PyTorch Developer Podcast
81 episodes - English - Latest episode: 4 days ago - ★★★★★ - 35 ratingsThe PyTorch Developer Podcast is a place for the PyTorch dev team to do bite sized (10-20 min) topics about all sorts of internal development topics in PyTorch.
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Episodes
Higher order operators
April 21, 2024 19:28 - 17 minutes - 15.7 MBHigher 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...
Inductor - Post-grad FX passes
April 12, 2024 07:00 - 24 minutes - 22.1 MBThe 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 ...
CUDA graph trees
March 24, 2024 07:00 - 20 minutes - 19.1 MBCUDA 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.
Min-cut partitioner
March 17, 2024 07:00 - 15 minutes - 14.6 MBThe 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.
AOTInductor
March 02, 2024 08:00 - 17 minutes - 16 MBAOTInductor 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...
Tensor subclasses and PT2
February 24, 2024 08:00 - 13 minutes - 12.3 MBTensor 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.
Compiled autograd
February 19, 2024 08:00 - 18 minutes - 16.6 MBCompiled 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.
PT2 extension points
February 05, 2024 09:00 - 15 minutes - 14.6 MBWe discuss some extension points for customizing PT2 behavior across Dynamo, AOTAutograd and Inductor.
Inductor - Define-by-run IR
January 24, 2024 08:00 - 12 minutes - 11.1 MBDefine-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.
Unsigned integers
January 17, 2024 14:00 - 13 minutes - 12 MBTraditionally, 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 ...
Inductor - IR
January 16, 2024 09:00 - 18 minutes - 16.5 MBInductor 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 c...
Dynamo - VariableTracker
January 12, 2024 17:40 - 15 minutes - 14.6 MBI talk about VariableTracker in Dynamo. VariableTracker is Dynamo's representation of the Python. I talk about some recent changes, namely eager guards and mutable VT. I also tell you how to find the functionality you care about in VariableTracker (https://docs.google.com/document/d/1XDPNK3iNNShg07jRXDOrMk2V_i66u1hEbPltcsxE-3E/edit#heading=h.i6v7gqw5byv6).
Unbacked SymInts
February 21, 2023 08:00 - 21 minutes - 19.7 MBThis podcast goes over the basics of unbacked SymInts. You might want to listen to this one before listening to https://pytorch-dev-podcast.simplecast.com/episodes/zero-one-specialization Some questions we answer (h/t from Gregory Chanan): - Are unbacked symints only for export? Because otherwise I could just break / wait for the actual size. But maybe I can save some retracing / graph breaks perf if I have them too? So the correct statement is "primarily" for export? - Why am I look...
Zero-one specialization
February 20, 2023 08:00 - 21 minutes - 19.3 MBMikey Dagistes joins me to ask some questions about the recent recent composability sync https://www.youtube.com/watch?v=NJV7YFbtoR4 where we discussed 0/1 specialization and its implications on export in PT2. What's the fuss all about? What do I need to understand about PT2 to understand why 0/1 specialization is a thing?
torchdynamo
December 06, 2022 08:00 - 25 minutes - 23.4 MBWhat is torchdynamo? From a bird's eye view, what exactly does it do? What are some important things to know about it? How does it differ from other graph capture mechanisms? For more reading, check out https://docs.google.com/document/d/13K03JN4gkbr40UMiW4nbZYtsw8NngQwrTRnL3knetGM/edit#
PyTorch 2.0
December 04, 2022 08:00 - 17 minutes - 16.3 MBSoumith's keynote on PT2.0: https://youtu.be/vbtGZL7IrAw?t=1037 PT2 Manifesto: https://docs.google.com/document/d/1tlgPcR2YmC3PcQuYDPUORFmEaBPQEmo8dsh4eUjnlyI/edit# PT2 Architecture: https://docs.google.com/document/d/1wpv8D2iwGkKjWyKof9gFdTf8ISszKbq1tsMVm-3hSuU/edit#
History of functorch
November 07, 2022 23:14 - 19 minutes - 17.6 MBJoin me with Richard Zou to talk about the history of functorch. What was the thought process behind the creation of functorch? How did it get started? JAX’s API and model is fairly different from PyTorch’s, how did we validate that it would work in PyTorch? Where did functorch go after the early user studies? Where is it going next?
Learning rate schedulers
June 13, 2022 16:00 - 19 minutes - 17.9 MBWhat’s a learning rate? Why might you want to schedule it? How does the LR scheduler API in PyTorch work? What the heck is up with the formula implementation? Why is everything terrible?
Weak references
June 06, 2022 16:00 - 16 minutes - 15.4 MBWhat are they good for? (Caches. Private fields.) C++ side support, how it’s implemented / release resources. Python side support, how it’s implemented. Weak ref tensor hazard due to resurrection. Downsides of weak references in C++. Scott Wolchok’s release resources optimization. Other episodes to listen to first: https://pytorch-dev-podcast.simplecast.com/episodes/reference-counting https://pytorch-dev-podcast.simplecast.com/episodes/pyobject-preservation
Strides
May 30, 2022 16:00 - 20 minutes - 18.8 MBMike Ruberry has an RFC about stride-agnostic operator semantics (https://github.com/pytorch/pytorch/issues/78050), so let's talk about strides. What are they? How are they used to implement views and memory format? How do you handle them properly when writing kernels? In what sense are strides overspecified, and therefore, not worth slavishly reimplementing in a system like PrimTorch? What does Edward think we should do about them? My blog post that covers strides along with other topics c...
AOTAutograd
May 09, 2022 16:00 - 19 minutes - 17.6 MBAOTAutograd is a cool new feature in functorch for capturing both forward and backward traces of PyTorch operators, letting you run them through a compiler and then drop the compiled kernels back into a normal PyTorch eager program. Today, Horace joins me to tell me how it works, what it is good to use for, and what our future plans for it are.
Dispatcher questions with Sherlock
May 02, 2022 16:00 - 18 minutes - 17 MBSherlock recently joined the PyTorch team, having previously worked on ONNX Runtime at Microsoft, and Sherlock’s going to ask me some questions about the dispatcher, and I’m going to answer them. We talked about the history of the dispatcher, how to override dispatching order, multiple dispatch, how to organize various dispatch keys and torch function mode. The companion video is at https://youtu.be/_qB2Ho1O3u4
New CI
April 25, 2022 16:00 - 16 minutes - 14.9 MBPyTorch recently moved all of its CI from CircleCI to GitHub Actions. There were a lot of improvements in the process, making my old podcast about CI obsolete! Today, Eli Uriegas joins me to talk about why we moved to GitHub Actions, how the new CI system is put together, and what some cool features about our new CI.
Python exceptions
April 17, 2022 16:00 - 14 minutes - 12.2 MBC++ has exceptions, Python has exceptions. But they’re not the same thing! How do exceptions work in CPython, how do we translate exceptions from C++ to Python (hint: it’s different for direct bindings versus pybind11), and what do warnings (which we also translate from C++ to Python) have in common with this infrastructure?
Torch vs ATen APIs
April 11, 2022 16:00 - 15 minutes - 13.8 MBPyTorch’s torch API is the Python API everyone knows and loves, but there’s also another API, the ATen API, which most of PyTorch’s internal subsystems are built on. How to tell them apart? What implications do these have on our graph mode IR design? Also, a plug for PrimTorch, a new set of operators, not designed for eager mode, that is supposed to be even lower level than ATen.
All about NVIDIA GPUs
September 24, 2021 16:00 - 19 minutes - 17.8 MBPyTorch is in the business of shipping numerical software that can run fast on your CUDA-enabled NVIDIA GPU, but it turns out there is a lot of heterogeneity in NVIDIA’s physical GPU offering and when it comes to what is fast and what is slow, what specific GPU you have on hand matters quite a bit. Yet there are literally hundreds of distinct NVIDIA GPU models on the market, how do you make sense of the madness? Today, Natalia Gimelshein joins me to talk about everything that’s going on in t...
Tensor subclasses and Liskov substitution principle
September 16, 2021 13:00 - 19 minutes - 17.6 MBA lot of recent work going in PyTorch is all about adding new and interesting Tensor subclasses, and this all leads up to the question of, what exactly is OK to make a tensor subclass? One answer to this question comes from an old principle from Barbara Liskov called the Liskov substitution principle, which informally can be stated as S is a subtype of T if anywhere you have T, it can be replaced with S without altering "desirable" properties of this program. In this podcast I'll talk about ...
Half precision
September 10, 2021 13:00 - 18 minutes - 16.5 MBIn this episode I talk about reduced precision floating point formats float16 (aka half precision) and bfloat16. I'll discuss what floating point numbers are, how these two formats vary, and some of the practical considerations that arise when you are working with numeric code in PyTorch that also needs to work in reduced precision. Did you know that we do all CUDA computations in float32, even if the source tensors are stored as float16? Now you know! Further reading. The Wikipedia articl...
DataLoader with multiple workers leaks memory
September 01, 2021 13:00 - 16 minutes - 15.2 MBToday I'm going to talk about a famous issue in PyTorch, DataLoader with num_workers > 0 causes memory leak (https://github.com/pytorch/pytorch/issues/13246). This bug is a good opportunity to talk about DataSet/DataLoader design in PyTorch, fork and copy-on-write memory in Linux and Python reference counting; you have to know about all of these things to understand why this bug occurs, but once you do, it also explains why the workarounds help. Further reading. A nice summary of the full ...
Batching
August 18, 2021 13:00 - 13 minutes - 12.5 MBPyTorch operates on its input data in a batched manner, typically processing multiple batches of an input at once (rather than once at a time, as would be the case in typical programming). In this podcast, we talk a little about the implications of batching operations in this way, and then also about how PyTorch's API is structured for batching (hint: poorly) and how Numpy introduced a concept of ufunc/gufuncs to standardize over broadcasting and batching behavior. There is some overlap betw...
Multiple dispatch in __torch_function__
August 10, 2021 13:00 - 14 minutes - 13.1 MBPython is a single dispatch OO language, but there are some operations such as binary magic methods which implement a simple form of multiple dispatch. torch_function__ (through its Numpy predecessor __array_function) generalizes this mechanism so that invocations of torch.add with different subclasses work properly. This podcast describes how this mechanism works and how it can be used (in an unconventional way) to build composable subclasses ala JAX in functorch. Further reading: This po...
Multithreading
August 03, 2021 13:00 - 18 minutes - 17 MBWriting multithreading code has always been a pain, and in PyTorch there are buckets and buckets of multithreading related issues you have to be aware about and deal with when writing code that makes use of it. We'll cover how you interface with multithreading in PyTorch, what goes into implementing those interfaces (thread pools!) and also some miscellaneous stuff like TLS, forks and data structure thread safety that is also relevant. Further reading: TorchScript CPU inference threading d...
Asynchronous versus synchronous execution
July 27, 2021 13:00 - 15 minutes - 13.8 MBCUDA is asynchronous, CPU is synchronous. Making them play well together can be one of the more thorny and easy to get wrong aspects of the PyTorch API. I talk about why non_blocking is difficult to use correctly, a hypothetical "asynchronous CPU" device which would help smooth over some of the API problems and also why it used to be difficult to implement async CPU (but it's not hard anymore!) At the end, I also briefly talk about how async/sync impedance can also show up in unusual places,...
gradcheck
July 23, 2021 13:00 - 16 minutes - 15.5 MBWe talk about gradcheck, the property based testing mechanism that we use to verify the correctness of analytic gradient formulas in PyTorch. I'll talk a bit about testing in general, property based testing and why gradcheck is a particularly useful property based test. There will be some calculus, although I've tried to keep the math mostly to intuitions and pointers on what to read up on elsewhere. Further reading. Gradcheck mechanics, a detailed mathematical explanation of how it works ...
torch.use_deterministic_algorithms
July 21, 2021 13:00 - 10 minutes - 9.92 MBtorch.use_deterministic_algorithms lets you force PyTorch to use deterministic algorithms. It's very useful for debugging! There are some errors in the recording: the feature is called torch.use_deterministic_algorithms, and there is not actually a capability to warn (this was in an old version of the PR but taken out), we just error if you hit nondeterministic code. Docs: https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms
Reference counting
July 20, 2021 13:00 - 15 minutes - 13.9 MBReference counting is a common memory management technique in C++ but PyTorch does its reference counting in a slightly idiosyncratic way using intrusive_ptr. We'll talk about why intrusive_ptr exists, the reason why refcount bumps are slow in C++ (but not in Python), what's up with const Tensor& everywhere, why the const is a lie and how TensorRef lets you create a const Tensor& from a TensorImpl* without needing to bump your reference count. Further reading. Why you shouldn't feel bad ab...
Memory layout
July 13, 2021 13:00 - 16 minutes - 15 MBMemory layout specifies how the logical multi-dimensional tensor maps its elements onto physical linear memory. Some layouts admit more efficient implementations, e.g., NCHW versus NHWC. Memory layout makes use of striding to allow users to conveniently represent their tensors with different physical layouts without having to explicitly tell every operator what to do. Further reading. Tutorial https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html Memory format RFC https:/...
pytorch-probot
July 12, 2021 14:28 - 13 minutes - 12 MBpytorch-probot is a GitHub application that we use to automate common tasks in GitHub. I talk about what it does and some design philosophy for it. Repo is at: https://github.com/pytorch/pytorch-probot
API design via lexical and dynamic scoping
July 09, 2021 13:00 - 21 minutes - 19.9 MBLexical and dynamic scoping are useful tools to reason about various API design choices in PyTorch, related to context managers, global flags, dynamic dispatch, and how to deal with BC-breaking changes. I'll walk through three case studies, one from Python itself (changing the meaning of division to true division), and two from PyTorch (device context managers, and torch function for factory functions). Further reading. Me unsuccessfully asking around if there was a way to simulate __futur...
Intro to distributed
July 08, 2021 13:00 - 15 minutes - 14.4 MBToday, Shen Li (mrshenli) joins me to talk about distributed computation in PyTorch. What is distributed? What kinds of things go into making distributed work in PyTorch? What's up with all of the optimizations people want to do here? Further reading. PyTorch distributed overview https://pytorch.org/tutorials/beginner/dist_overview.html Distributed data parallel https://pytorch.org/docs/stable/notes/ddp.html
Double backwards
July 07, 2021 13:00 - 16 minutes - 15.2 MBDouble backwards is PyTorch's way of implementing higher order differentiation. Why might you want it? How does it work? What are some of the weird things that happen when you do this? Further reading. Epic PR that added double backwards support for convolution initially https://github.com/pytorch/pytorch/pull/1643
Functional modules
July 06, 2021 13:00 - 14 minutes - 13.3 MBFunctional modules are a proposed mechanism to take PyTorch's existing NN module API and transform it into a functional form, where all the parameters are explicit argument. Why would you want to do this? What does functorch have to do with it? How come PyTorch's existing APIs don't seem to need this? What are the design problems? Further reading. Proposal in GitHub issues https://github.com/pytorch/pytorch/issues/49171 Linen design in flax https://flax.readthedocs.io/en/latest/design_no...
CUDA graphs
June 28, 2021 13:00 - 13 minutes - 12.7 MBWhat are CUDA graphs? How are they implemented? What does it take to actually use them in PyTorch? Further reading. NVIDIA has docs on CUDA graphs https://developer.nvidia.com/blog/cuda-graphs/ Nuts and bolts implementation PRs from mcarilli: https://github.com/pytorch/pytorch/pull/51436 https://github.com/pytorch/pytorch/pull/46148
Default arguments
June 25, 2021 13:00 - 14 minutes - 13.7 MBWhat do default arguments have to do with PyTorch design? Why are default arguments great for clients (call sites) but not for servers (implementation sites)? In what sense are default arguments a canonicalization to max arity? What problems does this canonicalization cause? Can you canonicalize to minimum arity? What are some lessons to take? Further reading. https://github.com/pytorch/pytorch/issues/54613 stop serializing default arguments
Anatomy of a domain library
June 24, 2021 13:00 - 16 minutes - 14.8 MBWhat's a domain library? Why do they exist? What do they do for you? What should you know about developing in PyTorch main library versus in a domain library? How coupled are they with PyTorch as a whole? What's cool about working on domain libraries? Further reading. The classic trio of domain libraries is https://pytorch.org/audio/stable/index.html https://pytorch.org/text/stable/index.html and https://pytorch.org/vision/stable/index.html Line notes. why do domain libraries exist? lot...
TensorAccessor
June 23, 2021 13:00 - 11 minutes - 10.7 MBWhat's TensorAccessor? Why not just use a raw pointer? What's PackedTensorAccessor? What are some future directions for mixing statically typed and typed erase code inside PyTorch proper? Further reading. TensorAccessor source code, short and sweet https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/core/TensorAccessor.h Legacy THCDeviceTensor https://github.com/pytorch/pytorch/blob/master/aten/src/THC/THCDeviceTensor.cuh
Random number generators
June 22, 2021 13:00 - 14 minutes - 13.2 MBWhy are RNGs important? What is the generator concept? How do PyTorch's CPU and CUDA RNGs differ? What are some of the reasons why Philox is a good RNG for CUDA? Why doesn't the generator class have virtual methods for getting random numbers? What's with the next normal double and what does it have to do with Box Muller transform? What's up with csprng? Further reading. CUDAGeneratorImpl has good notes about CUDA graph interaction and pointers to all of the rest of the stuff https://github...
vmap
June 21, 2021 13:00 - 17 minutes - 16.3 MBWhat is vmap? How is it implemented? How does our implementation compare to JAX's? What is a good way of understanding what vmap does? What's up with random numbers? Why are there some issues with the vmap that PyTorch currently ships? Further reading. Tracking issue for vmap support https://github.com/pytorch/pytorch/issues/42368 BatchedTensor source code https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/BatchedTensorImpl.h , logical-physical transformation helper code https:/...
Expect tests
June 18, 2021 13:00 - 13 minutes - 12.3 MBWhat's an expect test? Why should you use them? Why is inline expect test better than out of line? How to write a good expect test? Further reading. expecttest source implementation https://github.com/pytorch/pytorch/blob/master/torch/testing/_internal/expecttest.py (only 311 lines!)
XLA
June 17, 2021 13:00 - 15 minutes - 14.6 MBWhat's PyTorch XLA? Why should you care? How is it implemented? How does PyTorch XLA trade off functionality versus ease of performance debugging? What are some new developments in this space? Further reading. XLA's repo has lots of really good docs. Check out https://github.com/pytorch/xla/blob/master/OP_LOWERING_GUIDE.md and also the main https://github.com/pytorch/xla/blob/master/README.md Alex Suhan's RFC about lazy core https://github.com/pytorch/rfcs/pull/18