<div><div><div dir="auto">Hi Sean,</div><div dir="auto"><br></div><div dir="auto">Sorry for the delay.</div><br><div class="gmail_quote"></div></div></div><div><div><div class="gmail_quote"><div dir="auto">On Sat, May 5, 2018 at 1:23 PM Sean Silva <<a href="mailto:chisophugis@gmail.com" target="_blank">chisophugis@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div dir="auto"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:small;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Very cool! The first thing that jumps out to me is how tidy and modular the code structure is. The code feels very familiar (stylistically, organizationally, etc.) to me as an LLVM developer.</span></div></div></blockquote><div dir="auto"><br></div></div></div></div><div><div><div class="gmail_quote"><div dir="auto">Thanks! We absolutely took inspiration from the wonderful work in LLVM :). I’m glad that you found it familiar and tidy.<br></div></div></div></div><div><div><div class="gmail_quote"><div dir="auto"><br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div dir="auto"><span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:small;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"></span></div></div></blockquote><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div dir="auto">One thing that wasn't at all clear to me is how this is different/similar to TensorFlow XLA<span style="color:rgb(34,34,34);font-family:arial,sans-serif;font-size:small;font-style:normal;font-variant-ligatures:normal;font-variant-caps:normal;font-weight:400;letter-spacing:normal;text-align:start;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><span> </span>(previously mentioned on this list)</span>. Can you briefly compare and contrast this with TensorFlow XLA?</div></div></blockquote><div dir="auto"><br></div></div></div></div><div><div><div class="gmail_quote"><div dir="auto">That is a very keen observation. There are many similarities between the two projects. However, there are some differences too.</div><div dir="auto"><br></div><div dir="auto">Both are interested in performing cross-node optimizations to address memory usage and execution time. In order to accomplish their goals, both have their own IR and optimization passes.</div><div dir="auto"><br></div><div dir="auto">At the same time, there seem to be some differences as well. In the case of glow we have focused on the use of Caffe2 models and have support for the ONNX format as well. We have been trying to focus on providing a more target independent model and have been considering some heterogeneous execution models as well. XLA is definitely a more mature compiler compared to glow.</div><div dir="auto"><br></div><div dir="auto">I think that there are sufficient similarities and differences that there are ample opportunities for collaboration as these projects grow further.</div><div dir="auto"><br></div></div></div></div><div><div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div dir="auto"></div><div>-- Sean Silva</div><div><br></div><div><br></div><div class="gmail_quote"></div></div><div><div class="gmail_quote"><div>On Thu, May 3, 2018, 6:14 PM Saleem Abdulrasool via llvm-dev <<a href="mailto:llvm-dev@lists.llvm.org" target="_blank">llvm-dev@lists.llvm.org</a>> wrote:<br></div></div></div><div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div>Hello LLVM community,</div><div><br></div><div>We have been working hard on a new domain specific optimizing compiler, and we</div><div>are pleased to announce that we have recently open sourced the project! We</div><div>would like to introduce you to Glow, an optimizing compiler for neural networks!</div><div><br></div><div>This new compiler is built on the hard work of this community and we would like</div><div>to thank all of the contributors to the LLVM project. We hope that the project</div><div>will be beneficial to others as well, which would not have been possible without</div><div>your work.</div><div><br></div><div>You can find the sources to it at <a href="http://github.com/pytorch/glow" rel="noreferrer" target="_blank">http://github.com/pytorch/glow</a> and read up on</div><div>the work in the associated paper we have released at <a href="https://arxiv.org/pdf/1805.00907" rel="noreferrer" target="_blank">https://arxiv.org/pdf/1805.00907</a>.</div><div><br></div><div>Thank you all!</div><div><br></div><div>The Glow Developers</div>
</div></blockquote></div></div><div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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</blockquote></div></div>
</blockquote></div></div></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature">Saleem Abdulrasool<br>compnerd (at) compnerd (dot) org</div>