TLite is a lightweight, statically linkable[1], model evaluator, supporting a
subset of what the full tensorflow library does, sufficient for the
types of scenarios we envision having. It is also faster.
We still use saved models as "source of truth" - 'release' mode's AOT
starts from a saved model; and the ML training side operates in terms of
saved models.
Using TFLite solves the following problems compared to using the full TF
C API:
- a compiler-friendly implementation for runtime-loadable (as opposed
to AOT-embedded) models: it's statically linked; it can be built via
cmake;
- solves an issue we had when building the compiler with both AOT and
full TF C API support, whereby, due to a packaging issue on the TF
side, we needed to have the pip package and the TF C API library at
the same version. We have no such constraints now.
The main liability is it supporting a subset of what the full TF
framework does. We do not expect that to cause an issue, but should that
be the case, we can always revert back to using the full framework
(after also figuring out a way to address the problems that motivated
the move to TFLite).
Details:
This change switches the development mode to TFLite. Models are still
expected to be placed in a directory - i.e. the parameters to clang
don't change; what changes is the directory content: we still need
an `output_spec.json` file; but instead of the saved_model protobuf and
the `variables` directory, we now just have one file, `model.tflite`.
The change includes a utility showing how to take a saved model and
convert it to TFLite, which it uses for testing.
The full TF implementation can still be built (not side-by-side). We
intend to remove it shortly, after patching downstream dependencies. The
build behavior, however, prioritizes TFLite - i.e. trying to enable both
full TF C API and TFLite will just pick TFLite.
[1] thanks to @petrhosek's changes to TFLite's cmake support and its deps!
Pointed out in Issue #56432: the current reference models may not be
quite friendly to open source projects. Their purpose is only
illustrative - the expectation is that projects would train their own.
To avoid unintentionally pulling such a model, made the URL cmake
setting require explicit user setting.
Differential Revision: https://reviews.llvm.org/D129342
The tensorflow AOT compiler can cross-target, but it can't run on (for
example) arm64. We added earlier support where the AOT-ed header and object
would be built on a separate builder and then passed at build time to
a build host where the AOT compiler can't run, but clang can be otherwise
built.
To simplify such scenarios given we now support more than one AOT-able
case (regalloc and inliner), we make the AOT scenario centered on whether
files are generated, case by case (this includes the "passed from a
different builder" scenario).
This means we shouldn't need an 'umbrella' LLVM_HAVE_TF_AOT, in favor of
case by case control. A builder can opt out of an AOT case by passing that case's
model path as `none`. Note that the overrides still take precedence.
This patch controls conditional compilation with case-specific flags,
which can be enabled locally, for the component where those are
available. We still keep an overall flag for some tests.
The 'development/training' mode is unchanged, because there the model is
passed from the command line and interpreted.
Differential Revision: https://reviews.llvm.org/D117752
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.
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
If the flag is not set, the script saved_model_aot_compile.py in tensorflow will
default it to the correct value. However, in TF 2.5, the way the value is set in
TensorFlowCompile.cmake file triggers a build error.
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D103972
This allows one to cross-compile the header/object for a model in a
setup where the compiler is built on a system that cannot host the AOT
compiler. For example, if arm-hostable clang is desired, while the AOT
Tensorflow compiler can cross-compile to arm, it can't currently run on
arm.
The only alternative in that scenario would be to cross-compile clang
itself, but that gets complicated when trying to run tests after that.
Differential Revision: https://reviews.llvm.org/D99992
Mircea Trofin