tvm VS deepsparse

Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:

Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447

Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?

I have tried the iPhone app. It's fast. They're using Apache TVM which should allow better use of native accelerators on different devices. Like using metal on Apple and Vulcan or CUDA or whatever instead of just running the thing on the CPU like llama.cpp.

I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity, IREE [2], or torch.export [3]

[1] https://github.com/apache/tvm/tree/unity

[2] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...

[3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...

Apache TVM home page: https://tvm.apache.org/

It's pretty clear its referring to the output of Apache TVM and Meta's Glow

As mentioned in sibling comments, Torch is indeed the glue in this implementation. Other glues are TVM[0] and ONNX[1]

These just cover the neural net though, and there is lots of surrounding code and pre-/post-processing that isn't covered by these systems.

For models on Replicate, we use Docker, packaged with Cog for this stuff.[2] Unfortunately Docker doesn't run natively on Mac, so if we want to use the Mac's GPU, we can't use Docker.

I wish there was a good container system for Mac. Even better if it were something that spanned both Mac and Linux. (Not as far-fetched as it seems... I used to work at Docker and spent a bit of time looking into this...)

[0] https://tvm.apache.org/

Or use TVM, the idea is to compile your model into code that you can load at runtime. Similar to onnxruntime, it only does DNN inference; so you need domain-specific code.

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.

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

sparsity https://github.com/neuralmagic/deepsparse

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.

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

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!

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.

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!

And benchmark/deploy with 8X better performance in DeepSparse!

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