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16 Nov, 10 tweets, 11 min read
Get ready for PyTorch Developer Day on December 1-2, 2021! We’ve got an amazing lineup of speakers for you on Day 1.

And don’t forget to register for #PTD2 Day 2 taking place on Gather.Town: pytorchdeveloperday.fbreg.com/apply

Check the thread below to see the speakers ⬇️
1/10
🎙Keynote Speakers🎙
1. Lin Qiao - Engineering Director @MetaAI
2. @DougalMaclaurin - Sr. Research Scientist @Google
3. Philippe Tillet - Member of Technical Staff @OpenAI
4. @dwarak - Engineering Director @MetaAI
5. @dzhulgakov - Software Engineer @MetaAI

2/10
🎙Research🎙
1. Vitaly Fedyunin - Software Engineer @MetaAI
2. @mikeruberry - Software Engineer @MetaAI
3. Richard Zou + @cHHillee - Software Engineers @MetaAI
4. Yanli Zhao - Software Engineer @MetaAI

cont'd below

3/10
5. Nicholas Joseph - Member of Technical Staff @AnthropicAI
6. @fvsmassa - Research Engineer @MetaAI
7. Moto Hira - Software Engineer @MetaAI
8. @ButterKaffee - Next Generation Technologies Sr. Leader @MarsGlobal

4/10
🎙Performance & Profiling🎙
1. Sabrina Smai - Program Manager @Microsoft
2. Michael Carilli - Senior DL Framework Engineer @nvidia
3. Victor Bittorf - Applied Research Scientist @MetaAI

5/10
🎙Production & Mobile🎙
1. @jamesr66a - Software Engineer @MetaAI
2. @suospeaks - Software Engineer @MetaAI
3. Kiuk Chung - Software Engineer @MetaAI
4. @austinvhuang - Vice President, AI & Machine Learning @Fidelity

cont'd below

6/10
5. @geeta4c - Manager, Partner Engineering, AI @MetaAI
6. @razielag + @raedle - Software Engineers @MetaAI
7. @txhf - Software Engineer @MetaAI
8. Christian Keller (Product Manager) + Linbin Yu (Product Manager) - @MetaAI

7/10
🎙Privacy & Explainability🎙
1. @alexsablay (Research Scientist) + Igor Shilov (Software Engineer) - @MetaAI
2. Narine Kokhlikyan - Research Scientist, Artificial Intelligence @MetaAI

8/10
🎙Fireside Chat and Closing🎙
1. @ylecun (VP & Chief AI Scientist) + @soumithchintala (Software Engineer) @MetaAI

9/10
We'll be livestreaming right here on our Twitter channel starting at 7AM PST on December 1st!

Stay tuned for more details.

10/10

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More from @PyTorch

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25 Oct
ICYMI: PyTorch 1.10 was released last Thursday. Here are some highlights of the release.

Stay tuned for tweet threads in the next couple weeks delving deeper into these cool new features!

1/8
CUDA Graphs are now in beta, and allow you to capture (and replay!) static CUDA workloads without needing to relaunch kernels, leading to massive overhead reductions! Our integration allows for seamless interop between CUDA graphs and the rest of your model.

2/9
FX, an easy to use Python platform for writing Python-to-Python transforms of PyTorch programs, is now in stable. FX makes it easy to programmatically do things like fusing convolution w/ batch norm. Stay tuned for some FX examples of cool things that users have built!

3/9
Read 10 tweets
PyTorch Profile picture
19 Oct
✨ Low Numerical Precision in PyTorch ✨
Most DL models are single-precision floats by default.
Lower numerical precision - while reasonably maintaining accuracy - reduces:

a) model size
b) memory required
c) power consumed

Thread about lower precision DL in PyTorch ->
1/11
Lower precision speeds up :

* compute-bound operations, by reducing load on the hardware

* memory bandwidth-bound operations, by accessing smaller data

In many deep models, memory access dominates power consumption; reducing memory I/O makes models more energy efficient.

2/11
3 lower precision datatypes are typically used in PyTorch:

* FP16 or half-precision (`torch. float16`)

* BF16 (`torch. bfloat16`)

* INT8 (`torch.quint8` and `torch. qint8`) which stores floats in a quantized format

3/11
Read 11 tweets
PyTorch Profile picture
14 Sep
Want to make your inference code in PyTorch run faster? Here’s a quick thread on doing exactly that.

1. Replace torch.no_grad() with the ✨torch.inference_mode()✨ context manager.
2. ⏩ inference_mode() is torch.no_grad() on steroids

While NoGrad excludes operations from being tracked by Autograd, InferenceMode takes that two steps ahead, potentially speeding up your code (YMMV depending on model complexity and hardware)
3. ⏩ InferenceMode reduces overheads by disabling two Autograd mechanisms - version counting and metadata tracking - on all tensors created here ("inference tensors").

Disabled mechanisms mean inference tensors have some restrictions in how they can be used 👇
Read 7 tweets

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