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
Fix a test using invalid MLIR using different VRegs for the tied operands
of ADD64rr, which happened to trigger an assertion after my latest
changes.
Also attempting to adjust the MLRegalloc tests to the adjusted regalloc
(though I don't have a 100% working setup for them even without my
changes)
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
Factoring it out so we can subsequently cache it. This should be a NFC,
however, for the float quantities, we see small errors in the least
significant digits. This is because, before, we were summing up one by
one. Now, we sum up results of sums.
This shouldn't matter for ML, and will require rework when we do
quantization (avoiding floats altogether), but meanwhile, it did require
an update to the reference file used for testing.
The patch also bumps the precision of the variables involved in this, to
reduce the error (note they are casted back to float at the end by the
SET macro, since we only work with float and not double in TF)
Differential Revision: https://reviews.llvm.org/D118659
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
