TensorAccessor

PyTorch Developer Podcast

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The Agile Brand with Greg Kihlström®

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#657: Augmenting front-line employees with AI for better experiences, with Fabrice Martin, Medallia 22:42

5 days ago22:42

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22:42

We are here recording live at Medallia Experience at the Wynn in Las Vegas, and have been seeing and hearing some amazing things about how AI can enhance the customer experience as well as enable teams at organizations to create more meaningful connections with customers. Today we’re going to talk about how AI can help to create better experiences for customers before, during, and after their interactions. To help me discuss this topic, I’d like to welcome Fabrice Martin, Chief Product Officer at Medallia. RESOURCES Medallia: https://www.medallia.com Catch the future of e-commerce at eTail Boston, August 11-14, 2025. Register now: https://bit.ly/etailboston and use code PARTNER20 for 20% off for retailers and brands Don't Miss MAICON 2025, October 14-16 in Cleveland - the event bringing together the brights minds and leading voices in AI. Use Code AGILE150 for $150 off registration. Go here to register: https://bit.ly/agile150 Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstrom Don't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.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 podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow 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…

4 years ago 11:40

MP3•Laman utama episod

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

83 episod

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

PyTorch Developer Podcast

TensorAccessor

PyTorch Developer Podcast

32 subscribers

published 4 years ago

Kongsi

MP3•Laman utama episod

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

83 episod

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

Semua episod

PyTorch Developer Podcast

1
Compiler collectives 16:33

35 weeks ago16:33

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…

PyTorch Developer Podcast

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TORCH_TRACE and tlparse 15:28

49 weeks ago15:28

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.

PyTorch Developer Podcast

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Higher order operators 17:10

50 weeks ago17:10

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.…

PyTorch Developer Podcast

1
Inductor - Post-grad FX passes 24:07

51 weeks ago24:07

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.…

PyTorch Developer Podcast

1
CUDA graph trees 20:50

1 year ago20:50

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.…

PyTorch Developer Podcast

1
Min-cut partitioner 15:56

1 year ago15:56

15:56

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

PyTorch Developer Podcast

1
AOTInductor 17:30

1 year ago17:30

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.…

PyTorch Developer Podcast

1
Tensor subclasses and PT2 13:25

1 year ago13:25

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.…

PyTorch Developer Podcast

1
Compiled autograd 18:07

1 year ago18:07

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.…

PyTorch Developer Podcast

1
PT2 extension points 15:54

1 year ago15:54

15:54

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

PyTorch Developer Podcast

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

1 year ago12:06

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.…

PyTorch Developer Podcast

1
Unsigned integers 13:07

1 year ago13:07

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.…

PyTorch Developer Podcast

1
Inductor - IR 18:00

1 year ago18:00

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.…

PyTorch Developer Podcast

1
Dynamo - VariableTracker 15:55

1 year ago15:55

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 ).…

PyTorch Developer Podcast

1
Unbacked SymInts 21:31

2 years ago21:31

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?…

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