[HN Gopher] PyGraph: Robust Compiler Support for CUDA Graphs in ...
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       PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch
        
       Author : mfiguiere
       Score  : 79 points
       Date   : 2025-04-24 19:28 UTC (1 days ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | infocollector wrote:
       | The lack of a readily available, installable package (pip install
       | pygraph - has no relation to this paper as far as i can tell)
       | makes it difficult to fully assess the reproducibility and
       | practical applicability of the work.
        
         | easygenes wrote:
         | There's a request code button here:
         | https://www.catalyzex.com/paper/pygraph-robust-compiler-supp...
        
           | bwfan123 wrote:
           | why request code.. when all of pytorch2 is open, and this is
           | built on top of it with some enhancements, why not put this
           | out in the open
        
             | tough wrote:
             | I think that might be just a feature of the catalyzex
             | platform for papers with no linked code yet that might
             | internally add a +1 to code requested on their db and thats
             | it
             | 
             | some times papers come out a few weeks before code when its
             | bleeding edge
        
       | tho423i43234 wrote:
       | Nice to see work by IISc show up on HN.
       | 
       | Uday Bondhugula, the lead developer of Pluto framework for
       | polyhedral comp. is also at IISc, whose group has spun out a
       | startup,
       | 
       | https://www.polymagelabs.com/
       | 
       | Nice to see IISc support cool stuff like this (incl. their
       | ArtPark initiative.)
        
       | OutOfHere wrote:
       | I don't see any source code.
        
       | saagarjha wrote:
       | This is neat, although it would be nice to see it merged into
       | PyTorch instead of just a paper :) The key seems to be (beyond
       | "obvious" optimizations like not running graphs that are measured
       | to be slower) is that graphs "bake-in" parameters and if those
       | change then the graph needs to be thrown away. The solution is
       | indirecting more, so that what gets captured is a pointer that
       | can remain constant, while the data behind it is changed. This
       | also saves the need to copy in and out of a graph-captured buffer
       | because you can just swap out the pointer instead. Of course
       | there is overhead to this approach (I don't think the authors
       | actually explore this much) in that you throw away information
       | (divisibility, for example) that would allow for constructing
       | better kernels, but often this is still worth it. (Or you could
       | pass this through too.)
       | 
       | Something worth exploring later would be getting better support
       | for the rest of CUDA graphs into PyTorch, like conditional nodes.
        
       | damnitbuilds wrote:
       | Python can be used for many types of graphs. This package is for
       | CUDA Graphs, so wouldn't "PyCudaGraph" be a better name?
        
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