History and constraints of Tensor

PyTorch Developer Podcast

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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.

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Close Encounters with UFO Hot Spots: Area 51, Roswell, and the Great ET Road Trip 39:50

לפני 17 ימים39:50

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39:50

The truth is out West! We’re hopping on the ET Highway and venturing to the most notorious alien hot spots, including Roswell’s infamous crash site, Area 51’s eerie perimeter, and a mysterious desert watchtower. Join us as journalist Laura Krantz, host of the podcast Wild Thing , beams up to share stories from the front lines of UFO reporting—from strange sightings and quirky festivals to a mailbox where people leave letters to extraterrestrials. Maybe you’ll even decide for yourself: Is Earth a tourist stop for spaceships? UFO hot spots you’ll encounter in this episode: - UFO Watchtower (near Great Sand Dunes National Park, Colorado) - Roswell, New Mexico - Area 51, Nevada - Extraterrestrial Highway (aka State Route 375), Nevada - Little A’Le’Inn, ET Highway, Nevada - E.T. Fresh Jerky, ET Highway, Nevada - Alien Research Center, ET Highway, Nevada - The Black Mailbox, ET Highway, Nevada Via Podcast is a production of AAA Mountain West Group .…

לפני 4 שנים 14:48

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What historical constraints and design choices lead to the design of Tensor/Storage (and their Impl variants) as they are today? Why do we use intrusive refcounting? Why are we trying to get rid of virtual methods on TensorImpl? Why are there so many frickin' bitfields?

Further reading.

PyTorch internals blog post http://blog.ezyang.com/2019/05/pytorch-internals/
Writing Python in C++, a manifesto https://github.com/pytorch/pytorch/wiki/Writing-Python-in-cpp-(a-manifesto)
At time of writing, the breakdown of all fields on TensorImpl https://github.com/pytorch/pytorch/blob/71f4c5c1f436258adc303b710efb3f41b2d50c4e/c10/core/TensorImpl.h#L2155-L2177

83 פרקים

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

History and constraints of Tensor

PyTorch Developer Podcast

32 subscribers

published לפני 4 שנים

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MP3•בית הפרקים

Further reading.

PyTorch internals blog post http://blog.ezyang.com/2019/05/pytorch-internals/
Writing Python in C++, a manifesto https://github.com/pytorch/pytorch/wiki/Writing-Python-in-cpp-(a-manifesto)
At time of writing, the breakdown of all fields on TensorImpl https://github.com/pytorch/pytorch/blob/71f4c5c1f436258adc303b710efb3f41b2d50c4e/c10/core/TensorImpl.h#L2155-L2177

83 פרקים

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

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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…

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PyTorch Developer Podcast

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PyTorch Developer Podcast

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PyTorch Developer Podcast

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PyTorch Developer Podcast

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Weak references 16:46

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Strides 20:31

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Mike 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 can be found at http://blog.ezyang.com/2019/05/pytorch-internals/…

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AOTAutograd 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.…

PyTorch Developer Podcast

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Dispatcher questions with Sherlock 18:36

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18:36

Sherlock 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…

PyTorch Developer Podcast

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New CI 16:12

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PyTorch Developer Podcast

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Python exceptions 14:47

לפני 3 years14:47

14:47

C++ 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?…

PyTorch Developer Podcast

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Torch vs ATen APIs 15:03

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15:03

PyTorch’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.…

PyTorch Developer Podcast

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All about NVIDIA GPUs 19:29

לפני 4 years19:29

19:29

PyTorch 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 the NVIDIA GPU market, and what, as a framework developer, you have to care about to make sense of it all. Further reading. NVIDIA microarchitectures on Wikipedia https://en.wikipedia.org/wiki/Category:Nvidia_microarchitectures A slightly old post about matching SM to architecture https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/…

PyTorch Developer Podcast

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Tensor subclasses and Liskov substitution principle 19:13

לפני 4 years19:13

19:13

A 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 LSP and how it relates to the design of Tensor subclasses and a hypothetical "abstract Tensor specification" which really doesn't exist but which sort of implicitly exists in the corpus of existing PyTorch programs. Further reading: This is a cool interview with Barbara Liskov that I quote in the podcast https://www.youtube.com/watch?v=-Z-17h3jG0A Max Balandat talking about linear operators in PyTorch https://github.com/pytorch/pytorch/issues/28341 At the end I talk a little bit about multiple dispatch; an earlier discussion about this topic is in this podcast https://pytorch-dev-podcast.simplecast.com/episodes/multiple-dispatch-in-torch-function…

PyTorch Developer Podcast

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Half precision 18:00

לפני 4 years18:00

18:00

In 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 article on IEEE floating point is pretty great https://en.wikipedia.org/wiki/IEEE_754 How bfloat16 works out when doing training https://arxiv.org/abs/1905.12322 Definition of acc_type in PyTorch https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/AccumulateType.h…

PyTorch Developer Podcast

1
DataLoader with multiple workers leaks memory 16:38

לפני 4 years16:38

16:38

Today 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 issue https://github.com/pytorch/pytorch/issues/13246#issuecomment-905703662 DataLoader architecture RFC https://github.com/pytorch/pytorch/issues/49440 Cinder Python https://github.com/facebookincubator/cinder…

PyTorch Developer Podcast

1
Batching 13:37

לפני 4 years13:37

13:37

PyTorch 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 between this podcast and previous podcasts about TensorIterator and vmap; you may also be interested in those episodes. Further reading. ufuncs and gufuncs https://numpy.org/doc/stable/reference/ufuncs.html and https://numpy.org/doc/stable/reference/c-api/generalized-ufuncs.html A brief taxonomy of PyTorch operators by shape behavior http://blog.ezyang.com/2020/05/a-brief-taxonomy-of-pytorch-operators-by-shape-behavior/ Related episodes on TensorIterator and vmap https://pytorch-dev-podcast.simplecast.com/episodes/tensoriterator and https://pytorch-dev-podcast.simplecast.com/episodes/vmap…

PyTorch Developer Podcast

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Multiple dispatch in __torch_function__ 14:20

לפני 4 years14:20

14:20

Python 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 podcast in written form https://dev-discuss.pytorch.org/t/functorch-levels-as-dynamically-allocated-classes/294 Multiple dispatch resolution rules in the RFC https://github.com/pytorch/rfcs/blob/master/RFC-0001-torch-function-for-methods.md#process-followed-during-a-functionmethod-call…

PyTorch Developer Podcast

1
Multithreading 18:34

לפני 4 years18:34

18:34

Writing 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 documentation https://github.com/pytorch/pytorch/blob/master/docs/source/notes/cpu_threading_torchscript_inference.rst c10 thread pool https://github.com/pytorch/pytorch/blob/master/c10/core/thread_pool.h and autograd thread pool https://github.com/pytorch/pytorch/blob/master/torch/csrc/autograd/engine.cpp Tracking issue for TLS propagation across threads https://github.com/pytorch/pytorch/issues/28520…

PyTorch Developer Podcast

1
Asynchronous versus synchronous execution 15:03

לפני 4 years15:03

15:03

CUDA 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, namely the CUDA caching allocator. Further reading. CUDA semantics which discuss non_blocking somewhat https://pytorch.org/docs/stable/notes/cuda.html Issue requesting async cpu https://github.com/pytorch/pytorch/issues/44343…

PyTorch Developer Podcast

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gradcheck 16:58

לפני 4 years16:58

16:58

We 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 https://pytorch.org/docs/stable/notes/gradcheck.html In particular, it also explains how gradcheck extends to complex numbers JAX has a pretty good explanation about vjp and jvp at https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html Fast gradcheck tracking issue https://github.com/pytorch/pytorch/issues/53876…

PyTorch Developer Podcast

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torch.use_deterministic_algorithms 10:50

לפני 4 years10:50

10:50

torch.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…

PyTorch Developer Podcast

1
Reference counting 15:14

לפני 4 years15:14

15:14

Reference 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 about passing tensor by reference https://dev-discuss.pytorch.org/t/we-shouldnt-feel-bad-about-passing-tensor-by-reference/85 Const correctness in PyTorch https://github.com/zdevito/ATen/issues/27 TensorRef RFC https://github.com/pytorch/rfcs/pull/16…

PyTorch Developer Podcast

1
Memory layout 16:26

לפני 4 years16:26

16:26

Memory 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://github.com/pytorch/pytorch/issues/19092 Layout permutation proposal (not implemented) https://github.com/pytorch/pytorch/issues/32078…

PyTorch Developer Podcast

1
pytorch-probot 13:06

לפני 4 years13:06

13:06

pytorch-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

PyTorch Developer Podcast

1
API design via lexical and dynamic scoping 21:44

לפני 4 years21:44

21:44

Lexical 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 __future__ in libraries https://stackoverflow.com/questions/66927362/way-to-opt-into-bc-breaking-changes-on-methods-within-a-single-module A very old issue asking for a way to change the default GPU device https://github.com/pytorch/pytorch/issues/260 and a global GPU flag https://github.com/pytorch/pytorch/issues/7535 A more modern issue based off the lexical module idea https://github.com/pytorch/pytorch/issues/27878 Array module NEP https://numpy.org/neps/nep-0037-array-module.html…

PyTorch Developer Podcast

1
Intro to distributed 15:41

לפני 4 years15:41

15:41

Today, 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…

PyTorch Developer Podcast

1
Double backwards 16:39

לפני 4 years16:39

16:39

Double 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…

PyTorch Developer Podcast

1
Functional modules 14:34

לפני 4 years14:34

14:34

Functional 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_notes/linen_design_principles.html…

PyTorch Developer Podcast

1
CUDA graphs 13:55

לפני 4 years13:55

13:55

What 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…

PyTorch Developer Podcast

1
Default arguments 14:57

לפני 4 years14:57

14:57

What 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…

PyTorch Developer Podcast

1
Anatomy of a domain library 16:11

לפני 4 years16:11

16:11

What'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? lots of domains specific gadgets, inappropriate for PyTorch what does a domain library do operator implementations (old days: pure python, not anymore) with autograd support and cuda acceleration esp encoding/decoding, e.g., for domain file formats torchbind for custom objects takes care of getting the dependencies for you esp transformations, e.g., for data augmentation models, esp pretrained weights datasets reference scripts full wheel/conda packaging like pytorch mobile compatibility separate repos: external contributors with direct access manual sync to fbcode; a lot easier to land code! less motion so lower risk coupling with pytorch? CI typically runs on nightlies pytorch itself tests against torchvision, canary against extensibility mechanisms mostly not using internal tools (e.g., TensorIterator), too unstable (this would be good to fix) closer to research side of pytorch; francesco also part of papers…

PyTorch Developer Podcast

1
TensorAccessor 11:40

לפני 4 years11:40

11:40

What'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…

PyTorch Developer Podcast

1
Random number generators 14:24

לפני 4 years14:24

14:24

Why 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.com/pytorch/pytorch/blob/1dee99c973fda55e1e9cac3d50b4d4982b6c6c26/aten/src/ATen/CUDAGeneratorImpl.h Transform uniformly distributed random numbers to other distributions with https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/core/TransformationHelper.h torchcsprng https://github.com/pytorch/csprng…

PyTorch Developer Podcast

1
vmap 17:47

לפני 4 years17:47

17:47

What 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://github.com/pytorch/pytorch/blob/master/aten/src/ATen/VmapTransforms.h (well documented, worth a read) functorch, the better, more JAX-y implementation of vmap https://github.com/facebookresearch/functorch Autodidax https://jax.readthedocs.io/en/latest/autodidax.html which contains a super simple vmap implementation that is a good model for the internal implementation that PyTorch has…

PyTorch Developer Podcast

1
Expect tests 13:26

לפני 4 years13:26

13:26

What'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!)

PyTorch Developer Podcast

1
XLA 15:59

לפני 4 years15:59

15:59

What'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…

PyTorch Developer Podcast

1
TH 11:09

לפני 4 years11:09

11:09

What is TH? Why might you care? What is so horrible about it? What the heck is the generic/ folder? Why are we porting everything to C++? What are some downsides of having ported all our TH code to C++? Further reading. The TH to ATen porting guide has lots of explanations of old school TH idioms https://github.com/pytorch/pytorch/wiki/TH-to-ATen-porting-guide Old notes about refcounting in TH https://github.com/pytorch/pytorch/blob/master/aten/src/README.md…

PyTorch Developer Podcast

1
TorchScript 19:56

לפני 4 years19:56

19:56

There is a really good TorchScript overview at https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md and in this 20min podcast, I want to give you some of the highlights from this document.

PyTorch Developer Podcast

1
CMake 17:49

לפני 4 years17:49

17:49

Why is PyTorch's build so g-dang complicated. How to avoid having to deal with cmake at all? And if you have to deal with cmake, what are the most important things to know? And if you were going to improve our cmake, how would you go about doing it... Further reading. The official CMake documentation is a great help and well worth reading https://cmake.org/documentation If you work in torch/csrc chances are you'll need to edit this file https://github.com/pytorch/pytorch/blob/master/tools/build_variables.bzl Liner notes. multiple build systems: cmake, buck, xplat buck, ovrsource buck, bazel tools/build_variables.bzl is read from cmake! append_filelist but not used uniformly for all components! (ouch!) mashed together ATen and Caffe2 build systems (e.g., main library libtorch_cpu is defined in caffe2/CMakeLists.txt) cmake: not very much syntax, "everything is a function". This means you can look up constructs relatively easily; e.g., even if() is a command the general cmake model: "set a bunch of variables, run a bunch of commands". cmake is VERY GREPPABLE but not everything is in CMakeLists.txt; check *.cmake too the directory structure makes no sense, you really need to grep. (doing a lot of set PARENT_SCOPE to propagate stuff) renaming a file? grep for it primary hazard of refactoring: need to make sure all the variables are setup at the new location many directories are not recursive glob, beware of adding new directories old school cmake: literally everything is stuffed in variables (CMAKE_CXX_FLAGS). new school cmake: attach things to targets, things propagate when you depend on targets (public/private dependencies) add_library: the most important thing don't randomly change things and pray: have hypotheses and test them…

PyTorch Developer Podcast

1
torchdeploy 13:42

לפני 4 years13:42

13:42

torchdeploy is a way of running multiple Python interpreters inside the same process. It can be used to deploy Python PyTorch programs in situations where the GIL is a problem, not the CPython interpreter. How does it work, and what kind of challenges does it pose for people who want to write code that calls from C++ to Python? 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…

PyTorch Developer Podcast

1
C++ frontend 17:07

לפני 4 years17:07

17:07

What's the C++ frontend? Why is avoiding templates so important? Why is Tensor a reference type? How do we simulate keyword arguments in C++? Where did the nn Module support in the C++ API come from? Why did we reimplement all modules in C++? How are modules implemented in C++? What are some performance challenges of writing Python in C++, and how are we working around them? Further reading. C++ frontend tutorial https://pytorch.org/tutorials/advanced/cpp_frontend.html Writing Python in C++ (a manifesto) https://github.com/pytorch/pytorch/wiki/Writing-Python-in-cpp-(a-manifesto) MaybeOwned PR https://github.com/pytorch/pytorch/pull/53317…

PyTorch Developer Podcast

1
PyObject preservation 16:19

לפני 4 years16:19

16:19

Given two separately refcounted objects, how can you arrange for each of them to stay live so long as the other is live? Why doesn't just having a strong-strong or strong-weak reference between the two objects work? What is object resurrection in CPython? What's a finalizer and why does it make things more complicated? How does Python GC work? Further reading. PyObject preservation PR https://github.com/pytorch/pytorch/pull/56017 Sam Gross's original PoC, which works fine if the two objects in question are both PyObjects https://github.com/colesbury/refcount/ PEP 442 Safe object finalization https://www.python.org/dev/peps/pep-0442/ Essential reading about Python GC https://devguide.python.org/garbage_collector/…

PyTorch Developer Podcast

1
Mobile selective build 16:02

לפני 4 years16:02

16:02

What is mobile selective build? Why are we so obsessed with reducing binary size? How does selective build work? Why doesn't static linking just work? Why can't you just read out the ops used in a TorchScript model to determine what operators you actually need? What are the tradeoffs of statically determining the operator dependency graph versus tracing? What's up with the SELECTIVE_NAME macro? How the heck does selective build work at all when you have multiple mobile apps in a single Buck build system? What takeaways should I have as a regular PyTorch developer? Further reading: Official open source mobile documentation on custom selective builds https://pytorch.org/mobile/android/#custom-build How to rebuild the op dependency yaml https://github.com/pytorch/pytorch/blob/master/tools/code_analyzer/build.sh Liner notes: binary size is premium; ship only what you actually need big idea: get the ops your model needs -> apply this to build of pytorch get the ops your model needs TorchScript ~> read it out directly from the model itself but what if ops use other ops? need a dependency graph. done with static analysis llvm (jiakai) ~> with a (possibly inaccurate) yaml checked in for easy kickstart if you don't want to run the pass (updated by bot, not operational since Feb, recommend rebuilding from scratch if you run into trouble) other possibility: dynamic tracing pro: no need for dependency graph, just look at what was called; works for dtypes con: need representative inputs, if control flow might not cover everything apply this to build of pytorch ordinarily: static linking ensures stuff that isn't used gets pruned but this doesn't work with distributed operator registration based on static initializers how? codegen - just don't generate it no codegen - SELECTIVE_NAME - C++ doesn't support string in macro build system integration buck constraint: only one library therefore: generate multiple copies of glue library alt: atomize library into each operator. caffe2 used to do this; each library takes a long time to build (1m) and crashes xcode because there's too many common hiccups modify implementation details, some op is/isn't called anymore ~> error! usually just means some yaml needs regenerating. PyTorch Edge developers are very friendly and can help…

PyTorch Developer Podcast

1
torch.nn 14:18

לפני 4 years14:18

14:18

What goes into the implementation of torch.nn? Why do NN modules exist in the first place? What's the function of Parameter? How do modules actually track all the parameters in question? What is all of the goop in the top level NN module class? What are some new developments in torch.nn modules? What are some open problems with our modules? Further reading: Implementation of nn.Module https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/module.py nn.Module is complicated and that means its sometimes a bit slow. Some analysis at https://dev-discuss.pytorch.org/t/overhead-in-nn-module-causing-massive-slowdowns-compared-to-raw-cublas-or-torchscript/110 Lazy modules PR https://github.com/pytorch/pytorch/pull/44538 and factory kwargs https://github.com/pytorch/pytorch/pull/54508 Liner notes: python for hackability (c++ is reimplemented) parameters parameter collection (for optimization) buffers: not considered optimizable modules functorial operation (_apply) jit script: staged computation (init is not scripted) __call__ to forward (extra instrumentation) serialization / state_dict new stuff: device kwarg (joel schlosser) new stuff: lazy modules (emcastillo) open problems: parameter initialization…

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History and constraints of Tensor

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History and constraints of Tensor