intel-extension-for-pytorch
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intel-extension-for-pytorch | stable_diffusion.openvino | |
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14 | 47 | |
1,342 | 1,524 | |
9.6% | - | |
9.7 | 0.8 | |
3 days ago | 7 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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intel-extension-for-pytorch
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Efficient LLM inference solution on Intel GPU
OK I found it. Looks like they use SYCL (which for some reason they've rebranded to DPC++): https://github.com/intel/intel-extension-for-pytorch/tree/v2...
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
Just to point out it does, kind of: https://github.com/intel/intel-extension-for-pytorch
I've asked before if they'll merge it back into PyTorch main and include it in the CI, not sure if they've done that yet.
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Watch out AMD: Intel Arc A580 could be the next great affordable GPU
Intel already has a working GPGPU stack, using oneAPI/SYCL.
They also have arguably pretty good OpenCL support, as well as downstream support for PyTorch and Tensorflow using their custom extensions https://github.com/intel/intel-extension-for-tensorflow and https://github.com/intel/intel-extension-for-pytorch which are actively developed and just recently brought up-to-date with upstream releases.
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How to run Llama 13B with a 6GB graphics card
https://github.com/intel/intel-extension-for-pytorch :
> Intel® Extension for PyTorch extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*
https://pytorch.org/blog/celebrate-pytorch-2.0/ :
> As part of the PyTorch 2.0 compilation stack, TorchInductor CPU backend optimization brings notable performance improvements via graph compilation over the PyTorch eager mode.
The TorchInductor CPU backend is sped up by leveraging the technologies from the Intel® Extension for PyTorch for Conv/GEMM ops with post-op fusion and weight prepacking, and PyTorch ATen CPU kernels for memory-bound ops with explicit vectorization on top of OpenMP-based thread parallelization*
DLRS Deep Learning Reference Stack: https://intel.github.io/stacks/dlrs/index.html
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Train Lora's on Arc GPUs?
Install intel extensions for pytorch using docker. https://github.com/intel/intel-extension-for-pytorch
- Does it make sense to buy intel arc A770 16gb or AMD RX 7900 XT for machine learning?
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PyTorch Intel HD Graphics 4600 card compatibility?
There is: https://github.com/intel/intel-extension-for-pytorch for intel cards on GPUs, but I would assume this doesn't extend to integraded graphics
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Stable Diffusion Web UI for Intel Arc
Nonetheless, this issue might be relevant for your case.
- Does anyone uses Intel Arc A770 GPU for machine learning? [D]
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Will ROCm finally get some love?
I'm not sure where the disdain for ROCm is coming from, but tensorflow-rocm and the rocm pytorch container were fairly easy to setup and use from scratch once I got the correct Linux kernel installed along with the rest of the necessary ROCm components needed to use tensorflow and pytorch for rocm. TBF Intel Extension for Tensorflow wasn't too bad to setup either (except for the lack of float16 mixed precision training support, that was definitely a pain point to not be able to have), but Intel Extension for Pytorch for Intel GPUs (a.k.a. IPEX-GPU) however, has been a PITA to use for my i5 11400H iGPU NOT because the iGPU itself is slow, BUT because the current i915 driver in the mainline linux kernel simply doesn't work with IPEX-GPU (every script that I've ran ends up freezing when using even the i915 drivers as recent as Kernel version 6), and when I ended up installing drivers that were meant for the Arc GPUs that finally got IPEX-GPUs to work, I ended up with even more issues such as sh*tty FP64 emulation support that basically meant I had to do some really janky workarounds for things to not break while FP64 emulation was enabled (disabling was simply not an option for me, long story short). And yea unlike Intel, both Nvidia AND AMD actually do support FP64 instructions AND FLOAT16 mixed precision training natively on their GPUs so that one doesn't have to worry about running into "unsupported FP64 instructions" and "unsupported training modes" no matter what software they're running on those GPUs.
stable_diffusion.openvino
- FLaNK Stack 05 Feb 2024
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Installing A1111 Stable Diffusion Error
it might be the --xformers flag, try getting rid of that since your not using cuda you wouldn't be able to run it with xformers and you could also try --use-cpu all ... you can also check this out .. https://github.com/bes-dev/stable_diffusion.openvino .. it's probably your best option if your using CPU, which if your PC Graphics are using Intel UHD 620 then you don't have a GPU and an optimized CPU inference would be best to run
- 4 Reasons to Switch to Intel Arc GPUs
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why is SD not actually using the GPU?
SD can be run on a CPU without a GPU. I know for certain it can be done with OpenVINO. In fact, on some i7s, it will run at around 3 seconds per iteration. There was a reddit SD thread a while back saying it can be done with Automatic111. Also, soe recent threads on problems with AMD GPUs suggest Automatic1111 is using the CPU rather than the intended GPU. (Fortuanely, I have a GPU, so I don't have to deal with it myself!)
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Slow Performance on RX 6800 XT; Am I Doing Something Wrong or is ROCm Just this Slow?
I'm not actually entirely convinced that it's even using the GPU. Radeontop shows 0% utilization while the images are generating. Additionally, the listed iteration speed should be impossibly slow for any GPU; it says 26.58s/it, which is slower than just running on a CPU.
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How can i fix it?
iGPU's are in short not supported. There's this repo that may or may not help you, but even if it did I wouldn't expect much.
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Stable Diffusion Web UI for Intel Arc
You can also run it in windows native with openvino, there is a barebones webui for it as well in one of the forks.Requires setting cpu to gpu in one the files. https://github.com/bes-dev/stable_diffusion.openvino
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Intel Arc A770 is underperforming in Tom's Hardware Review
In https://github.com/bes-dev/stable_diffusion.openvino/blob/master/stable_diffusion_engine.py
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So a new benchmark was done for Stable Diffusion on GPU's
" We ended up using three different Stable Diffusion projects for our testing, mostly because no single package worked on every GPU. For Nvidia, we opted for Automatic 1111's webui version(opens in new tab). AMD GPUs were tested using Nod.ai's Shark version(opens in new tab), while for Intel's Arc GPUs we used Stable Diffusion OpenVINO(opens in new tab). "
- Anyone here using Mac?
What are some alternatives?
llama-cpp-python - Python bindings for llama.cpp
stable-diffusion
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
stable-diffusion
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
stable-diffusion-rocm
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
rocm-examples
stable-diffusion - A latent text-to-image diffusion model