MiniCPM is an End-Side LLM developed by ModelBest Inc. and TsinghuaNLP. MiniCPM-V is a series of end-side multimodal LLMs (MLLMs) designed for vision-language understanding. The models take image and text as inputs and provide high-quality text outputs. MiniCPM-V 2.0 is an efficient version with promising performance for deployment. The model is built based on SigLip-400Mand MiniCPM-2.4B, connected by a perceiver resampler. On this blog, we provide the OpenVINO™ optimization for MiniCPM-V 2.0 on Intel® platforms.
python ov_minicpm-v2-test.py -m /path/to/ MiniCPM-V-2 _ov -pic /path/to/hk_OCR.jpg -p “Describe the content of the image”
Question: Describe the content of the image
hk_OCR
Answer:
The image captures the vibrant and bustling atmosphere of abusy city street in Hong Kong. The street is lined with an array of neon signsand billboards, each one advertising a different business or establishment. Thesigns are in a variety of languages, including English, Chinese, and Japanese,reflecting the multicultural nature of the city.
The street itself is a hive of activity with several busesand a tram making their way through the traffic. The vehicles are in motion,adding a dynamic element to the scene.
The sky above is a beautiful gradient of colors,transitioning from a deep blue at the top to a lighter shade at the bottom.This suggests that the photo was taken during either sunrise or sunset, castinga warm glow over the cityscape.
The image also contains several text elements, including thenames of various establishments and the brand names of products. These textsadd another layer of information to the scene, providing insights into thenature of the businesses and the products they offer.
Overall, the image provides a vivid snapshot of life in HongKong, capturing the city's vibrant energy and the diverse range of businessesand products that make up its bustling streets.
Authors: Tianmeng Chen, Xiake Sun, Fiona Zhao, Su Yang
Introduction
Personalized Speech Synthesis is the process of using some recording devices around you to record certain voice clips of a particular person, and then letting Text-To-Speech (TTS) technology synthesize the voice, manner of speaking, and emotion of a particular person. SAMBERT-HifiGAN is a complete personalized TTS solution designed by Alibaba Damo Institute, which includes the first part of SAMBERT's acoustic model and the second part of the HifiGAN vocoder.
Structure of SAMBERT
Structure of HifiGAN
In this blog, we will introduce how to utilize OpenVINOTM Python API to enable the SAMBERT-HifiGAN pipeline. All the project code can be found here.
KAN-TTS by Ali provides a tutorial for training SAMBERT-HifiGAN. A pipeline for personalized speech synthesis based on PyTorch is provided on modelscope, what we will do here is toreplace the PyTorch based part of it with OpenVINOTM. It is worth noting that due to some of the operators in the model, there are some modules that cannot be replaced with OpenVINOTM Python API.
Pre-requisite
Since we need to make changes on the pipeline based on PyTorch backend, the first thing we need to do is to download the KAN-TTS source code and successfully run through the pipeline to get the inputs and outputs of the model as well as the state of the middle layer. Of course we also need the OpenVINOTM environment.
Get the KAN-TTS source code and create anacondaenvironment.
git clone -b develop https://github.com/alibaba-damo-academy/KAN-TTS.git
cd KAN-TTS
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
conda env create -f environment.yaml
conda activate maas
Then we install openvino in same environment. Ifyou want specific version of OpenVINOTM, you can install it byyourself through Install OpenVINO™.
pip install openvino
Follow the KAN-TTS practice tutorial of official with readme in ModelScope.
After you finish the pipelining of KAN-TTS, you can get the res folder and ckpt filesspeech_personal_sambert-hifigan_nsf_tts_zh-cn_pretrain_16k .
Get the OpenVINOTM backend projectsource code and copy the res folder to project folder.
Aftera few minutes, you will get two converted OpenVINOTM model sambert_encoder.xml sambert_encoder.bin and hifigan_t.xml hifigan_t.bin.
In the code after we load the model and get the inputs, we add the following code to convert the loaded PyTorch backend model to OpenVINOTM backend model and save it.
Example code of SAMBERT pipeline
Example code of HifiGAN pipeline
Run the inferencewith OpenVINOTM model
Before running the inference, the res folder should be renamed to allow for comparisons later.
After a few minutes, you will get the wav file in res/test_male_ptts_syn. For example in test.txt we write a random sentence:
After running pipeline, we will get a 7 seconds wav file under res folder:
In the code we modified the original pytorch banckend inference code so that pipeline uses openvino backend for inference.
Example code of SAMBERT pipeline
Example code of HifiGAN pipeline
Summary
This blog describes about how to run the SAMBERT-HifiGANpipeline using the OpenVINOTM Python API, please see the source code formore details and modifications.
Use Case 1: C++ RAG Sample that supports most popular models like LLaMA 2
This example showcases for Retrieval-Augmented Generation based on text-generation Large Language Models (LLMs): chatglm, LLaMA, Qwen and other models with the same signature and Bert model for embedding feature extraction. The sample fearures ov::genai::LLMPipeline and configures it for the chat scenario. There is also a Jupyter notebook which provides an example of LLM-powered RAG in Python.
Download and convert the model and tokenizers
The --upgrade-strategy eager option is needed to ensure optimum-intel is upgraded to the latest version.
Load: document_loaders is used to load document data.
Split: text_splitter breaks large Documents into smaller chunks. This is useful both for indexing data and for passing it in to a model, since large chunks are harder to search over and won’t in a model’s finite context window.
PostgreSQL
Download postgresql from enterprisedb.(postgresql-16.2-1-windows-x64.exe is tested)
Install PostgreSQL with postgresqltutorial. Setup of PostgreSQL: 1. Open pgAdmin 4 from Windows Search Bar. 2. Click Browser (left side) > Servers > Postgre SQL 10. 3. Create the user postgres with password openvino (or your own setting) 4. Open SQL Shell from Windows Search Bar to check this setup. 'Enter' to set Server, Database, Port, Username as default and type Password.
Server [localhost]:
Database [postgres]:
Port [5432]:
Username [postgres]:
Password for user postgres:
Open-source vector similarity search for Postgres.
By default, pgvector performs exact nearest neighbor search, which provides perfect recall. It also supports approximate nearest neighbor search (HNSW), which trades some recall for speed.
For Windows, Ensure C++ support in Visual Studio 2022 is installed, then use nmake to build in Command Prompt for VS 2022(run as Administrator). Please follow with the pgvector
Enable the extension (do this once in each database where you want to use it), run SQL Shell from Windows Search Bar with "CREATE EXTENSION vector;".
Printing CREATE EXTENSION shows successful setup of Pgvector.
pgvector support for C++ (supports libpqxx). The headers (pqxx.hpp, vector.hpp, halfvec.hpp) are copied into the local folder rag_sample\include. Our pipeline does the vector similarity search for the chunks embeddings in PostgreSQL, based on pgvector-cpp.
Install OpenVINO, VS2022 and Build this pipeline
Download 2024.2 release from OpenVINO™ archives*. This OV built package is for C++ OpenVINO pipeline, no need to build the source code. Install latest Visual Studio 2022 Community for the C++ dependencies and LLM C++ pipeline editing.
Extract the zip file in any location and set the environment variables with dragging this setupvars.bat in the terminal Command Prompt. setupvars.ps1 is used for terminal PowerShell. <INSTALL_DIR> below refers to the extraction location. Run the following CMD in the terminal Command Prompt.
Install on Windows: Copy all the DLL files of PostgreSQL, OpenVINO and tbb and openvino-genai into the release folder. The SQL DLL files locate in the installed PostgreSQL path like "C:\Program Files\PostgreSQL\16\bin".
If cmake not installed in the terminal Command Prompt, please use the terminal Developer Command Prompt for VS 2022 instead.
The openvino tokenizer in the third party needs several minutes to build. Set 8 for -j option to specify the number of parallel jobs.
Once the cmake finishes, check rag_sample_client.exe and rag_sample_server.exe in the relative path .\build\samples\cpp\rag_sample\Release.
If Cmake completed without errors, but not find exe, please open the .\build\OpenVINOGenAI.sln in VS2022, and set the solution configuration as Release instead of Debug, then build the llm project within VS2022 again.
Run
Launch RAG Server
rag_sample_server.exe --llm_model_path TinyLlama-1.1B-Chat-v1.0 --llm_device CPU --embedding_model_path bge-large-zh-v1.5 --embedding_device CPU --db_connection "user=postgres host=localhost password=openvino port=5432 dbname=postgres"
Lanuch RAG Client
rag_sample_client.exe
Lanuch python Client
Use python client to send the message of DB init and send the document chunks to DB for embedding and storing.
To enable the SDPA fusion on GPU, we firstly need to convert model IR with SDPA Op.
Create new class SdpaAttention in the modeling_ custom_model.py using torch.scaled_dot_product_attention. This Pytorch Op could be matched and converted into OpenVINO SDPA Op.
Fig1: from Attention Is All You Need
Refer to Phi3SdpaAttention, this module is inherited from Phi3Attention as the weights of the module stay untouched. The only changes are on the forward pass to adapt to SDPA API.
From the equivalent implementation, the target is to replace the related Pytorch Ops(like softmax, matmul and dropout) with torch.nn.functional.scaled_dot_product_attention.
Fig2: implementation equivalent from PyTorch 2.3 documentation
For some custom model, the equivalent code in is as follow:
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout_rate if self.training else 0.0,
is_causal= True and attention_mask is None and q_len > 1,
scale=1/math.sqrt(self.head_dim)
)
The SDPA Operator has already implemented. The SDPA fusion on GPU supports for head size =128 at OV24.2 release.
The OV24.3 release relax SDPA head size limitations for LLMs from 128 only to a range of 64 to 256, thanks to Sergey’s PR.
Usage
Replace the original modeling_custom_model.py with the new script (with SdpaAttention) in the Pytorch model folder.
Notice:
After converting model again, check the SDPA layers (“aten::scaled_dot_product_attention”) in the OV IR .xml file.
Double check the OpenVINO executable graph for the SDPA enabling.
Don’t forget to check the accuracy of the Pytorch model inference, after any modification with modeling_custom_model.py.
Conclusion
In this blog, we introduce how to use torch.scaled_dot_product_attention to enable the SDPA for custom model.
Performance improvement with SDPA on MTL iGPU is depended on the model structure. SDPA enabling for the custom model is the base for further optimization like Page Attention.
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