This reverts commit a772f0bb920a4957fb94dd8dbe45943809fd0ec3.
The main problem was related to how we handled `dbgs()` from the hosted
compiler. Using explicit `subprocess.communicate`, and not relying on
dbgs() being flushed until the end appears to address the problem.
Also some fixes due to some bots running older pythons, so we can't have
nice things like `int | float` and such.
This reverts commit a7354899d1a235a796b3a2ccb45f6596983c8672.
The way stdout/stderr get routed seems to work differently locally and
on the bots. Investigating.
This hooks up the interactive model runner to the passes that support
ml-based decisions. Because the interface to this runner is the exact
same as the one used during inference, we just reuse the exact same
setup we have for "release mode". This makes "release mode" a misnomer -
and that's something we needed to resolve sooner or later (e.g.
supporting more than one embedded model for the same problem was another
reason to drop that nomenclature). That will happen in a subsequent
change.
To use this evaluator, just enable the pass in (currently) "release"
mode, but also pass the base name for the 2 channel files via the
pass-specific flag.
The 2 files are the responsibilty of the hosting process. The added
tests use a minimal, toy such host, illustrating setup and
communication.
Differential Revision: https://reviews.llvm.org/D143218
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
