* [Collage] PruneCandidates and demo_collage_partition.py
See https://github.com/apache/tvm-rfcs/blob/main/rfcs/0062-collage.md.
This completes our checkin of our Collage 'sketch' branch into main. Special thanks
to Matthew Barrett for his help getting this over the line.
The only C++ functionality added here is for 'pruning' candidates. This is a somewhat
speculative algorithm (and I've called that out in the comments) which tries to
elide candidate partitions which will 'obviously' not contribute to the final optimal
partitioning. For largish models such as GPT2 this can significantly reduce the number of
candidates we need to actually measure latency on. I beefed up the MockCostEstimator to
make it possible to assert pruning occured from within the test_pass_collage_partition.py
unit test.
The rest of this PR adds the demo_collage_partition.py driver file we've been using
to test and measure perfomance differences against various baseline (though only
for the CUDA ecosystem). To eliminate loading time the models of interest are directly
expressed in Relay text form in menangerie.py.
* - lint
Mark Shields
d4365011f5