sender (Gateway).

6.       The FileSender (Gateway) publishes the job information to the EdgeXMessage Bus via Redis for the File Receiver (OEM) to subscribe andpull. The File Receiver (OEM) sends an HTTP request to the File Sender(Gateway) for the output file(s). The file(s) are sent as part of theresponse and the File Receiver (OEM) writes the output file(s) to thefile system.

Figure 3: Architecture andHigh-level Dataflow

AI Pipeline for CHOCell Segmentation Use Case

Let’s explain AI pipeline for CHOcell segmentation at a high level. As plate readers³ generate cellimages in their local file system, these images need to be transferred toanother device for analysis where AI software and hardware resources areavailable. This separation of data and model locations requires a flexible,microservice-based solution. We use the AiCSD microservice infrastructure totransfer the data to the edge compute device. AiCSD leverages EdgeX FoundryMicroservices to facilitate the automatic detection, management, and transferof scientific data. This microservice flexibility is crucial for addressing theheterogeneous system integration and asymmetric data interfacing inherent inthis project.

The AI pipeline on the edgecompute device includes image preprocessing, inference of multiple DeepLearning models optimized by the OpenVINO toolkit, and image postprocessing.Figure 4 shows an example of using Deep Learning models to process cell images,where UNet is used to mask and count MSC nuclei. These processes arecontainerized using BentoML,an open-source tool. Additionally, the OpenVINO Toolkit accelerates DeepLearning model inference, providing lower latency and higher throughput whilemaintaining accuracy and optimizing hardware usage. OVMS handles model managementand version control. Once the AI pipeline processing job is completed on theedge compute device, the final results are transferred back to the local filesystem of the original scientific device using the AiCSD microserviceinfrastructure to complete the task.

Overall, the integration of IntelEdge AI solutions enables the efficient implementation of the AI pipeline forthe CHO cell segmentation use case.

Figure 4. MSC Nuclei countingusing UNet deep learning model.

Conclusion

In the article, we discussed theimplementation of an AI pipeline for cell image analysis, particularly focusingon the application of Intel Edge AI solutions in processing brightfield cellimages. We highlighted Intel's OpenVINO toolkit as a crucial component foroptimizing the inference of existing Deep Learning models within the cell AIpipeline. Additionally, we explained how the OpenVINO Model Server operates asa microservice, enabling other components within a system to interact with andutilize the models effectively. Furthermore, we explored AI Connect forScientific Data (AiCSD) and its role in the efficient implementation of thebrightfield cell image analysis pipeline.

The journey toward fully realizing thecapabilities of AI in biopharma is ongoing, and Intel's contributions arepaving the way for a future where drug development is more agile, precise, andpatient-centric. Stay tuned for further insights as we continue to explore theexciting intersection of Edge AI technology and biopharmaceutical research.

Reach out to Intel's Health and LifeSciences team at health.lifesciences@intel.com or learn more about what we doat https://www.intel.com/health.

We'd like to hear from you! Let usknow in the comments or discuss – which AI use cases in health and lifesciences do you think will have the greatest impact on global health?

If you enjoyed hearing from the Healthand Life Sciences team and want to hear more, give this post a like andensure you subscribe to get the latest updates from theteam. 

About the Author

Nooshin Nabizadeh has Ph.D.in Electrical and Computer Engineering from the University of Miami and worksat Intel Corporation as AI Solutions Architect. She enjoys photography, writingpoetry, reading about psychology and philosophy, and optimizing solutions torun as fast as possible on a given piece of hardware. Connect with her onLinkedIn https://www.linkedin.com/in/nooshin-nabizadeh/ by mentioning thisblog.

References

1.      Brightfieldmicroscopy is a widely used technique for observing the morphology of cells andtissues.

2.      https://docs.openvino.ai/archive/2023.2/ovms_what_is_openvino_model_server.html

3.      A plate reader is a laboratoryinstrument used to obtain images from samples in microtiter plates. The readershines a specific calibrated frequency of light (UV, visible, fluorescence,etc.) through the samples in the wells of the plate. Plate reader microscopydata sets have inherent variability which drives the requirement of regulartracked calibration and adjustment.

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In this blog, we will introduce the path that how OpenVINO support extensibility on CPU platform, and a sample of creating one custom operation by implement a SYCL program on CPU. oneAPI has two programming modes, one is through direct programming by SYCL which is C++ based language; another is based on acceleration libraries. In this sample we will use oneAPI DPC++ compiler to support SYCL program compiling in custom extension library, so that if users familiar with SYCL optimization can refer the OpenVINO extension mechanism to support and optimize their own operation kernel.

First of all, you should understand the interface and invoke scheduling of extension operations through OpenVINO core API. OpenVINO support to create a custom operation which is inherited from ov::op::Op and realize the member function “evaluate()” with SYCL implementation. Then, register this customer operation by “ov::OpExtension” to generate a runtime library of OpenVINO extensions. Finally, we will enable the custom extension library can be called by “add_extension()” function by Core API in runtime.

The next step is to create an IR model with this extension operation. We will introduce a method to create OV model by using OpenVINO opset and modify the layer version to extension make sure Core API can invoke operation registered in the extension library.

System requirement

Please make sure you already correctly install the OpenVINO C++ package from:

https://storage.openvinotoolkit.org/repositories/openvino/packages/

And setup environment variable for OpenVINO by:

source ./l_openvino_toolkit_ubuntu22_2024.0.0.14488.5e7e51dc778_x86_64/setupvars.sh

Then, install the DPC++ compiler, and source the environment variable:

source /opt/intel/oneapi/setvars.sh

In this blog, we create a customized “SYCL_Add” operation, the folder and files structure like below:

.
|-add
 | |-add.cpp
 | |-add.hpp
 |-CMakeLists.txt
 |-ov_extension.cpp

Step 1: Create custom operation by SYCL kernel.

For example, we create a custom operation to realize the functionality of “Add” and named it as “SYCL_Add”. We define this operation with header “add.hpp”:

#pragma once

//! [op:common_include]
#include <openvino/op/op.hpp>
#include <vector>
//! [op:common_include]

//! [op:header]
namespace TemplateExtension {

class Add : public ov::op::Op {
public:
    OPENVINO_OP("SYCL_Add");

    Add() = default;
    Add(const ov::Output<ov::Node>& A, const ov::Output<ov::Node>& B);
    void validate_and_infer_types() override;
    std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector& new_args) const override;
    bool visit_attributes(ov::AttributeVisitor& visitor) override;

    bool evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const override;
    bool has_evaluate() const override;


private:
};
//! [op:header]

}  // namespace TemplateExtension

Then, we need to override the member functions of this new operation, especially the implementation of “evaluate()”.If this blog, we will show an example of SYCL kernel. To enable SYCL programming on CPU, you are required to install the DPC++ compiler and include the header <sycl/sycl.hpp>. Below is the code implementation of “add.cpp”:

// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0

#include "add.hpp"
#include <sycl/sycl.hpp>

using namespace TemplateExtension;
using namespace sycl;

//! [op:ctor]
Add::Add(const ov::Output<ov::Node>& A, const ov::Output<ov::Node>& B): Op(ov::OutputVector{A,B}){
    constructor_validate_and_infer_types();
}
//! [op:ctor]

//! [op:validate]
void Add::validate_and_infer_types() {
    auto outShape = get_input_partial_shape(0);
    set_output_type(0, ov::element::Type_t::i32, outShape);
}
//! [op:validate]

//! [op:copy]
std::shared_ptr<ov::Node> Add::clone_with_new_inputs(const ov::OutputVector& new_args) const {
    OPENVINO_ASSERT(new_args.size() == 2, "Incorrect number of new arguments");
    return std::make_shared<Add>(new_args.at(0), new_args.at(1));
}
//! [op:copy]

//! [op:visit_attributes]
bool Add::visit_attributes(ov::AttributeVisitor& visitor) {
    return true;
}
//! [op:visit_attributes]

void add_vectors(sycl::queue& queue, sycl::buffer<float>& a, sycl::buffer<float>& b, sycl::buffer<float>& c, int& N) {
   //sycl::range n(a.size());

   queue.submit([&](sycl::handler& cgh) {
      auto in_a_accessor = a.get_access<sycl::access::mode::read>(cgh);
      auto in_b_accessor = b.get_access<sycl::access::mode::read>(cgh);
      auto out_c_accessor = c.get_access<sycl::access::mode::write>(cgh);

      cgh.parallel_for(range<1>(N), [=](sycl::id<1> i) {
               out_c_accessor[i] = in_a_accessor[i] + in_b_accessor[i];
      });
   });
}

//! [op:evaluate]
bool Add::evaluate(ov::TensorVector& outputs, const ov::TensorVector& inputs) const {
    //std::cout << ".........Add SYCL Impl execute.........." << std::endl;

    float* src_0_ptr = reinterpret_cast<float*>(inputs[0].data());
    float* src_1_ptr = reinterpret_cast<float*>(inputs[1].data());
    float* dst_ptr = reinterpret_cast<float*>(outputs[0].data());

    sycl::queue Q;

    std::vector<size_t> in_dims = inputs[0].get_shape();

    int len = static_cast<int>(in_dims[0]);
    for(int i=1;i<in_dims.size();i++){
        len = len * static_cast<int>(in_dims[i]);
    }

    sycl::buffer<float,1> src_0(src_0_ptr, sycl::range<1>(len));
    sycl::buffer<float,1> src_1(src_1_ptr, sycl::range<1>(len));
    sycl::buffer<float,1> dst(dst_ptr, sycl::range<1>(len));

    add_vectors(Q, src_0, src_1, dst, len);

    return true;
}

bool Add::has_evaluate() const {
    return true;
}
//! [op:evaluate]

As you can see, in this SYCL kernel implementation, there require creating buffer objects which can be managed on device and create accessors to control the accessing of these buffers. So, it remains buffer type conversion between C++ float pointer and SYCL float buffer. The idea of SYCL programming is like OpenCL for heterogeneous platform like GPU/NPU which remains buffer management and synchronization between host and device. This sample is just for CPU extension, there’s no use with device memory.

Step 2: Register custom operation as extension.

To register the customer operation by “ov::OpExtension”,refer below code of “ov_extension.cpp”:

// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0

#include <openvino/core/extension.hpp>
#include <openvino/core/op_extension.hpp>
#include <openvino/frontend/extension.hpp>
#include "add/add.hpp"

//! [ov_extension:entry_point]
OPENVINO_CREATE_EXTENSIONS(
std::vector<ov::Extension::Ptr>({
std::make_shared<ov::OpExtension<TemplateExtension::Add>>(),
std::make_shared<ov::frontend::OpExtension<TemplateExtension::Add>>()
})
);
//! [ov_extension:entry_point]

Then, you can create “CMakeLists.txt” file like below. Make sure use the DPC++ compiler with option “-fsycl”.

cmake_minimum_required(VERSION 3.16)
project(custom_layer)
set(CMAKE_CXX_STANDARD 17)

set(TARGET_NAME "custom")
set(CMAKE_CXX_COMPILER "icpx")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -O3 -std=c++17 -mavx512f -mavx512vl -mavx512pf -mavx512er -mavx512cd")
find_package(OpenVINO REQUIRED)
add_library(${TARGET_NAME} MODULE
        ${CMAKE_SOURCE_DIR}/ov_extension.cpp
        ${CMAKE_SOURCE_DIR}/add/add.cpp
        ${CMAKE_SOURCE_DIR}/add/add.hpp
        )
target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_EXTENSION_API)
target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime)

Use cmake to compile the runtime library for the extension operation. If you have more operations, just add source files into “add_library()”. Then we can get the runtime library called “libcustom.so”.If you meet any problem about compiler icpx, please make sure you already correctly install the DPC++ compile, and source the environment variable.

Step 3: Create IR model by OpenVINO opset

Here introduces a hack method to create ancustom operation “SYCL_Add” by exist OpenVINO opset. Due to the parameter and nodeinput/output of custom op is same as “ov::op::v1::Add”, thus we can use thismethod.

Firstly, create a python program to build OpenVINO IR model with “ov::op::v1::Add”. You can also use OpenVINO C++ API to create model, here use Python code just for quick verification.

from openvino.runtime import Core, Model, Tensor, Type
import openvino.runtime as ov
from openvino.runtime import opset11 as opset

def model():
    data1 = opset.parameter([-1,-1,-1,-1], Type.i32, name='input_1')
    data2 = opset.parameter([-1,-1,-1,-1], Type.i32, name='input_2')
    SYCL_add = opset.add(data1,data2,auto_broadcast='numpy',name="Add")
    SYCL_add.set_friendly_name("Add")
    Result = opset.result(SYCL_add, name='output_add')
return Model([Result],[data1,data2])

core = Core()
m = model()
ov.save_model(m, "SYCL_add.xml")

Now, you will get the IR model with OpenVINO “opset.Add”. We can directly modify the “.xml” like below, change the type of this layer to “SYCL_Add” and modify the version of the layer to “extension”.

Step 4: Run and profile the model execution with the SYCLextension library.

Now, you can quick check the workable and performance by OpenVINO benchmark_app sample:

$ ./benchmark_app -m ~/POC/sycl_custom/SYCL_add.xml -extensions ~/POC/sycl_custom/build/libcustom.so -data_shape input_1[64,64,64,64], input_2[64,64,64,64] -t 1 -pc

You can check the execution time of yourSYCL kernel:

[ INFO ] Performance counts for 0-th infer request
input_1              Status.NOT_RUN       layerType: Parameter            execType: unknown_i32          realTime (ms): 0.000      cpuTime (ms): 0.000
input_2              Status.NOT_RUN       layerType: Parameter            execType: unknown_i32          realTime (ms): 0.000      cpuTime (ms): 0.000
Add                  Status.EXECUTED      layerType: Reference            execType: ref_i32              realTime (ms): 21.977     cpuTime (ms): 21.977
output_add           Status.EXECUTED      layerType: Result               execType: unknown_i32          realTime (ms): 0.001      cpuTime (ms): 0.001
Total time:     21.978 milliseconds
Total CPU time: 21.978 milliseconds

Please note, the “execType” is using the ref_xxx means your custom reference implementation kernel with the data type.

Summary

This blog just shows the capable way to enable SYCL kernel as the extension of CPU plugin, we will not focusing on guiding the user implement the SYCL kernel like above programming. There are a lot of technic skills of kernel optimization, if you already have an efficient SYCL kernel and want to enable as the CPU extension to workaround some customized operations. We hope this blog will be helpful to you.

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Authors: Wenyi Zou, Xiake Sun

Introduction

GroundingDINO introduces a language-guided query selection module to enhance object detection using input text. This module selects relevant features from image and text inputs and uses them as decoder queries. In this blog, we provide the OpenVINO™ optimization for GroundingDINO on Intel® platforms.

The public GroundingDINO project is referenced from: GroundingDINO

The GroundingDINO refer the model structure in below picture:

OpenVINO™ backend on GroundingDINO

In this project, you do not require to download OpenVINO™ and build the library with GroundingDINO project manually. It’s already fully integrated with OpenVINO™ runtime library for downloading, program compiling and linking.

At present, this repository already optimized and validated by OpenVINO™ 2023.1.0.dev20230811 version. Check the operating system which can support OpenVINO™ runtime library directly:

• Ubuntu 22.04 long-term support     (LTS), 64-bit (Kernel 5.15+)
• Ubuntu 20.04 long-term support     (LTS), 64-bit (Kernel 5.15+)
• Ubuntu 18.04 long-term support     (LTS) with limitations, 64-bit (Kernel 5.4+)
• Windows* 10 
• Windows* 11 
• macOS* 10.15 and above,     64-bit 
• Red Hat Enterprise Linux* 8,     64-bit

Step 1: Install system dependency and setup environment

Create and enable python virtual environment

conda create -n ov_py310 python=3.10 -y
conda activate ov_py310

Clone the GroundingDINO repository from GitHub

git clone https://github.com/wenyi5608/GroundingDINO.git -b wenyi5608-openvino

Change the current directory to the GroundingDINO folder

cd GroundingDINO/

Install python dependency

pip install -r requirements.txt
pip install openvino==2023.1.0.dev20230811 openvino-dev==2023.1.0.dev20230811 onnx onnxruntime

Install the required dependencies in the current directory

pip install -e .

Download pre-trained model weights

mkdir weights
cd weights/
wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth
cd ..

Step 2: Export to OpenVINO™ models

python demo/export_openvino.py -c groundingdino/config/GroundingDINO_SwinT_OGC.py -p weights/groundingdino_swint_ogc.pth -o weights/

Step 3: Simple inference test with PyTorch and OpenVINO™

Inference with PyTorch

python demo/inference_on_a_image.py \
-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
-p weights/groundingdino_swint_ogc.pth \
-i .asset/demo7.jpg \
-t "Horse. Clouds. Grasses. Sky. Hill." \
-o logs/1111 \
 --cpu-only
 

Inference with OpenVINO™

python demo/ov_inference_on_a_image.py \
-c groundingdino/config/GroundingDINO_SwinT_OGC.py \
-p weights/groundingdino.xml \
-i .asset/demo7.jpg  \
-t " Horse. Clouds. Grasses. Sky. Hill."  \
-o logs/2222 -d CPU

Authors Wenyi Zou, Su Yang

Introduction

Tokenization is the process of breaking down text into smaller units, such as words or subwords, known as tokens. These tokens are the building blocks for various NLP tasks, including text classification, sentiment analysis, machine translation, and more. Tokenization makes it easier for machines to understand and work with textual data by converting unstructured text into a structured format.

This article will demonstrate a C++ application of the tokenize and detokenize model with Intel’s OpenVINO™ C++ API on Linux/Windows systems.

Model Conversion

The OpenVINO™ Tokenizers project has a tool to convert a HuggingFace tokenizer into OpenVINO™ IR model tokenizer and detokenizer: it provides the convert_tokenizer function that accepts a tokenizer.

Install system dependency and setup environment

Step1: Download code

git clone https://github.com/openvinotoolkit/openvino.genai.git
cd openvino.genai
git submodule update --init

Step2: Install OpenVINO™ Install OpenVINO™ Archives >=2023.3. <INSTALL_DIR> below refers to the extraction location.

For Linux

source <INSTALL_DIR>/setupvars.sh

For Windows

<INSTALL_DIR>\setupvars.bat

Step3: Create python environment

conda create -n ov_genai python=3.10
cd text_generation/causal_lm/cpp
python -m pip install --upgrade-strategy eager "optimum>=1.14" -r ../../../llm_bench/python/requirements.txt ../../../thirdparty/openvino_contrib/modules/custom_operations/[transformers] --extra-index-url https://download.pytorch.org/whl/cpu

step4: Convert tokenizer to OpenVINO™ IR.

Take the tokenize and detokenize of the chatglm3-6b as an example.

convert_tokenizer ./chatglm3-6b/ --output ./chatglm3-6b/ov/ --with-detokenizer --trust-remote-code

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Build custom OpenVINO operation extension library

cd thirdparty/openvino_contrib/modules/custom_operations
mkdir build && cd build
cmake ../ -DCMAKE_BUILD_TYPE=Release
cmake --build . --parallel 4

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Load and use custom OpenVINO operation extension library

#include <openvino/openvino.hpp>
namespace {
std::pair<ov::Tensor, ov::Tensor> tokenize(ov::InferRequest& tokenizer, std::string&& prompt) {
    constexpr size_t BATCH_SIZE = 1;
    tokenizer.set_input_tensor(ov::Tensor{ov::element::string, {BATCH_SIZE}, &prompt});
    tokenizer.infer();
    return {tokenizer.get_tensor("input_ids"), tokenizer.get_tensor("attention_mask")};
}

std::string detokenize(ov::InferRequest& detokenizer, std::vector<int64_t>& tokens) {
    constexpr size_t BATCH_SIZE = 1;
    detokenizer.set_input_tensor(ov::Tensor{ov::element::i64, {BATCH_SIZE, tokens.size()}, tokens.data()});
    detokenizer.infer();
    return detokenizer.get_output_tensor().data<std::string>()[0];
}
;

int main(int argc, char* argv[]) try {
    if (argc != 3) {
        throw std::runtime_error(std::string{"Usage: "} + argv[0] + " <MODEL_DIR> '<PROMPT>'");
}

    ov::Core core;

#ifdef _WIN32
    core.add_extension(“path/to/user_ov_extensions.dll”);
#else
    core.add_extension(“path/to/libuser_ov_extensions.so”);
#endif

    std::cout << "promt " << argv[2] << std::endl;

    // tokenizer and detokenizer work on CPU only
    ov::InferRequest tokenizer = core.compile_model(
        std::string{argv[1]} + "/openvino_tokenizer.xml", "CPU").create_infer_request();
    auto [input_ids, attention_mask] = tokenize(tokenizer, argv[2]);
    ov::InferRequest detokenizer = core.compile_model(
        std::string{argv[1]} + "/openvino_detokenizer.xml", "CPU").create_infer_request();
    
    std::cout << "input lenghth " << input_ids.get_size() << std::endl;

    std::vector<int64_t> tokens;
    for (size_t idx = 0; idx < input_ids.get_size(); ++idx) {
        tokens.push_back(input_ids.data<int64_t>()[idx]);
    }
    
    std::cout << detokenize(detokenizer, tokens) << std::endl;
    
} catch (const std::exception& error) {
    std::cerr << error.what() << '\n';
    return EXIT_FAILURE;
} catch (...) {
    std::cerr << "Non-exception object thrown\n";
    return EXIT_FAILURE;
}

‍

‍

Authors: Kunda,Xu Song, Bell

This chapter introduces the extension mechanism of OpenVINO on GPU. And combine thecode snippets to explain how a custom operation should be expanded on the GPU.

First, we need to understand the OpenVINO Extensibility Mechanism, Custom operations, which are not included in the list, are not recognized by OpenVINO out-of-the-box. The need for custom operation may appearin two cases:

-         A new or rarely used regular framework operation is notsupported in OpenVINO yet.

-         A new user operation that was created for some specific modeltopology by the author of the model using framework extension capabilities.

Importing models with such operations requires additional steps. This guide illustrates the workflow for running inference on models featuring custom operations. 

Introduction

How to implement custom operations by OpenVINO Extensibility API 

OpenVINO Extensibility API enables adding support for those custom operations and using one implementation for Model Optimizer and OpenVINO Runtime.

Defining a new custom operation basically consists of two parts:

-         Definition of operation semantics in OpenVINO, the code that describes how this operation should be inferred consuming input tensor(s) andproducing output tensor(s). The implementation of execution kernels for GPU is described in separate guides.

-         Mapping rule that facilitates conversion of framework operation representation to OpenVINO defined operation semantics.

The first part is required for inference. Thesecond part is required for successful import of a model containing suchoperations from the original framework model format.

How to implement GPU custom operations

To enable operations not supported by OpenVINO™ out of the box, you may need an extension for OpenVINO operation set, and a custom kernel for the device you will target. This article describes custom kernel supportfor the GPU device.

The GPU code path abstracts many details about OpenCL. Youneed to provide the kernel code in OpenCL C and an XML configuration file that connects the kernel and its parameters to the parameters of the operation.

The process of GPU operation extension is the same as theoverall process of CPU operation, but because OpenVINO GPU operations are defined using OpenCL, additional files are required to provide interface and function definitions for custom ops.

In the above figure, custom_op.cl and custom_op.xml areadditional files required by the GPU extension than the CPU extension.

-         custom_op.cl is the function definition of custom operation using OpenCL

-         custom_op.xml is an extended function of OpenVINO GPU and provides the interface definition of custom operation function.

And need call the core.set_property() method from yourapplication with the "CONFIG_FILE" key and the configuration filename as a value before loading the network that uses custom operations to the plugin.

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Quick Start

Similarto the OpenVINO CPU Extension process, the GPU extension process is as follows, there are several options to implement each part. The following sections will describe them in detail.
It is recommended to familiarize yourself with the implementation of the OpenVINO extension on the CPU first, so that you can better understand it when using GPU custom operations.

Definition of Operation Semantics

There aretwo ways to define custom operations,

-         OpenVINO operation set combination. If thecustom operation can be mathematically represented as a combination of exiting OpenVINO operations and such decomposition gives desired performance.

-         Implementing custom operation by C++/OpenCL. General advice, try to decompose the operation by OpenVINO operation set first, If such decomposition is not possible or appears too bulkywith a large number of constituent operations that do not perform well, then a new class for the custom operation should be implemented

-         Custom Operation Guide Reference Link: https://docs.openvino.ai/2023.3/openvino_docs_Extensibility_UG_add_openvino_ops.html

Here wetake a simple OpenCL custom op as an example to illustrate.

Mapping from Framework Operation

Mapping of custom operation is implemented differently,depending on model format used for import. If a model is represented in the ONNX (including models exported from PyTorch in ONNX), TensorFlow Lite,PaddlePaddle or TensorFlow formats.

Frontend Extension API Reference Link:  https://docs.openvino.ai/2024/documentation/openvino-extensibility/frontend-extensions.html

Registering Extensions

A custom operation class and a new mapping frontend extensionclass object should be registered to be usable in OpenVINO runtime.

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Create a Library with Extensions

To create an extension library, for example, to load the extensions into OpenVINO Inference engine, perform the following:

CMake file define.

Build the extension library, running the commands below

OpenVINOGPU extension code snippets

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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

Summary

This quarter we observe paving ultra low-bit Large Language Models (LLMs) weight quantization including ternary quantization where weight can take only three possible values {-1, 0, 1}. Many papers are also focusing on vector quantization of weights when a sequence of weights (2, 4, or 8 elements) is replaced by the index in the lookup table (e.g. based on E8 lattice).

A lot of effort is dedicated to KV-cache optimization of LLMs. Methods such as low-bit quantization and token eviction are getting mainstream in the SW solution designed for LLM inference. And here is the announced Nvidia Blackwell can get a competitive advantage as it supportsFP4 type on the HW level that fits well KV-cache on-the-fly compression/decompression.

Highlights

• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits by Microsoft Research and University of Chinese Academy of Sciences (https://arxiv.org/pdf/2402.17764.pdf).The paper introduces 1-bit LLM variant called BitNet b1.58, where every parameter is ternary, taking on values of {-1, 0, 1}. BitNet b1.58 retains all the benefits of the original 1-bit BitNet, including new computation paradigm, which requires almost no multiplication operations for matrix multiplication and can be highly optimized. BitNet b1.58 offers two additional advantages. Firstly, its modeling capability is stronger due to its explicit support for feature filtering, made possible by the inclusion of 0 in the model weights, which can significantly improve the performance of 1-bit LLMs. Secondly, experiments show that BitNet b1.58 can match full precision (i.e., FP16) baselines in terms of both perplexity and end-task performance, starting from a 3B size, when using the same configuration.

• QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks by Cornell University (https://arxiv.org/pdf/2402.04396.pdf).The paper introduces QuIP#, a weight-only post-training compression method for2, 3, and 4-bit LLM quantization. It uses the Randomized Hadamard Transform(RHT) as a form of outlier suppression and introduces the E8 lattice-based E8Pcodebook to better quantize RHT transformed weights. The E8P codebook is highly symmetric and admits fast inference, allowing a “proof of concept” QuIP# CUDA implementation to achieve over 50% peak memory bandwidth on GPUs. QuIP# also implements inter-layer fine-tuning, further improving quantization. The method achieves superior scaling at 3 bits over 4 bits and similar scaling at 2 bits to higher bitrates. The code is available at https://github.com/Cornell-RelaxML/quip-sharp.

• FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design by Microsoft (https://arxiv.org/pdf/2401.14112v1.pdf).Authors highlight the key challenges in supporting FP6 quantization on modern GPUs. They propose TC-FPx, the first full-stack GPU kernel design scheme with unified Tensor Core support of float-point weights with various bit-width, in particular, FP6. They also provide new end-to-end inference support for quantized LLMs through the integration of TC-FPx, achieving better trade-offs between inference cost and model quality, and evaluate FP6-LLM on various LLM models demonstrating that it substantially outperforms the baseline.

• Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference by Nvidia, University of Wrocław, University of Edinburgh. (https://arxiv.org/pdf/2403.09636.pdf). DMC proposes to bake in online KV cache compression capability to pretrained LLM through an uptraining approach. The core idea to is determine if an incoming KV pair should be accumulated into an existing KV pair aggregated over successive timesteps or appended as a new KV segment of aggregation. To facilitate this, the authors propose a predictor module that is integrated into each attention head, utilizing solely the first feature of the current Q and K projections to produce the binary decision and the accumulation factor. With no overhead learning parameters, continued pretraining as low as 2-4% of original learning attains significant dynamic cache compression. DMC, applied across Llama2 7-70B size, preserves the original downstream task quality with up to 4× cache compression, translating throughput improvement up to 3.4× on A100 and 3.7× on H100. The reader is encouraged to consult the full paper for training solutions for the non-differentiability and dynamic KV management during training.

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Papers with notable results

Quantization

• Extreme Compression of Large Language Models via Additive Quantization by HSE University, Yandex Research, Skoltech, IST Austria, Neural Magic (https://arxiv.org/pdf/2401.06118.pdf).The paper proposes a practical adaptation of Additive Quantization to the task of post-training quantization of LLMs. Authors adapt the optimization problem to be instance-aware, taking layer calibration input and output activations into account. They also complement the layer-wise optimization with an efficient intra-layer tuning technique, which optimizes quantization parameters jointly over several layers, using only the calibration data. The method shows promising results in a 2, 3,4-bit weight compression settings achieving significantly better accuracy than GPTQ. The code is available at https://github.com/vahe1994/AQLM.
• Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models by Chinese Academy of Sciences (https://arxiv.org/pdf/2401.04585.pdf).Authors identify two levels of mismatch in diffusion models, including the calibration sample level and the reconstruction output level, which result in the low performance of PTQ. Based on this, they propose an alignment method to address the calibration sample level mismatch as well as a technique to eliminate the reconstruction output level mismatch. Results show that, with theW4A8 precision setting, the quantized models can compare to the full-precision models, and the method is robust to the inference space, resolution, and guidance conditions of diffusion models.
• KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization by UC Berkeley (https://arxiv.org/pdf/2401.18079.pdf). The work facilitates low-precision KV cache quantization by incorporating several novel methods: (i) Per-Channel Key Quantization, where the dimension is adjusted along which the Key activations are quantized to better match the distribution; (ii) Pre RoPE Key Quantization, where Key activations are quantized before the rotary positional embedding to mitigate its impact on quantization; (iii) Non-Uniform KV Cache Quantization, where per-layer sensitivity-weighted non-uniform datatypes are derived that better represent the distributions; (iv)Per-Vector Dense-and-Sparse Quantization, where outliers are isolated separately for each vector to minimize skews in quantization ranges; and (v) Q-Norm, where quantization centroids are normalized to mitigate distribution shift, providing additional benefits for 2-bit quantization. The method helps to achieve < 0.1 perplexity degradation with 3-bitquantization on both Wikitext-2 and C4 and enables serving LLaMA-7B with a context length of up to 1 million on a singleA100-80GB GPU and up to 10 million on an 8-GPU system.
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs by The University of Hong Kong, Beihang University, ETH Zurich (https://arxiv.org/pdf/2402.04291.pdf).The paper presents 1-bitpost-training quantization scheme tailored for pretrained LLMs. Based on the weight distribution of LLMs, the method first identifies and structurally selects salient weights, and minimizes the compression loss through an effective binary residual approximation strategy. Moreover, considering the bell-shaped distribution of the non-salient weights, authors propose a splitting search to group and binarize them accurately. The method achieves8.41 perplexity on LLaMA2-70B (with 3.32baseline) with 1.08-bit weights across.
• DB-LLM: Accurate Dual-Binarization for Efficient LLMs by The Beihang University, The University of Sydney, Harbin Institute of Technology, Shenzhen and Jiangsu University (https://arxiv.org/pdf/2402.11960.pdf). The paper analyzes properties of LLM weight distribution and proposes a method consisted of two parts: 1) Flexible Dual Binarization of weights which effectively uses two binary matrices instead of one in conventional binarization method, allowing to use 4 quantization levels instead of 2 and preserve zero values after the quantization. 2) Deviation-Aware  Distillation which prioritizes uncertain samples by utilizing a pair of teacher-student entropy as a difficulty indicator using a data-free distillation approach. The method achieves 4.64 perplexity onLLaMA2-70B (with 3.32 baseline) outperforming BiLLM method (with 8.41perplexity) in terms of metrics.
• Accurate LoRA-Finetuning Quantization of LLMs via Information Retention by Beihang University, ETH Zurich, Byte dance AI Lab (https://arxiv.org/pdf/2402.05445.pdf).The paper proposes two methods to improve accuracy of LLMs during weight quantization: (1) statistics-based Information Calibration Quantization allows the quantized parameters of LLM to retain original information accurately; (2)finetuning-based Information Elastic Connection makes LoRA utilizes elastic representation transformation with diverse information. The method helps improve accuracy under 2-4 bit-widths, e.g., 4- bit LLaMA-7B achieves 1.4% improvement on MMLU compared with the state-of-the-art methods. The significant performance gain requires only a tiny 0.31% additional time consumption, revealing the satisfactory efficiency of our IR QLoRA The code is available at https://github.com/htqin/ir-qlora.
• GPTVQ: The Blessing of Dimensionality for LLM Quantization by Qualcomm AI Research (https://arxiv.org/html/2402.15319v1).The paper introduces a post-training method for vector quantization of LLM weights. The method interleaves quantization of one or more columns with updates to the remaining unquantized weights, using information from the Hessian of the per-layer output reconstruction MSE. Quantization codebooks are initialized using a data-aware version of the EM algorithm. The codebooks are then updated, and further compressed by using integer quantization and SVD-based compression. The method achieves good results on very large models such as aLlamav2-70B and improved latency compared to using a 4-bit integer format with on-device timings for decompression on a mobile CPU. The source code will be available at https://github.com/qualcomm-ai-research/gptvq.
• L4Q: Parameter Efficient Quantization-Aware Training on Large Language Models via LoRA-wise LSQ by Seoul National University, Sungkyunkwan University (https://arxiv.org/pdf/2402.04902.pdf).Authors propose quantization-aware training scheme applicable to both full-precision or pre-quantized LLMs that acts as a shortcut of LoRA fine-tuning on pre-quantized models. The method demonstrates good performance within a limited number of training steps. Experiments highlight a capability to fine-tune a 33B model using a single A100 GPU with 80G memory. Additionally, authors show that the joint training of quantization parameters not only significantly reduces memory requirements but also preserves model quality.
• EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs by Tencent (https://arxiv.org/pdf/2403.02775.pdf).Authors show that the outliers in weights are more critical to the model’s performance compared to the normal elements. Beyond this, they propose to use a gradient-based method for optimizing the quantization range. These two strategies can also be used in other scenarios, such as weight-activation quantization and quantization-aware training (QAT). The method is fast and does not have generalization problems as it is data-free.
• OneBit: Towards Extremely Low-bit Large Language Models by Tsinghua University and Harbin Institute of Technology (https://arxiv.org/pdf/2403.02775.pdf). This paper introduces a method that quantizes the weight matrices of LLMs to 1-bit. Authors introduce a 1-bit quantization-aware training (QAT) framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the QAT framework. Experimental results indicate that OneBit achieves good performance (at least 83% of then on-quantized performance) with robust training processes.
• GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM by Georgia Tech, Intel, and University of Maryland (https://arxiv.org/pdf/2403.05527.pdf).The paper proposes a method to tackle the problem of KV-cache compression of text-generation transformer models. The method first applies quantization to the majority of entries of similar magnitudes to ultra-low precision. It then employs a low-rank matrix to approximate the quantization error, and a sparse matrix to remedy individual errors from outlier entries. Experiments demonstrate the proposed approach achieves near-lossless 4-bit KV cache compression with up to 2.38× throughput improvement while reducing peak-memory size up to 2.29×.The code is available at https://github.com/HaoKang-Timmy/GEAR.
• 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.

• AFFINEQUANT: AFFINETRANSFORMATION QUANTIZATION FOR LARGE LANGUAGE MODELS by Xiamen University, Byte Dance and Peng Cheng Laboratory (https://arxiv.org/pdf/2403.05527.pdf).Authors propose an affine transform in PTQ, which minimizes quantization error, especially under low-bit quantization of LLMs. They propose an optimization algorithm that guarantees invertibility throughout the process, utilizing the Levy-Desplanques theorem, and simultaneously reduces computational costs. The method obtains good performance for large language model quantization, especially on low-bit or small models. Without additional overhead, on the w4a4 configuration of LLaMA2-7B, our perplexity on the C4dataset is 15.76 (2.26↓ vs 18.02 in OmniQuant). The code is available at: https://github.com/bytedance/AffineQuant.
• QAQ: Quality Adaptive Quantization for LLM KV Cache by Nanjing University (https://arxiv.org/pdf/2403.04643.pdf).The paper focuses on the KV-cache compression problem of LLMs Authors and claims that existing methods primarily rely on various hypotheses, such as sorting the KV cache based on attention scores for replacement or eviction, to compress the KV cache and improve model throughput. However, heuristics used by these strategies may wrongly evict essential KV cache, which can significantly degrade model performance. Authors propose a scheme for the KV cache and theoretically demonstrate that key cache and value cache exhibit distinct sensitivities to quantization, leading to the formulation of separate quantization strategies for their non-uniform quantization. Through the integration of dedicated outlier handling, as well as an improved attention-aware approach, the method achieves up to 10× the compression ratio of the KV cache size with a moderate impact on model performance.
• Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference by CMU, UT Austin and Meta (https://arxiv.org/pdf/2402.09398.pdf). Authors propose a simple integration of a constant sized cache with eviction-based cache methods, such that all tokens can be queried at later decoding steps. Low-rank caches in the method occupy constant memory with respect to the sequence length, and in the experiments, the extra storage to accommodate is nearly free, taking up the equivalent space of only 4 extra KV pairs in our experiments. Inspired by recurrent networks, the low-rank state stores new information by recursive updates rather than concatenation. As each sample has its own cache, the method provides the same proportional cache reduction for small and large batch sizes. Its ability to retain information throughout time shows merit on a variety of tasks where we demonstrate it can help reduce the performance gap from caching everything, some times even matching it, all while being efficient. Code can be found at https://github.com/hdong920/LESS.
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache by Rice University et. al. (https://arxiv.org/pdf/2402.02750.pdf)This paper analyzes methodically into extreme 2-bit quantization of KV cache and uncovers that the optimal scheme is per channel on key cache and value cache by token. Due to the streaming nature of caching and per-channel quantization coupling key cache across tokens, the authors propose grouping within the key and value caches. Quantization is performed only upon the completion of each group and maintained in FP16 otherwise. Extensive set of task benchmarks have demonstrated KIVI’s great retention of FP16 quality across Llama, Falcon, Mistral families. With tailored GPU implementation, KIVI can serve Llama2-7B with up to 4× larger batch size and achieves 2.35× ∼3.47× increased throughput on A100 (80GB). Code is available at https://github.com/jy-yuan/KIVI.

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Pruning

• RockEngine: Sparse and Efficient Fine-tuning in a Pocket by MIT, UCAD and NVidia (https://arxiv.org/pdf/2310.17752.pdf).Authors introduce PockEngine: a sparse and efficient engine to enable fine-tuning on various edge devices. PockEngine supports sparse backpropagation: it prunes the backward graph and sparsely updates the model with measured memory saving and latency reduction while maintaining the model quality. PockEngine is compilation first: the entire training graph is derived at compile-time, which reduces the runtime overhead and brings opportunities for graph transformations. It also integrates a rich set of training graph optimizations, thus can further accelerate the training cost, including operator reordering and backend switching. PockEngine supports diverse applications, frontends and hardware backends: it flexibly compiles and tunes models defined in PyTorch/TensorFlow/Jax and deploys binaries to mobile CPU/GPU/DSPs. It achieves up to 15x speedup over off-the-shelf TensorFlow(Raspberry Pi), 5.6x memory saving back-propagation (Jetson AGX Orin).Remarkably, PockEngine enables fine-tuning LLaMav2-7B on NVIDIA Jetson AGX Orinat 550 tokens/s, 7.9x faster than the PyTorch.
• SLICEGPT: COMPRESS LARGELANGUAGE MODELS BY DELETING ROWS AND COLUMNS by ETH Zurich and Microsoft (https://arxiv.org/pdf/2401.15024.pdf). The paper introduces a post-training sparsification scheme that makes transformer networks (including LLMs) smaller by first applying orthogonal transformations to each transformer layer that leave the model unchanged, and then slicing off the least significant rows and columns (chosen by the eigenvalue decay) of the weight matrices. The model structure is left unchanged, but each weight matrix is replaced by a smaller (dense) weight matrix, reducing the embedding dimension of the model. This results in speedups(without any additional code optimization) and a reduced memory footprint. The method can remove up to 25% of the model for LLAMA-270B, OPT 66B, and Phi-2 models while maintaining 99%, 99%, and 90% zero-shot task performance respectively. Code is available at: https://github.com/microsoft/TransformerCompression.
• Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models by CUHK MMlab, Shanghai Jiao Tong University, Shanghai Artificial Intelligence Laboratory (https://arxiv.org/pdf/2402.14800.pdf). One of the first attempts to prune MoE models. The method works post-training and statistically estimates contribution each expert. Then, it gradually removes the less used experts from the model. The code will be available at https://github.com/Lucky-Lance/Expert_Sparsity.  
• ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models by Tsinghua University, ICT Academy China  and Tencent (http://arxiv.org/abs/2402.13516). The work extends the training-based activation sparsification, Relufication with progressive regularization and concluding with ReLU threshold adjustment. In particular, it employs L1 regularization after the initial Relufication, incrementally by annealing regularization factor following a sine function. The final technique is a calibration of positive threshold for ReLU to enhance sparsity. Under a given budget of training tokens, the efficacy of increasing sparsity while attaining better task quality is demonstrated on Llama2-7B &13B and benchmarked against recipes including vanilla, shifted Relufication, and fixed L1 factor. On acceleration, a set of CUDA Sparse kernel is developed. Up to 70% of additional speedup can be achieved on top of the Relufied models. Kernel codes can be found here: https://github.com/Raincleared-Song/sparse_gpu_operator.
• Keyformer: KV Cache reduction through key tokens selection for Efficient Generative Inference by University of British Columbia. (https://arxiv.org/pdf/2403.09054.pdf). This work investigates the task inaccuracies caused by token pruning techniques in KV cache compression. It was observed that the attention scores in subsequent decoding steps is highly distorted, causing spurious token importance assessment and elimination. To counter this, Keyformer proposes a regularization approach on attention score by substituting the softmax inattention with Gumbel softmax. By calibrating hyperparameters (noise and temperature) of Gumbel softmax empirically, Keyformer outperforms Heavy-Hitter Oracle in Pareto efficiency on generation tasks, such as conversation and summarization across GPT-J, Celebras-GPT, and MPT which utilize varied position embeddings.
• Shears: Unstructured Sparsity with Neural Low-rank Adapter Search by Intel Labs (https://arxiv.org/abs/2404.10934).  Recently, several approaches successfully demonstrated that weight-sharing Neural Architecture Search (NAS) can effectively explore a search space of elastic low-rank adapters (LoRA), allowing the parameter-efficient fine-tuning (PEFT) and compression of large language models. In this paper, researchers introduce a novel approach called Shears, demonstrating how the integration of cost-effective sparsity and a proposed Neural Low-rank adapter Search (NLS) algorithm can further improve the efficiency of PEFT approaches. Results demonstrate the benefits of Shears compared to other methods, reaching high sparsity levels while improving or with little drop in accuracy, utilizing a single GPU for a pair of hours.

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Other

• MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases by Meta (https://arxiv.org/pdf/2402.14905.pdf). Authors propose deep and thin architectures, coupled with embedding sharing and grouped-query attention mechanisms, and establish a strong baseline network denoted as MobileLLM, which attains a 2.7%/4.3%accuracy boost over the preceding 125M/350M state-of-the-art models. Additionally, they proposed an immediate block-wise weight-sharing approach with no increase in model size and only marginal latency overhead. The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0.7%/0.8% than MobileLLM 125M/350M.
• MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training by Apple (https://arxiv.org/pdf/2311.17049.pdf).Authors design a new family of mobile-friendly CLIP models, MobileCLIP. They use hybrid CNN-transformer architectures with structural reparametrization in image and text encoders to reduce the size and latency. Authors introduce multi-modal reinforced training, a novel training strategy that incorporates knowledge transfer from a pre-trained image captioning model and an ensemble of strong CLIP models to improve learning efficiency. They also introduce two variants of our reinforced datasets: DataCompDR-12M and DataCompDR-1B. Using DataCompDR, they demonstrate 10x-1000x learning efficiency in comparison to DataComp. MobileCLIP family obtains state-of-the-art latency-accuracy tradeoff on zero-shot tasks ,including marking a new best ViT-B/16 based CLIP model.
• Speculative Streaming: FastLLM Inference without Auxiliary Models by Apple (https://arxiv.org/pdf/2402.11131.pdf). The paper proposes a single-model speculative decoding method that fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. Author also introduced a parallel tree draft pruning layer, which prunes some of the tokens from the input tree draft based on transition probability between parent and immediate child tokens. The method achieves on-par/higher speed-ups than Medusa-style architectures while using∼10000X fewer extra parameters.
• LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression by Microsoft and Tsinghua University (https://arxiv.org/pdf/2403.12968.pdf).Code is available at https://aka.ms/LLMLingua-2. Authors propose a data distillation procedure to derive knowledge from an LLM to compress prompts without losing crucial information and introduce an extractive text compression dataset. They formulate prompt compression as a token classification problem and use a Transformer encoder as the base architecture to capture all essential information for prompt compression from the full bidirectional context. The approach leads to lower latency by explicitly learning the compression objective with smaller models such as XLM-RoBERTa large and mBERT. They evaluate their method on both in-domain and out-of-domain datasets, including MeetingBank, LongBench, ZeroScrolls, GSM8K,and BBH. Despite its small size, our model shows significant performance gains over strong baselines and demonstrates robust generalization ability across different LLMs. Additionally, the model is 3x-6x faster than existing prompt compression methods, while accelerating the end-to-end latency by 1.6x-2.9xwith compression ratios of 2x-5x.

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Introduction

Latent Consistency Models (LCMs) is the next generation of generative models after Latent Diffusion Models (LDMs). While Latent Diffusion Models (LDMs) like Stable Diffusion are capable of achieving the outstanding quality of generation, they often suffer from the slowness of the iterative image denoising process. LCM is an optimized version of LDM. Inspired by Consistency Models (CM), Latent Consistency Models (LCMs) enabled swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion. The Consistency Models is a new family of generative models that enables one-step or few-step generation. More details about the proposed approach and models can be found using the following resources: project page, paper, original repository.

This article will demonstrate a C++ application of the LCM model with Intel’s OpenVINO™ C++ API on Linux systems. For model inference performance and accuracy, the C++ pipeline is well aligned with the Python implementation.

The full implementation of the LCM C++ demo described in this post is available on the GitHub: openvino.genai/lcm_dreamshaper_v7.

Model Conversion

To leverage efficient inference with OpenVINO™ runtime on Intel platforms, the original model should be converted to OpenVINO™ Intermediate Representation (IR).

LCM model

Optimum Intel can be used to load SimianLuo/LCM_Dreamshaper_v7 model from Hugging Face Hub and convert PyTorch checkpoint to the OpenVINO™ IR on-the-fly, by setting export=True when loading the model, like:

from optimum.intel.openvino import OVLatentConsistencyModelPipeline

model = OVLatentConsistencyModelPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", export=True)
model.save_pretrained("ov_lcm_model")

Tokenizer

OpenVINO Tokenizers is an extension that adds text processing operations to OpenVINO Inference Engine. In addition, the OpenVINO Tokenizers project has a tool to convert a HuggingFace tokenizer into OpenVINO IR model tokenizer and detokenizer: it provides the convert_tokenizer function that accepts a tokenizer Python object and returns an OpenVINO Model object:

from transformers import AutoTokenizer
from openvino_tokenizers import convert_tokenizer
from openvino import compile_model, save_model

hf_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
ov_tokenizer_encoder = convert_tokenizer(hf_tokenizer)
save_model(ov_tokenizer_encoder, "ov_tokenizer.xml")

Note: Currently OpenVINO Tokenizers can be inferred on CPU devices only.

Conversion step

You can find the full script for model conversion at the original repo.

Note: The tutorial assumes that the current working directory is and <openvino.genai repo>/image_generation/lcm_ dreamshaper_v7/cpp all paths are relative to this folder.

Let’s prepare a Python environment and install dependencies:

conda create -n openvino_lcm_cpp python==3.10
conda activate openvino_lcm_cpp
conda install -c conda-forge 'openvino>=2023.3.0'
python -m pip install -r scripts/requirements.txt
python -m pip install ../../../thirdparty/openvino_contrib/modules/custom_operations/[transformers]

Now we can use the script scripts/convert_model.py to download and convert models:

cd scripts
python convert_model.py -lcm "SimianLuo/LCM_Dreamshaper_v7" -t FP16

C++ Pipeline

Pipeline flow

Let’s now talk about the logical structure of the LCM model pipeline.

Just like the classic Stable Diffusion pipeline, the LCM pipeline consists of three important parts:
- A text encoder to create a condition to generate an image from a text prompt.
- U-Net for step-by-step denoising the latent image representation.
- Autoencoder (VAE) for decoding the latent space to an image.

The pipeline takes a latent image representation and a text prompt transformed to text embedding via CLIP’s text encoder as an input. The initial latent image representation is generated using random noise generator. LCM uses a guidance scale for getting time step conditional embeddings as input for the diffusion process, while in Stable Diffusion, it used for scaling output latents.

Next, the U-Net iteratively denoises the random latent image representations while being conditioned on the text embeddings. The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm. LCM introduces its own scheduling algorithm that extends the denoising procedure introduced by denoising diffusion probabilistic models (DDPMs) with non-Markovian guidance. The denoising process is repeated for a given number of times to step-by-step retrieve better latent image representations. When complete, the latent image representation is decoded by the decoder part of the variational auto encoder.

The C++ implementations of the scheduler algorithm and LCM pipeline are available at the following links: LCM Scheduler, LCM Pipeline.

LoRA support

LoRA (Low-Rank Adaptation) is a training technique for fine-tuning Stable Diffusion models. There are various LoRA models available on https://civitai.com/tag/lora.

The main idea for LoRA weights enabling, is to append weights onto the OpenVINO LCM models at runtime before compiling the Unet/text_encoder model. The method is to extract LoRA weights from safetensors file, find the corresponding weights in Unet/text_encoder model and insert the LoRA bias weights. The common approach to add LoRA weights looks like:

The original LoRA safetensor model is loaded via safetensors.h. The layer name and weight of LoRA are modified with Eigen Lib and inserted into Unet/text_encoder OpenVINO model using ov::pass::MatcherPass - you can see the implementation in the file common/diffusers/src/lora.cpp.

To run the LCM demo with the LoRA model, first download LoRA, for example: LoRa/Soulcard.

Build and Run LCM demo

Let’s start with the dependencies installation:‍

conda activate openvino_lcm_cpp
conda install -c conda-forge eigen c-compiler cxx-compiler make

Now we can build the application:

cmake -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build --config Release --parallel
cd build

‍‍And finally we’re ready to run the LCM demo. By default the positive prompt is set to: “a beautiful pink unicorn”.

Please note, that the quality of the resulting image depends on the quality of the random noise generator, so there is a difference for output images generated by the C++ noise generator and the PyTorch generator. Use oprion -r to read the PyTorch generated noise from the provided textfiles for the alignment with Python pipeline.
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Note: Run ./lcm_dreamshaper -h to see all the available demo options

Let’s try to run the application in a few modes:

Read the numpy latent input and noise for scheduler instead of C++ std lib for the alignment with Python pipeline: ./lcm_dreamshaper -r‍

Generate image with C++ std lib generated latent and noise : ./lcm_dreamshaper

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Generate image with Soulcard LoRa and C++ generated latent and noise: ./lcm_dreamshaper -r -l path/to/soulcard.safetensors

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See Also

1. Optimizing Latent Consistency Model for Image Generation with OpenVINO™ and NNCF
2. Image generation with Latent Consistency Model and OpenVINO
3. C++ Pipeline for Stable Diffusion v1.5 with Pybind for Lora Enabling
4. Enable LoRA weights with Stable Diffusion Controlnet Pipeline

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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.