-p, --posPrompt arg Initial positive prompt for SD (default: cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting)
-n, --negPrompt arg Default negative prompt is empty with space (default: )
-d, --device arg AUTO, CPU, or GPU (default: CPU)
-s, --seed arg Number of random seed to generate latent (default: 42)
--height arg height of output image (default: 512)
--width arg width of output image (default: 512)
--log arg Generate logging into log.txt for debug
-c, --useCache Use model caching
-e, --useOVExtension Use OpenVINO extension for tokenizer
-r, --readNPLatent Read numpy generated latents from file
-m, --modelPath arg Specify path of SD model IR (default: /YOUR_PATH/SD_ctrlnet/dreamlike-anime-1.0)
-t, --type arg Specify precision of SD model IR (default: FP16_static)
Positive prompt: cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting.
Negative prompt: (empty, here couldn't use OV tokenizer, check the issues for details).
Read the numpy latent instead of C++ std lib for the alignment with Python pipeline.
Generate image without lora
./SD-generate -r -l ""
Fig. 1 without Lora
Generate image with Soulcard Lora
./SD-generate -r
Fig. 2 with Lora
Generate the debug logging into log.txt
./SD-generate --log
Benchmark:
The performance and image quality of C++ pipeline are aligned with Python.
To align the performance with Python SD pipeline, C++ pipeline will print the duration of each model inferencing only.
For the diffusion part, the duration is for all the steps of Unet inferencing, which is the bottleneck.
For the generation quality, be careful with the negative prompt and random latent generation.
Limitation:
Pipeline features:
- Batch size 1
- LMS Discrete Scheduler
- Text to image
Program optimization: now parallel optimization with std::for_each only and add_compile_options(-O3 -march=native -Wall) with CMake
The pipeline with INT8 model IR not improve the performance
Lora enabling only for FP16
Random generation fails to align, C++ random with MT19937 results is differ from numpy.random.randn(). Hence, please use -r, --readNPLatent for the alignment with Python
OV extension tokenizer cannot recognize the special character, like “.”, ”,”, “”, etc. When write prompt, need to use space to split words, and cannot accept empty negative prompt. So use default tokenizer without config -e, --useOVExtension, when negative prompt is empty
Setup in Windows 10 with VS2019:
1. Python env: Setup Conda env SD-CPP with the anaconda prompt terminal
2. C++ dependencies:
OpenVINO and OpenCV:
Download and setup Environment Variable: add the path of bin and lib (System Properties -> System Properties -> Environment Variables -> System variables -> Path )
In the ever-evolving landscape of biopharmaceutical technology and drug development, a recent effort in the field of Cell Analytics for Monoclonal Antibody Production has shed light on the crucial role of Edge AI Technology in navigating complex challenges of scaling and producing solutions.
In this 2-part blog series, we will explore the use of Intel Edge AI Technology in biopharma and drug development, addressing challenges and providing insights into the development of AI pipelines for cell segmentation and analysis.
Intel has been involved in this process with a variety of partners. One of Intel’s contributions to the cell image project centers around processing brightfield1 images using an AI pipeline containing multiple deep learning models. The pipeline's purpose is to identify cells and other biological components and provide feedback on dynamic biological characteristics such as cell morphology, viability, and phenotypic changes, among others. Throughout this process, working on cell-AI projects usually brings a unique set of challenges to the forefront.
First, it is an interdisciplinary field and the knowledge gap between data scientists and biopharma experts requires more back-and-forth clear communications for planning and validity checks. Frequently when attempting to implement AI solutions in the laboratory, data scientists and bench scientists struggle to fully grasp the nature and needs of each other’s role. This lack of mutual understanding can also hinder the usability and scalability of an AI solution needing to be integrated into diverse lab environments.
The secondchallenge is instrument variability. Different plate reader2 microscopes have different hardware, optics, and apertures which cause their produced images not to be consistent. This adds an extra layer of work to assess and address these inconsistencies along the way (like regular tracked calibration and adjustment). Additionally, equipment vendor-to-vendor differences, culture temperature, medium conditions, and genetic modifications can all affect the variability of data and the inherent transferability of the deep learning pipeline. This would drive the need to monitor the performance of DL models at the edge and cloud ML ops components.
The third challenge is obtaining peer-review labels because the process is based on supervised Machine Learning and obtaining clean accurate labels is very costly and time-consuming.
And the last challenge is about the model deployment. In most cases, cloud deployment is not an option due to data size and data privacy. Produced images from plate reader microscopes are huge and transferring data to the cloud and sending the results back would create high latency because a huge amount of data must be streamed (30Gb per hour). And more importantly, laboratories are usually not willing to share the data. Due to these two constraints, cloud deployments are not usually an option, and the pipeline must be deployed at the edge.
Now, let’s talk about a specific application of this technology: the CHO Cell Segmentation Use Case.
CHO Cell Segmentation Use Case
CHO cells, or Chinese Hamster Ovary cells, are a cornerstone in the production of complex protein molecules such as monoclonal antibodies, fusion proteins, hormones, and coagulation factors. Unlike stem cells or CAR-T cells, where the cells themselves are the therapeutic product, in CHO cells, it is the proteins they produce that are of paramount importance. Monitoring the health, viability, and production capability of these cells is a critical step in commercial protein production.
Traditionally, assessing the condition of CHO cells involves a multi-step process that is not only time-consuming but also requires the use of expensive reagents and chemicals. Depending on the process, the workflow can be something like below.
Culture cells
Fix cells – wash in expensive reagents to remove the culture medium.
Permeabilization – wash in more expensive chemicals to permeabilize the cell membrane (to stain for intercellular proteins).
Blocking – incubate cells in another expensive reagent to prevent binding of no specific antibodies.
Primary Antibody Incubation – antibody specifically to bind to a protein that is being produced.
Washing – removing unbound Primary Antibodies using more expensive chemicals.
Nuclear staining – use nuclear stain like DAPI to visualize cell nuclei then wash with the same chemicals from the washing step
Mounting – get ready to read in the microscope (plate reader1)
Imaging – Stained cells …. count them up and determine the state in the protein production cycle and relative cell health (eventually they peter out and stop producing and the batch needs to be flushed. (Cell count, viability number, etc. are the output not the image)
From culturing to imaging, each step plays a vital role in ensuring the quality of the protein product. However, with the advent of AI and deep learning, there is an opportunity to streamline this workflow significantly. Using an AI pipeline including multiple Deep Learning models and data pre and post-processing, we can go from Step 1 directly to Step 9, removing the majority of the labor and latency in getting actionable results out of a staining workflow and bypassing expensive specialty chemicals requirement. Intel has put together a reference implementation for deploying said pipeline and inferencing of these images on the edge as part of the Cell Image project https://www.cellimage.ie/. OpenVINO Toolkit, OpenVINO Model Server, and AI Connect for Scientific Data are used in this design.Let’s briefly talk about each of these wonderful SW packages in part 2 of this article series. Stay tuned!
Conclusion
In conclusion, the integration of Intel Edge AI Technology into the biopharmaceutical sector represents a transformative step towards more efficient and scalable drug development processes. As we have seen in this first installment of our blog series, the deployment of AI pipelines for cell segmentation and analysis in monoclonal antibody production is not without its challenges. These include bridging the interdisciplinary knowledge gap, managing instrument variability, acquiring peer-reviewed labels, and overcoming the hurdles associated with model deployment.
Despite these challenges, the potential benefits of Edge AI in biopharma are substantial. By leveraging Intel's advanced AI technologies, we can significantly reduce the time and cost associated with traditional cell analysis methods, while also enhancing the accuracy and reliability of the results. The use of edge computing addresses the concerns of data size and privacy, allowing for real-time processing and analysis without the need for cloud transfer.
As we move forward in this blog series, we will delve deeper into the specifics of Intel's Edge AI solutions, including the OpenVINO toolkit, OpenVINO Model Server, and AI Connect for Scientific Data. We will explore how these tools are being applied in real-world scenarios to drive innovation and improve outcomes in the realm of biopharma and drug development in the next part of this series.
We'd like to hear from you! Let us know in the comments or discuss – which AI use cases in health and life sciences do you think will have the greatest impact on global health?
If you enjoyed hearing from the Health and Life Sciences team and want to hear more, give this post a like and ensure you subscribe to get the latest updates from the team.
About the Author
Nooshin Nabizadeh has Ph.D. in Electrical and Computer Engineering from the University of Miami and works at Intel Corporation as AI Solutions Architect. She enjoys photography, writing poetry, reading about psychology and philosophy, and optimizing solutions to run as fast as possible on a given piece of hardware. Connect with her on LinkedIn https://www.linkedin.com/in/nooshin-nabizadeh/ by mentioning this blog.
Brightfield microscopy is a widely used technique for observing the morphology of cells and tissues.
A plate reader is a laboratory instrument used to obtain images from samples in microtiter plates. The reader shines a specific calibrated frequency of light (UV, visible, fluorescence, etc.) through the samples in the wells of the plate. Plate reader microscopy data sets have inherent variability which drives the requirement of regular tracked calibration and adjustment.
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:
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”:
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”:
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”:
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”.
manually modify layer type and version to extension operation
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:
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.
Authors: Nikita Savelyev, Alexander Kozlov, Ekaterina Aidova, Maxim Proshin
Introduction
Whisper is a general-purpose speech recognition model from OpenAI. The model can transcribe speech across dozens of languages and even handle poor audio quality or excessive background noise. You can find more information about this model in the research paper, OpenAI blog, model card and GitHub repository.
Recently, a distilled variant of the model called Distil-Whisper has been proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. Compared to Whisper, Distil-Whisper runs several times faster with 50% fewer parameters, while performing to within 1% word error rate (WER) on out-of-distribution evaluation data.
Whisper is a Transformer-based encoder-decoder model, also referred to as a sequence-to-sequence model. It maps a sequence of audio spectrogram features to a sequence of text tokens. First, the raw audio inputs are converted to a log-Mel spectrogram by action of the feature extractor. Then, the Transformer encoder encodes the spectrogram to form a sequence of encoder hidden states. Finally, the decoder autoregressively predicts text tokens, conditional on both the previous tokens and the encoder's hidden states.
You can see the model architecture in the diagram below:
In this article, we would like to demonstrate how to improve Whisper and Distil-Whisper inference speed with OpenVINO for Intel hardware. Additionally, we show how to make models even faster by applying 8-bit Post-training Quantization with Neural Network Compression Framework (NNCF). In the end we present evaluation results from accuracy and performance standpoints on a large-scale dataset.
We are going to load models from Hugging Face Hub with the help of Optimum Intel library which makes it easier to load and run OpenVINO-optimized models. For more details, pleaes refer to the Hugging Face Optimum documentation.
For example, the following code loads the Distil-Whisper large-v2 model ready for inference with OpenVINO.
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "distil-whisper/distil-large-v2"
model_path = Path(model_id)
if not model_path.exists():
ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
model_id, export=True, compile=False, load_in_8bit=False)
ov_model.half()
ov_model.save_pretrained(model_path)
else:
ov_model = OVModelForSpeechSeq2Seq.from_pretrained(
model_path, compile=False)
To transcribe an input audio with the loaded model, we first compile the model to the device of choice and then call generate() method on input features prepared by corresponding processor.
The output is the following. As you can see the transcription equals the reference text.
Reference: MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL
Result: Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
Running Post-Training Quantization with NNCF
NNCF enables post-training quantization by adding quantization layers into the model graph and then using a subset of the training dataset to initialize parameters of these additional quantization layers. During quantization, some layers (e.g., MatMuls, Convolutions) are transformed to be executed in INT8 instead of FP16/FP32. If a quantized operation is parameterized then its corresponding weight variable is also converted to INT8.
In general, the optimization process contains the following steps:
Create a calibration dataset for quantization.
Run nncf.quantize() to obtain quantized encoder and decoder models.
Serialize the INT8 models using openvino.save_model() function.
Whisper model consists of an encoder and decoder submodels. Furthermore, for the decoder model its forward() signature is different for the first call compared to all subsequent calls. During the first call, key-value cache is empty and is not needed for decoder inference. Starting from the second call, key-value cache is fed to the decoder. Because of this, these two cases are represented by two separate OpenVINO models: openvino_decoder_model.xml and openvino_decoder_with_past_model.xml. Since the first decoder model is inferred only once it does not make much sense to quantize it. So, we apply quantization to the encoder and the decoder with past models.
The first step towards quantization is collecting calibration data. For that, we need to collect some number of model inputs for both models. To do that, we patch OpenVINO model request objects with an InferRequestWrapper class instance that will intercept model inputs during inference and store them in a list. We infer the model on about 50 samples from validation split of librispeech_asr dataset.
def collect_calibration_dataset(ov_model: OVModelForSpeechSeq2Seq, calibration_dataset_size: int):
# Overwrite model request properties, saving the original ones for restoring later
original_encoder_request = ov_model.encoder.request
original_decoder_with_past_request = ov_model.decoder_with_past.request
encoder_calibration_data = []
decoder_calibration_data = []
ov_model.encoder.request = InferRequestWrapper(original_encoder_request, encoder_calibration_data)
ov_model.decoder_with_past.request = InferRequestWrapper(original_decoder_with_past_request,
decoder_calibration_data)
calibration_dataset = load_dataset("librispeech_asr", "clean", split="validation", streaming=True)
for sample in islice(calibration_dataset, calibration_dataset_size):
input_features = extract_input_features(sample)
ov_model.generate(input_features)
ov_model.encoder.request = original_encoder_request
ov_model.decoder_with_past.request = original_decoder_with_past_request
return encoder_calibration_data, decoder_calibration_data
With the collected calibration data for encoder and decoder models we can proceed to quantization itself. Let's examine the quantization call for the encoder model. For the decoder model, it is similar.
quantized_encoder = nncf.quantize(
ov_model.encoder.model, # ov.Model object of the encoder model
nncf.Dataset(encoder_calibration_data), # calibration data wrapped in a nncf.Dataset object
subset_size=len(encoder_calibration_data), # number of samples to calibrate on (all are chosen)
model_type=nncf.ModelType.TRANSFORMER, # providing the information that Whisper encoder is of
# a Transformer architecture
advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alpha=0.50) # Smooth Quant
# algorithm reduces activation quantization error; optimal alpha was obtained through grid search
)
ov.save_model(quantized_encoder, quantized_model_path / "openvino_encoder_model.xml")
After both models are quantized and saved, the quantized Whisper model can be loaded and run the same way as shown previously. Comparing the transcriptions produced by original and quantized models results in the following.
Original : Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
Quantized: Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
As you can see for the quantized distil-whisper-large-v2 transcription is the same.
Evaluating on Common Voice Dataset
We evaluate Whisper and Distil-Whisper large-v2 model variants on a Common Voice 13.0 speech-to-text dataset. We use en/test split containing 16372 audio samples amounting to about 27 hours of recordings.
For all combinations above we measure transcription time and accuracy. When measuring time for a model we sum up generate() call durations for all audio samples. Transcription accuracy is represented as Accuracy = (100 - WER), WER stands for Word Error Rate. We compute accuracy for each audio sample and then take the average value across the dataset. The results are given in the table below.
Please note that we report transcription time in relative terms such that the values for each CPU are normalized over its corresponding column. The duration of audio data in the dataset is 27.06 hours and the absolute transcription time values for Whisper large-v2 PyTorch on each CPU are:
20.35 hours for Core i9-10980XE
14.09 hours for Xeon Gold 6338
15.03 hours for Xeon Gold 6430L
Based on the results we can conclude that:
OpenVINO models execute 1.4x - 5.1x faster than PyTorch models with pretty much the same accuracy across all cases.
When compared to original PyTorch models, quantized OpenVINO models provide 2.1x - 6.1x performance boost with 1-2% accuracy drop.
NOTE: in terms of this article we focus on presenting performance values. Accuracy of quantized models can be improved with a more careful selection of calibration data.
Notices and Disclaimers:
Performance varies by use, configuration, and other factors. Learn more at www.intel.com/PerformanceIndex. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates. No product or component can be absolutely secure. Intel technologies may require enabled hardware, software or service activation.
The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.
Test Configuration: Intel® Core™ i9-10980XE CPU Processor at 3.00GHz with DDR4 128 GB at 3000MHz, OS: Ubuntu 20.04.3 LTS; Intel® Xeon® Gold 6338 CPU Processor at 2.00GHz with DDR4 256 GB at 3200MHz, OS: Ubuntu 20.04.3 LTS; Intel® Xeon® Gold 6430L CPU Processor at 1.90GHz with DDR5 1024 GB at 4800MHz, OS: Ubuntu 20.04.6 LTS. Testing was performed using distil-whisper-asr notebook for model export and whisper evaluation notebook for model evaluation.
The test was conducted by Intel in December 2023.
Conclusion
We demonstrated how to load and run Whisper and Distil-Whisper models for audio transcription task with OpenVINO and Optimum Intel, and how to perform INT8 post-training quantization of these models with NNCF. Further we evaluated these models on a large scale speech-to-text dataset across multiple CPU devices. The evaluation results show a significant performance boost of OpenVINO vs PyTorch models without loss of transcription quality, and even a larger boost with a tolerable accuracy drop when we apply INT8 quantization.
