Min-cut partitioner

PyTorch Developer Podcast

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Nội dung được cung cấp bởi PyTorch, Edward Yang, and Team PyTorch. Tất cả nội dung podcast bao gồm các tập, đồ họa và mô tả podcast đều được PyTorch, Edward Yang, and Team PyTorch hoặc đối tác nền tảng podcast của họ tải lên và cung cấp trực tiếp. Nếu bạn cho rằng ai đó đang sử dụng tác phẩm có bản quyền của bạn mà không có sự cho phép của bạn, bạn có thể làm theo quy trình được nêu ở đây https://vi.player.fm/legal.

Young and Profiting with Hala Taha (Entrepreneurship, Sales, Marketing)

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Dave Ramsey: 5 Stages to Build and Scale a Business That Lasts | Entrepreneurship | E344 1:03:38

cách đây 22 ngày trước1:03:38

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Too many entrepreneurs get stuck on the business treadmill, hustling nonstop, unable to scale, and unknowingly stalling their growth. That’s where Dave Ramsey began. After crashing into $3 million in debt, he rebuilt from scratch, turning a small radio program into a national show with millions of listeners. With over three decades of experience in entrepreneurship, business growth, and content creation, he knows what it takes to build a lasting business. In this episode, Dave reveals the six drivers of long-term success, the five key stages of startup growth, and how he balances life as an entrepreneur and a content creator. In this episode, Hala and Dave will discuss: (00:00) Introduction (00:23) The Core Principles of Financial Freedom (05:42) Adapting to Change as a Content Creator (09:22) Balancing Content Creation and Entrepreneurship (12:34) How to Create a Clear Path in Business (15:19) The Truth About Starting a Business Today (18:22) The Six Drivers of Business Success (26:20) Shifting From Tactical to Strategic Thinking (29:44) The Five Stages of Business Growth (41:10) Leading with Care, Clarity, and Accountability (47:10) Identifying the Right Leadership Skills (48:35) Starting a Media Business as an Entrepreneur Dave Ramsey is a personal finance expert, radio personality, bestselling author, and the founder and CEO of Ramsey Solutions. Over the past three decades, he has built a legacy of helping millions achieve financial freedom. As the host of The Ramsey Show , Dave reaches more than 18 million listeners each week. He is the author of eight national bestselling books. His latest, Build a Business You Love , helps entrepreneurs navigate growth and overcome challenges at every stage. Sponsored By: Shopify - Sign up for a one-dollar-per-month trial period at youngandprofiting.co/shopify OpenPhone: Streamline and scale your customer communications with OpenPhone. Get 20% off your first 6 months at openphone.com/profiting Airbnb - Find yourself a co-host at airbnb.com/host Indeed - Get a $75 sponsored job credit at indeed.com/profiting RobinHood - Receive your 3% boost on annual IRA contributions, sign up at robinhood.com/gold Factor - Get 50% off your first box plus free shipping at factormeals.com/factorpodcast Rakuten - Save while shopping at rakuten.com Microsoft Teams - Stop paying for tools. Get everything you need, for free at aka.ms/profiting LinkedIn Marketing Solutions - Get a $100 credit on your next campaign at linkedin.com/profiting Resources Mentioned: Dave’s Book, Build a Business You Love: bit.ly/BuildaBusinessYouLove Dave’s Website: ramseysolutions.com Active Deals - youngandprofiting.com/deals Key YAP Links Reviews - ratethispodcast.com/yap Youtube - youtube.com/c/YoungandProfiting LinkedIn - linkedin.com/in/htaha/ Instagram - instagram.com/yapwithhala/ Social + Podcast Services: yapmedia.com Transcripts - youngandprofiting.com/episodes-new Entrepreneurship, Entrepreneurship Podcast, Business, Business Podcast, Self Improvement, Self-Improvement, Personal Development, Starting a Business, Strategy, Investing, Sales, Selling, Psychology, Productivity, Entrepreneurs, AI, Artificial Intelligence, Technology, Marketing, Negotiation, Money, Finance, Side Hustle, Mental Health, Career, Leadership, Mindset, Health, Growth Mindset, Side Hustle, Passive Income, Online Business, Solopreneur, Networking.…

cách đây khoảng một năm trước 15:56

MP3•Trang chủ episode

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.

83 tập

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

Min-cut partitioner

PyTorch Developer Podcast

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published cách đây khoảng một năm trước

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MP3•Trang chủ episode

83 tập

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

Tất cả các tập

1
Compiler collectives 16:33

cách đây 37 weeks trước16:33

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

cách đây 51 weeks trước15:28

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

cách đây 1 year trước17:10

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

cách đây 1 year trước24:07

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

cách đây 1 year trước20:50

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

cách đây 1 year trước15:56

15:56

1
AOTInductor 17:30

cách đây 1 year trước17:30

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

cách đây 1 year trước13:25

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

cách đây 1 year trước18:07

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

cách đây 1 year trước15:54

15:54

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

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

cách đây 1 year trước12:06

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

cách đây 1 year trước13:07

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

cách đây 1 year trước18:00

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.…

1
Dynamo - VariableTracker 15:55

cách đây 1 year trước15:55

15:55

I 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 ).…

1
Unbacked SymInts 21:31

cách đây 2 years trước21:31

21:31

This 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 looking into the broadcasting code at all? Naively, I would expect the export graph to be just a list of ATen ops strung together. Why do I recurse that far down? Why can't I annotate DONT_TRACE_ME_BRO? - How does 0/1 specialization fit into this? I understand we may want to 0/1 specialize in a dynamic shape regime in "eager" mode (is there a better term?), but that doesn't seem to matter for export? - So far we've mainly been talking about how to handle our own library code. There is a worry about pushing complicated constraints downstream, similar to torchscript. What constraints does this actually push?…

PyTorch Developer Podcast

1
Zero-one specialization 21:07

cách đây 2 years trước21:07

21:07

Mikey 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?…

PyTorch Developer Podcast

1
torchdynamo 25:35

cách đây 2 years trước25:35

25:35

What 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 Developer Podcast

1
PyTorch 2.0 17:51

cách đây 2 years trước17:51

17:51

Soumith'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#

PyTorch Developer Podcast

1
History of functorch 19:10

cách đây 2 years trước19:10

19:10

Join 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?…

PyTorch Developer Podcast

1
Learning rate schedulers 19:35

cách đây 3 years trước19:35

19:35

What’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?

PyTorch Developer Podcast

1
Weak references 16:46

cách đây 3 years trước16:46

16:46

What 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…

PyTorch Developer Podcast

1
Strides 20:31

cách đây 3 years trước20:31

20:31

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/…

PyTorch Developer Podcast

1
AOTAutograd 19:12

cách đây 3 years trước19:12

19:12

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

1
Dispatcher questions with Sherlock 18:36

cách đây 3 years trước18:36

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

1
New CI 16:12

cách đây 3 years trước16:12

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.…

PyTorch Developer Podcast

1
Python exceptions 14:47

cách đây 3 years trước14:47

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

1
Torch vs ATen APIs 15:03

cách đây 3 years trước15:03

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.…

PyTorch Developer Podcast

1
All about NVIDIA GPUs 19:29

cách đây 4 years trước19:29

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

cách đây 4 years trước19:13

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

cách đây 4 years trước18:00

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

cách đây 4 years trước16:38

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

cách đây 4 years trước13:37

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

cách đây 4 years trước14:20

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

cách đây 4 years trước18:34

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

cách đây 4 years trước15:03

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

cách đây 4 years trước16:58

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

cách đây 4 years trước10:50

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

cách đây 4 years trước15:14

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

cách đây 4 years trước16:26

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

cách đây 4 years trước13:06

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

cách đây 4 years trước21:44

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

cách đây 4 years trước15:41

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

cách đây 4 years trước16:39

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

cách đây 4 years trước14:34

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

cách đây 4 years trước13:55

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

cách đây 4 years trước14:57

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

cách đây 4 years trước16:11

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

cách đây 4 years trước11:40

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

cách đây 4 years trước14:24

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

cách đây 4 years trước17:47

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

cách đây 4 years trước13:26

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

cách đây 4 years trước15:59

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

cách đây 4 years trước11:09

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

cách đây 4 years trước19:56

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

cách đây 4 years trước17:49

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

cách đây 4 years trước13:42

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

cách đây 4 years trước17:07

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

cách đây 4 years trước16:19

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

cách đây 4 years trước16:02

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

cách đây 4 years trước14:18

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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