mirror of https://github.com/llvm/torch-mlir
79a3f639bf
This is a much simpler representation than the ad-hoc initializer function we had before. It is also less general, but given the rationale in Passes.td it seems like the right tradeoff right now. We can probably carry this representation for quite a while, and when we can't, it likely means that TorchScript has fixed their object identity bug and we probably need to just upgrade to a more general object graph modeling (more general than GlobalizeObjectGraph). In particular, we don't want to deal with defining and carrying around this initializer function concept until we need it. For example, if we want to constant-fold the global slots into uses, this is a much better representation, and it plays better with symbol-dce (the initializer function counts as a "use" of the symbol). (the alternative would have been to write a pass that converts the initializer function to this form when possible, but I realized that lots of information had been lost which made that fairly annoying -- it was all self-inflicted anyway, so best to just go to the source (GlobalizeObjectGraph) before the information is lost) Now symbol-dce works nicely (no more "training" bools) ``` pt_util ~/tmp/classifier.pt --import --exported-name forward \ | npcomp-opt -torch-globalize-object-graph -inline -symbol-dce ``` IR: https://gist.github.com/silvasean/8abe63d70d24e29d6db9170ccc8d512b |
||
|---|---|---|
| .. | ||
| Backend/Iree | ||
| CAPI | ||
| Conversion | ||
| Dialect | ||
| Python | ||
| RefBackend | ||
| npcomp-run-mlir | ||
| CMakeLists.txt | ||
| lit.cfg.py | ||
| lit.site.cfg.py.in | ||