pytorch

039faf0d - Add invariant that all symbolic shapes must be bound in graph (#99089)

Add invariant that all symbolic shapes must be bound in graph (#99089) Previously, we had a problem when partitioning forward-backward dynamic graphs, which is that we could end up with a backward graph that mentions a symbol in an input tensor (e.g., `f32[s0 + s1]`), but without this symbol being otherwise bound elsewhere. When this happens, we have no way of actually deriving the values of `s0` and `s1`. Our fix for this in https://github.com/pytorch/pytorch/pull/93059 was to just retrace the graph, so that s0 + s1 got allocated a new symbol s2 and everything was happy. However, this strategy had other problems, namely (1) we lost all information from the previous ShapeEnv, including guards and (2) we end up allocating a LOT of fresh new symbols in backwards. With this change, we preserve the same ShapeEnv between forward and backwards. How do we do this? We simply require that every symbol which may be present inside tensors, ALSO be a plain SymInt input to the graph. This invariant is enforced by Dynamo. Once we have done this, we can straightforwardly modify the partitioner to preserve these SymInt as saved for backwards, if they are needed in the backwards graph to preserve the invariant as well. This apparently breaks yolov3, but since everything else is OK I'm merging this as obviously good and investigating later. Signed-off-by: Edward Z. Yang <ezyang@meta.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/99089 Approved by: https://github.com/voznesenskym