ptrblck @ptrblck_de
Deep learning and drums, @PyTorch engineer at @NVIDIA github.com/ptrblck California, USA Joined April 2014-
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Thunder is the number 5 trending repo on Github! 🤯🤯 Thunder speeds up PyTorch models up to 40% (it's early still 😅😅). Contribute and support it so we can make PyTorch models even faster! github.com/Lightning-AI/l…
What an incredible week! I’m a bit fried but totally worth it 😄 The biggest highlight was shipping Thunder, our new compiler for PyTorch github.com/Lightning-AI/l… It’s going to be fun to see it speeding up our customers’ models 🚀 and see what the community builds with it ⚡️
"State of PyTorch [S62768]" session starting in a few mins at #GTC24! nvidia.com/gtc/session-ca…
Join our growing team at @nvidia! We're hiring interns in Helsinki and Tokyo to contribute to a PyTorch compiler project. Apply through the links below, and feel free to ask for details in the comments or via DM 🇫🇮Helsinki: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx… 🇯🇵Tokyo: nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAEx…
Thanks a ton for awarding me the @PyTorch superhero award at the conference! It really made my day, and it was great seeing so many community members in real life. You all are amazing!
"DREAMPlace computes both wire length and density gradients numerically using GPU-accelerated algorithms enabled by the PyTorch framework." 💪
"DREAMPlace computes both wire length and density gradients numerically using GPU-accelerated algorithms enabled by the PyTorch framework." 💪
A lot of interesting talks you shouldn't miss!
A lot of interesting talks you shouldn't miss!
Congrats on the release! I see one missed opportunity 😅
Congrats on the release! I see one missed opportunity 😅 https://t.co/51TYaD70c5
Hahaha, let's see when ML models will ask you for a minimal and executable code snippet to reproduce the issue 😅
Hahaha, let's see when ML models will ask you for a minimal and executable code snippet to reproduce the issue 😅
Lots of love in this thread! You all are amazing and made my day! :)
Lots of love in this thread! You all are amazing and made my day! :)
If you installed PyTorch-nightly on Linux between Dec. 25 and Dec. 30, uninstall it and torchtriton immediately and use the latest nightly binaries. Read the security advisory here: pytorch.org/blog/compromis…
Haha, it seems ChatGPT is already on it. Look at this kind message I've received today ☺️:
Haha, it seems ChatGPT is already on it. Look at this kind message I've received today ☺️: https://t.co/7xJac9BZyK
If you are interested to hear more about general nvFuser features, its progress, as well as the new Python interface check the [A41255] GTC session, Tuesday, Sep 20 from 12:00 PM - 12:50 PM PDT: register.nvidia.com/flow/nvidia/gt…
If you are interested to hear more about general nvFuser features, its progress, as well as the new Python interface check the [A41255] GTC session, Tuesday, Sep 20 from 12:00 PM - 12:50 PM PDT: register.nvidia.com/flow/nvidia/gt…
If you are using @StableDiffusion and want to make it faster, check Simo's post and notebook showing how nvFuser will speed it up: reddit.com/r/MachineLearn…
🗓️Save the Date: The #PyTorchConference returns in-person on Dec. 2nd as a satellite event to @NeurIPSConf in New Orleans. This is the biggest PyTorch event of the year! Interested in contributing content? Learn more: bit.ly/3wFRSec
nvFuser blogpost released! 🥳 Check the tutorial to see how runtime generated CUDA kernels can speed up your workload: pytorch.org/tutorials/inte…
nvFuser blogpost released! 🥳 Check the tutorial to see how runtime generated CUDA kernels can speed up your workload: pytorch.org/tutorials/inte…
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@ptrblck_de @PyTorch @soumithchintala ChatGPT is probably very good in PyTorch thanks to you! :)
Similar trick comes up when implementing backprop through discrete stochastic variables return loss + (grad_obj - grad_obj.detach()) Returns the forward loss, but also attaches the grad_obj node to the its Tensor so loss.backward() pushes the grad through this extra path
This line of @PyTorch code fascinates me every time I come across it: y = x_backward + (x_forward - x_backward).detach() As @ThomasViehmann explained: "It get’s you x_forward in the forward, but the derivative will act as if you had x_backward"
Shout out to the real legend behind AGI, if it ever arrived. This guy has raised generations of devs🫡
This line of @PyTorch code fascinates me every time I come across it: y = x_backward + (x_forward - x_backward).detach() As @ThomasViehmann explained: "It get’s you x_forward in the forward, but the derivative will act as if you had x_backward"
A few new CUDA hacker friends joined the effort and now llm.c is only 2X slower than PyTorch (fp32, forward pass) compared to 4 days ago, when it was at 4.2X slower 📈 The biggest improvements were: - turn on TF32 (NVIDIA TensorFLoat-32) instead of FP32 for matmuls. This is a…
Once you have the forward/backward, the rest of it (data loader, Adam update, etc) are mostly trivial. The real fun starts now though: I am now porting this to CUDA layer by layer so that it can be made efficient, perhaps even coming within reasonable fraction of PyTorch, but…
@cloneofsimo @ptrblck_de I just thanked this guy recently because I've seen him so often when I'm looking for help, really funny to see a tweet about this
He’s real…
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
🫡
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
The legit goat
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
True legend
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
this man is a goat
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
Legend!!
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
THIS MAN COMES IN MY DREAM
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
Raise your hand if you have had your question answered by him. I start .
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
@plutoniummm_ i don't think i even had to create a pytorch discuss account because he'd preemptively provided solutions to every single obscure issue i'd run into over the course of like 1 year
this guy has single handed solved all my doubts ahead of time via clairvoyance
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
ChatGPT is no useful .... it "learned" everything from this saviour ....
Friendly reminder in case yall forgot: why is torch + cuda stack so incredibly popular and user friendly that it dominates all of ai market?
@ptrblck_de @PyTorch You 100% deserve it! Huge thanks for your contribution to the community!
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