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
This patch fixes:
llvm/include/llvm/Analysis/Utils/TrainingLogger.h:94:14: error:
private field 'IncludeReward' is not used
[-Werror,-Wunused-private-field]
The dependency was due to the log format. This change switches to the
previously-introduced (D139370) "dependency-free" logger instead of the
protobuf-based one.
A subsequent change will clean out the unnecessary abstraction left
behind.
This change drops the logger unittest, we have sufficient test coverage
via lit tests, and a unit test would require adding, unnecesarily, a log
reader (the reader is expected to be python, for the ML side, and there
is a reader for that under Analysis/models, used for tests).
Differential Revision: https://reviews.llvm.org/D141720
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
This is the next step in dropping the dependency on protobuf.
The simple logger produces an output consisting of lines of json
strings. Tensor values - which should constitute the bulk of the data -
are serialized as raw byte buffers. This allows for light-weight reading
of the values.
The next step is to switch the training logic to the new logging format,
following which the protobuf-based logger will be dropped, together with
the training dependency on protobuf.
Subsequent changes will also stop buffering and stream, instead - the
buffering model is just as a convenient point-in-time.
Differential Revision: https://reviews.llvm.org/D139370
This is in preparation for dropping the dependency on protobuf. This
first step allows us to subsequently introduce the non-protobuf
implementation behind a flag. After that we can update the training side
to ingest the new format, after which we can drop the protobuf
implementation and de-virtualize everything.
Differential Revision: https://reviews.llvm.org/D139062
It's an artifact very specific to using TFAgents during training, so it
belongs with ModelUnderTrainingRunner.
Differential Revision: https://reviews.llvm.org/D139031
This just shuffles implementations and declarations around. Now the
logger and the TF C API-based model evaluator are separate.
Differential Revision: https://reviews.llvm.org/D131116
