Currently the autogenerated regalloc model will sometimes
output an incorrect LR index to evict instead of the first LR
with with the mask set to 1. This trips an assertion within
the MLRegallocAdvisor that the evicted LR has a mask of 1. This
patch, made possible by https://reviews.llvm.org/D124565, simplifies
the autogenerated model by taking away all unnecessary features and
getting rid of the functions that were previously to mix in all
the necessary inputs so they wouldn't get pruned by the Tensorflow
XLA AOT compiler. This is no longer necessary after the previously
mentioned patch. This also fixes the nondeterministic behavior
that is sometimes observed where the autogenerated model will
simply output 0 instead of the correct index.
Reviewed By: yundiqian
Differential Revision: https://reviews.llvm.org/D129254
The bulk of the implementation is common between 'release' mode (==AOT-ed
model) and 'development' mode (for training), the main difference is
that in development mode, we may also log features (for training logs),
inject scoring information (currently after the Virtual Register
Rewriter) and then produce the log file.
This patch also introduces the score injection pass, 'Register
Allocation Pass Scoring', which is trivially just logging the score in
development mode.
Differential Revision: https://reviews.llvm.org/D117147
When looking at building the generator for regalloc, we realized we'd
need quite a bit of custom logic, and that perhaps it'd be easier to
just have each usecase (each kind of mlgo policy) have it's own
stand-alone test generator.
This patch just consolidates the old `config.py` and
`generate_mock_model.py` into one file, and does away with
subdirectories under Analysis/models.
This change yields an additional 2% size reduction on an internal search
binary, and an additional 0.5% size reduction on fuchsia.
Differential Revision: https://reviews.llvm.org/D104751
They are not conducive to being stored in git. Instead, we autogenerate
mock model artifacts for use in tests. Production models can be
specified with the cmake flag LLVM_INLINER_MODEL_PATH.
LLVM_INLINER_MODEL_PATH has two sentinel values:
- download, which will download the most recent compatible model.
- autogenerate, which will autogenerate a "fake" model for testing the
model uptake infrastructure.
Differential Revision: https://reviews.llvm.org/D104251
We only need the C++ type and the corresponding TF Enum. The other
parameter was used for the output spec json file, but we can just
standardize on the C++ type name there.
Differential Revision: https://reviews.llvm.org/D86549
Different training algorithms may produce models that, besides the main
policy output (i.e. inline/don't inline), produce additional outputs
that are necessary for the next training stage. To facilitate this, in
development mode, we require the training policy infrastructure produce
a description of the outputs that are interesting to it, in the form of
a JSON file. We special-case the first entry in the JSON file as the
inlining decision - we care about its value, so we can guide inlining
during training - but treat the rest as opaque data that we just copy
over to the training log.
Differential Revision: https://reviews.llvm.org/D85674
Also compacted the checkpoints (variables) to one file (plus the index).
This reduces the binary model files to just the variables and their
index. The index is very small. The variables are serialized float
arrays. When updated through training, the changes are very likely
unlocalized, so there's very little value in them being anything else
than binary.
Aiden Grossman