Evaluate using Profile-Guided Optimization (PGO) and LLVM BOLT

Hi!

Recently I checked Profile-Guided Optimization (PGO) improvements on many projects - all current results are available here. According to multiple tests, PGO can help with improving performance in many cases (including libraries like pydantic-core). Trying to optimize the Tensorflow Text library can be beneficial since it could reduce spent CPU time on routines like text preprocessing.

I can suggest the following action points:

• Perform PGO benchmarks on Tensorflow Text. And if it shows improvements - add a note to the documentation about possible improvements in Tensorflow Text performance with PGO.
• Providing an easier way (e.g. a build option) to build scripts with PGO can be helpful for the end-users and maintainers since they will be able to optimize Tensorflow Text according to their own workloads if they decide to rebuild Tensorflow Text for their own needs.
• Optimize pre-built binaries (if it's possible to prepare or collect a good-enough training workload)

Since the Tensorflow Text native part (C++) is the library, I think the Pydantic-core experience can be reused here — also, Clang supports PGO for shared libraries. I think in this case possible to prepare some text preprocessing routines, collect the PGO profiles from them, and then use them as training PGO data.

Maybe testing Post-Link Optimization techniques (like LLVM BOLT) would be interesting too (Clang and Rustc already use BOLT as an addition to PGO) but I recommend starting from the usual PGO.

Here are some examples of how PGO optimization is integrated in other projects:

• Pydantic-core: GitHub PR
• Rustc: a CI script for the multi-stage build
• GCC:
  • Official docs, section "Building with profile feedback" (even AutoFDO build is supported)
  • A part in a "wonderful" configure script
• Clang: Docs
• Python:
  • CPython: README
  • Pyston: README
• Go: Bash script
• V8: Bazel flag
• ChakraCore: Scripts
• Chromium: Script
• Firefox: Docs
  • Thunderbird has PGO support too
• PHP - Makefile command and old Centminmod scripts
• MySQL: CMake script
• YugabyteDB: GitHub commit
• FoundationDB: Script
• Zstd: Makefile
• Foot: Scripts
• Windows Terminal: GitHub PR
• file.d: GitHub PR
• OceanBase: CMake flag

Many of the examples above are applications but there should be a huge difference - PGO works well with libraries too.

No TensorFlow Text file or test is identified. Start by benchmarking the native C++ text-preprocessing routines, then review the build scripts to determine whether PGO can be added and what workload and documentation would define a successful result.