torchdeploy

PyTorch Developer Podcast

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הוסף לפני four שנים

תוכן מסופק על ידי PyTorch, Edward Yang, and Team PyTorch. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי PyTorch, Edward Yang, and Team PyTorch או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

Full Police Interrogations, 911 Calls, and True Crime Investigations - True Crime Podcast 2025

1
The Craziest Serial Rapist In British History 200 Victims | The Case Of Reynhard Sinaga 38:44

לפני 6 weeks38:44

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38:44

The Craziest Serial Rapist In British History 200 Victims | The Case Of Reynhard Sinaga A terrible true crime story about how a quiet honors student in Indonesia became an insane maniac with over 200 victims. For more than two years he had been operating in Manchester, UK, and when he was caught, everyone was shocked! The heartbreaking case of Reynhard Sinaga! The Craziest Serial Rapist In British History | The Case Of Reynhard Sinaga | True Crime Documentary Become a supporter of this podcast: https://www.spreaker.com/podcast/full-police-interrogations-911-calls-and-true-crime-investigations-true-crime-podcast-2025--6463449/support .…

לפני 4 שנים 13:42

MP3•בית הפרקים

Further reading.

How the torchdeploy build system works https://dev-discuss.pytorch.org/t/torch-deploy-the-build/238
Description of the single interpreter per Tensor invariant https://github.com/pytorch/pytorch/issues/57756
Recent work on making it possible to load C extensions into torchdeploy https://dev-discuss.pytorch.org/t/running-multiple-python-interpreters-via-custom-dynamic-loading/241

83 פרקים

#Tech #PyTorch #Edward Yang #Team PyTorch #Deep Learning #Machine Learning

torchdeploy

PyTorch Developer Podcast

32 subscribers

published לפני 4 שנים

שתפו

MP3•בית הפרקים

Further reading.

How the torchdeploy build system works https://dev-discuss.pytorch.org/t/torch-deploy-the-build/238
Description of the single interpreter per Tensor invariant https://github.com/pytorch/pytorch/issues/57756
Recent work on making it possible to load C extensions into torchdeploy https://dev-discuss.pytorch.org/t/running-multiple-python-interpreters-via-custom-dynamic-loading/241

83 פרקים

#Tech #PyTorch #Edward Yang #Team PyTorch #Deep Learning #Machine Learning

כל הפרקים

1
Compiler collectives 16:33

לפני 46 weeks16:33

16:33

Compiler collectives are a PT2 feature where by compiler instances across multiple ranks use NCCL collectives to communicate information to other instances. This is used to ensure we consistently decide if inputs or static or dynamic across all ranks. See also PR at https://github.com/pytorch/pytorch/pull/130935…

1
TORCH_TRACE and tlparse 15:28

לפני 1 year15:28

15:28

TORCH_TRACE and tlparse are a structured log and log parser for PyTorch 2. It gives useful information about what code was compiled and what the intermediate build products look like.

1
Higher order operators 17:10

לפני 1 year17:10

17:10

Higher order operators are a special form of operators in torch.ops which have relaxed input argument requirements: in particular, they can accept any form of argument, including Python callables. Their name is based off of their most common use case, which is to represent higher order functions like control flow operators. However, they are also used to implement other variants of basic operators and can also be used to smuggle in Python data that is quite unusual. They are implemented using a Python dispatcher.…

1
Inductor - Post-grad FX passes 24:07

לפני 1 year24:07

24:07

The post-grad FX passes in Inductor run after AOTAutograd has functionalized and normalized the input program into separate forward/backward graphs. As such, they generally can assume that the graph in question is functionalized, except for some mutations to inputs at the end of the graph. At the end of post-grad passes, there are special passes that reintroduce mutation into the graph before going into the rest of Inductor lowering which is generally aware of passes. The post-grad FX passes are varied but are typically domain specific passes making local changes to specific parts of the graph.…

1
CUDA graph trees 20:50

לפני 1 year20:50

20:50

CUDA graph trees are the internal implementation of CUDA graphs used in PT2 when you say mode="reduce-overhead". Their primary innovation is that they allow the reuse of memory across multiple CUDA graphs, as long as they form a tree structure of potential paths you can go down with the CUDA graph. This greatly reduced the memory usage of CUDA graphs in PT2. There are some operational implications to using CUDA graphs which are described in the podcast.…

1
Min-cut partitioner 15:56

לפני 1 year15:56

15:56

The min-cut partitioner makes decisions about what to save for backwards when splitting the forward and backwards graph from the joint graph traced by AOTAutograd. Crucially, it doesn't actually do a "split"; instead, it is deciding how much of the joint graph should be used for backwards. I also talk about the backward retracing problem.…

1
AOTInductor 17:30

לפני 1 year17:30

17:30

AOTInductor is a feature in PyTorch that lets you export an inference model into a self-contained dynamic library, which can subsequently be loaded and used to run optimized inference. It is aimed primarily at CUDA and CPU inference applications, for situations when your model export once to be exported once while your runtime may still get continuous updates. One of the big underlying organizing principles is a limited ABI which does not include libtorch, which allows these libraries to stay stable over updates to the runtime. There are many export-like use cases you might be interested in using AOTInductor for, and some of the pieces should be useful, but AOTInductor does not necessarily solve them.…

1
Tensor subclasses and PT2 13:25

לפני 1 year13:25

13:25

Tensor subclasses allow you to add extend PyTorch with new types of tensors without having to write any C++. They have been used to implement DTensor, FP8, Nested Jagged Tensor and Complex Tensor. Recent work by Brian Hirsh means that we can compile tensor subclasses in PT2, eliminating their overhead. The basic mechanism by which this compilation works is a desugaring process in AOTAutograd. There are some complications involving views, dynamic shapes and tangent metadata mismatch.…

1
Compiled autograd 18:07

לפני 1 year18:07

18:07

Compiled autograd is an extension to PT2 that permits compiling the entirety of a backward() call in PyTorch. This allows us to fuse accumulate grad nodes as well as trace through arbitrarily complicated Python backward hooks. Compiled autograd is an important part of our plans for compiled DDP/FSDP as well as for whole-graph compilation.…

1
PT2 extension points 15:54

לפני 1 year15:54

15:54

We discuss some extension points for customizing PT2 behavior across Dynamo, AOTAutograd and Inductor.

1
Inductor - Define-by-run IR 12:06

לפני 1 year12:06

12:06

Define-by-run IR is how Inductor defines the internal compute of a pointwise/reduction operation. It is characterized by a function that calls a number of functions in the 'ops' namespace, where these ops can be overridden by different handlers depending on what kind of semantic analysis you need to do. The ops Inductor supports include regular arithmetic operators, but also memory load/store, indirect indexing, masking and collective operations like reductions.…

1
Unsigned integers 13:07

לפני 1 year13:07

13:07

Traditionally, unsigned integer support in PyTorch was not great; we only support uint8. Recently, we added support for uint16, uint32 and uint64. Bare bones functionality works, but I'm entreating the community to help us build out the rest. In particular, for most operations, we plan to use PT2 to build anything else. But if you have an eager kernel you really need, send us a PR and we'll put it in. While most of the implementation was straightforward, there are some weirdnesses related to type promotion inconsistencies with numpy and dealing with the upper range of uint64. There is also upcoming support for sub-byte dtypes uint1-7, and these will exclusively be implemented via PT2.…

1
Inductor - IR 18:00

לפני 1 year18:00

18:00

Inductor IR is an intermediate representation that lives between ATen FX graphs and the final Triton code generated by Inductor. It was designed to faithfully represent PyTorch semantics and accordingly models views, mutation and striding. When you write a lowering from ATen operators to Inductor IR, you get a TensorBox for each Tensor argument which contains a reference to the underlying IR (via StorageBox, and then a Buffer/ComputedBuffer) that says how the Tensor was computed. The inner computation is represented via define-by-run, which allows for compact definition of IR representation, while still allowing you to extract an FX graph out if you desire. Scheduling then takes buffers of inductor IR and decides what can be fused. Inductor IR may have too many nodes, this would be a good thing to refactor in the future.…

לפני 4 years13:17

13:17

In this episode, we will discuss how to bind a C++ object in Python? We'll try to answer the following questions: How does pybind11 do it? What's different about how we implement it for Tensor? What are some downsides of the approach? Note from the future: I recorded and then decided I didn't like my follow up episode about how to preserve PyObjects even when they go dead in Python. Maybe some day! Further reading: Python bindings for Tensor in PyTorch https://github.com/.../csrc/autograd/python_variable.cpp pybind11 hash map for maintaining object identity https://github.com/.../inc.../pybind11/detail/internals.h... Tensor subclasses don't save their properties https://github.com/pytorch/pytorch/issues/47117 (but the situation here is more complicated than I imply in the podcast)…

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