Mobile selective build

PyTorch Developer Podcast

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اضافه شده در four سال پیش

محتوای ارائه شده توسط PyTorch, Edward Yang, and Team PyTorch. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط PyTorch, Edward Yang, and Team PyTorch یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Worth Knowing with Bonnie Habyan

1
#11: From OJ Simpson Case to Best Selling Author - Marcia Clark Shares Latest Real Crime Book Release and How Resilience Is Key to Success and Reinvention 34:35

16 روز پیش34:35

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34:35

Marcia Clark, best known as the lead prosecutor in the O.J. Simpson trial, has become a trailblazer for women in law and beyond. Her journey from courtroom to bestselling author reflects her resilience and determination to redefine herself amidst intense public scrutiny. On this episode of Worth Knowing, Clark dives into her latest book, *Trial by Ambush*, which examines the 1953 Barbara Graham case—a story that highlights gender bias, media sensationalism, and the notion that all cases are subject to societal, cultural, and political winds. Clark shares how her experiences during the Simpson trial shaped her perspective on societal pressures and the role of women in high-stakes professions. Her reflections on how media, forensic science, and legal practices have evolved over decades offer valuable insights into the intersection of law and culture. This conversation is a compelling exploration of true crime, personal growth, and how Clark’s groundbreaking career continues to inspire a new generation of women to challenge norms and forge their own paths. Marcia Clark is a bestselling author and a criminal lawyer who began her career in law as a criminal defense attorney and went on to become a prosecutor in the L.A. District Attorney's Office in 1981. She spent ten years in the Special Trials Unit, where she handled a number of high-profile cases, including the prosecution of stalker/murderer Robert Bardo, whose conviction for the murder of actress Rebecca Schaeffer resulted in legislation that offered victims better protection from stalkers as well as increased punishment for the offenders. She was lead prosecutor for the O.J. Simpson murder trial. In May of 1997 her book on the Simpson case, "Without a Doubt," was published and reached #1 on the New York Times, Wall St. Journal, Washington Post, Los Angeles Times, and Publishers Weekly bestsellers lists. In February 2016, Clark re-released the book with a new foreword. Resources Sign up for the Worth Knowing LinkedIn Newsletter to stay up to date: https://www.linkedin.com/newsletters/worth-knowing-7236433935503618048/ Follow Bonnie on LinkedIn: https://www.linkedin.com/in/bonnie-habyan/ Go to the Worth Knowing website: https://www.worthknowing.show Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company…

۴ سال پیش 16:02

MP3•خانه قسمت

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

83 قسمت

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

Mobile selective build

PyTorch Developer Podcast

32 subscribers

published ۴ سال پیش

اشتراک گذاری

MP3•خانه قسمت

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

83 قسمت

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

همه قسمت ها

PyTorch Developer Podcast

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42 weeks پیش16:33

16:33

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15:56

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PyTorch Developer Podcast

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1 year پیش17:30

17:30

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1 year پیش13:25

13:25

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PyTorch Developer Podcast

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1 year پیش18:07

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.…

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1 year پیش12:06

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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.…

PyTorch Developer Podcast

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Unsigned integers 13:07

1 year پیش13:07

13:07

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PyTorch Developer Podcast

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1 year پیش18:00

18:00

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PyTorch Developer Podcast

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1 year پیش15:55

15:55

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PyTorch Developer Podcast

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Zero-one specialization 21:07

۲ years پیش21:07

21:07

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۲ years پیش25:35

25:35

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Weak references 16:46

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16:46

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۳ years پیش20:31

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AOTAutograd 19:12

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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

۳ years پیش18:36

18:36

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PyTorch Developer Podcast

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۳ years پیش16:12

16:12

PyTorch 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.…

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Python exceptions 14:47

۳ years پیش14:47

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?…

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۳ years پیش15:03

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.…

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All about NVIDIA GPUs 19:29

۴ years پیش19:29

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

1
Tensor subclasses and Liskov substitution principle 19:13

۴ years پیش19:13

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

1
Half precision 18:00

۴ years پیش18:00

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

۴ years پیش16:38

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

۴ years پیش13:37

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

1
Multiple dispatch in __torch_function__ 14:20

۴ years پیش14:20

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

۴ years پیش18:34

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

۴ years پیش15:03

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

1
gradcheck 16:58

۴ years پیش16:58

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

1
torch.use_deterministic_algorithms 10:50

۴ years پیش10:50

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

۴ years پیش15:14

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

۴ years پیش16:26

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

۴ years پیش13:06

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

۴ years پیش21:44

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

۴ years پیش15:41

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

۴ years پیش16:39

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

۴ years پیش14:34

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

۴ years پیش13:55

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…

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