There's an early-exit case for regalloc when we don't even get a chance
to ask for an advisor (priority or eviction), and switch the context.
Then, when we want to log the reward for that function (==the one with
the early exit case), we hit the error case where the function's name
doesn't match the last-seen context.
There are a few possible fixes, one would be to just switch context when
output-ing the reward, which would be correct. This patch opts for the
alternative where we check any loging happened in the first place - just
to re-validate that no function would have been regaloc-ed without first
log-ing its reward.
Differential Revision: https://reviews.llvm.org/D143359
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
This leverages the new logging format in that we don't need to buffer
the training data, we can just write it out.
Differential Revision: https://reviews.llvm.org/D142168
We use LLVM_HAVE_TFLITE as the key to enable the mlgo work these days,
and LLVM_HAVE_TF_API is defined whenever LLVM_HAVE_TF_API is defined.
I'm posting this patch because it's purely mechanical.
I'll post a follow-up patch to remove LLVM_HAVE_TF_API in non-C++
files, and that will not be as mechanical as this one.
Differential Revision: https://reviews.llvm.org/D139863
It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.
Differential Revision: https://reviews.llvm.org/D139031
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 and then produce the log file.
Differential Revision: https://reviews.llvm.org/D133616
