deepsparse
openai-whisper-cpu
deepsparse | openai-whisper-cpu | |
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21 | 5 | |
2,878 | 221 | |
1.5% | - | |
9.5 | 10.0 | |
about 9 hours ago | over 1 year ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
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deepsparse
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Fast Llama 2 on CPUs with Sparse Fine-Tuning and DeepSparse
Interesting company. Yannic Kilcher interviewed Nir Shavit last year and they went into some depth: https://www.youtube.com/watch?v=0PAiQ1jTN5k DeepSparse is on GitHub: https://github.com/neuralmagic/deepsparse
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The future of quantization techniques in deep learning.
sparsity https://github.com/neuralmagic/deepsparse
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.
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[D] Most efficient open source language model ?
You should look into deepsparse, they are working on delivering GPU level performance on consumer CPUs with some great results: https://github.com/neuralmagic/deepsparse. There is a great interview with the founder, Nir Shavit here: https://piped.kavin.rocks/watch?v=0PAiQ1jTN5k
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[R] New sparsity research (oBERT) enabled 175X increase in CPU performance for MLPerf submission
Utilizing the oBERT research we published at Neural Magic and some further iteration, we’ve enabled an increase in NLP performance of 175X while retaining 99% accuracy on the question-answering task in MLPerf. A combination of distillation, layer dropping, quantization, and unstructured pruning with oBERT enabled these large performance gains through the DeepSparse Engine. All of our contributions and research are open-sourced or free to use. Read through the oBERT paper on arxiv, try out the research in SparseML, and dive into the writeup to learn more about how we achieved these impressive results and utilize them for your own use cases!
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
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[R] BERT-Large: Prune Once for DistilBERT Inference Performance
BERT-Large (345 million parameters) is now faster than the much smaller DistilBERT (66 million parameters) all while retaining the accuracy of the much larger BERT-Large model! We made this possible with Intel Labs by applying cutting-edge sparsification and quantization research from their Prune Once For All paper and utilizing it in the DeepSparse engine. It makes BERT-Large 12x smaller while delivering 8x latency speedup on commodity CPUs. We open-sourced the research in SparseML; run through the overview here and give it a try!
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[R] How well do sparse ImageNet models transfer? Prune once and deploy anywhere for inference performance speedups! (arxiv link in comments)
And benchmark/deploy with 8X better performance in DeepSparse!
- Sparseserver.ui – test the performance of Sparse Transformers
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[P] SparseServer.UI : A UI to test performance of Sparse Transformers
Hi _Arsenie, this runs the deepsparse.server command for multiple models. and btw, we recently updated the READMEs for the Deepsparse Engine https://github.com/neuralmagic/deepsparse
openai-whisper-cpu
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How to run Llama 13B with a 6GB graphics card
I feel the same.
For example some stats from Whisper [0] (audio transcoding) show the following for the medium model (see other models in the link):
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GPU medium fp32 Linear 1.7s
CPU medium fp32 nn.Linear 60.7
CPU medium qint8 (quant) nn.Linear 23.1
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So the same model runs 35.7 times faster on GPU, and compared to an CPU-optimized model still 13.6.
I was expecting around an order or magnitude of improvement. Then again, I do not know if in the case of this article the entire model was in the GPU, or just a fraction of it (22 layers), which might explain the result.
[0] https://github.com/MiscellaneousStuff/openai-whisper-cpu
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Whispers AI Modular Future
According to https://github.com/MiscellaneousStuff/openai-whisper-cpu the medium model needs 1.7 seconds to transcribe 30 seconds of audio when run on a GPU.
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[P] Transcribe any podcast episode in just 1 minute with optimized OpenAI/whisper
There is a very simple method built-in to PyTorch which can give you over 3x speed improvement for the large model, which you could also combine with the method proposed in this post. https://github.com/MiscellaneousStuff/openai-whisper-cpu
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For CPU inference, model quantization is a very easy to apply method with great average speedups which is already built-in to PyTorch. For example, I applied dynamic quantization to the OpenAI Whisper model (speech recognition) across a range of model sizes (ranging from tiny which had 39M params to large which had 1.5B params). Refer to the below table for performance increases:
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[P] OpenAI Whisper - 3x CPU Inference Speedup
GitHub
What are some alternatives?
NudeNet - Neural Nets for Nudity Detection and Censoring
llama-cpp-python - Python bindings for llama.cpp
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
whisperX - WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
buzz - Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
kernl - Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.