Handle supported ImportOptions in tf.py and fix Windows compatibility. (#13287)

This adds back support for `import_only` and `save_temp_iree_input` to
our TensorFlow `compile_saved_model` API. I also removed unsupported
options (`import_extra_args`, `save_temp_tf_input`,
`save_temp_mid_level_input`, and `use_tosa`).

Those flags were dropped in https://github.com/openxla/iree/pull/12758 /
https://github.com/openxla/iree/pull/13025 , but they are still useful
in Colab notebooks and when debugging tests.

---

Progress on https://github.com/openxla/iree/issues/13148, though some
further updates will be needed to our Colab notebooks, such as
```python
# before:
compiler_module = tfc.compile_module(
    EdgeDetectionModule(), import_only=True,
    import_extra_args=["--output-format=mlir-ir"])
print("Edge Detection MLIR: ", compiler_module.decode('utf-8'))

# after:
compiler_module = tfc.compile_module(
    EdgeDetectionModule(), import_only=True)
print("Edge Detection MLIR: ", compiler_module)
```

---

I developed this change on Windows (Yes! Finally, I can use Python that
touches TF on Windows without needing to build TF from source!), where I
found that this pattern is broken:
```python
 with tempfile.NamedTemporaryFile(mode="w") as temp_file: 
   __main__.import_saved_model(output_path=temp_file.name, 
```
See https://stackoverflow.com/a/23212515 - `NamedTemporaryFile` _creates
and opens_ the file, and the file _cannot be opened again_... on Windows
(it can be opened again on Unix). I used the trick from
https://stackoverflow.com/a/45803022 to work around this:
```python
with tempfile.TemporaryDirectory() as tmpdir:
  # ...

  # Not saving the file, so generate a loose temp file without tfs.
  tf_iree_input = os.path.join(tmpdir, 'tf-iree-input.mlir')
```
2 files changed
tree: f4bace898551ac3d6dd374a07527743549faf480
  1. .devcontainer/
  2. .github/
  3. benchmarks/
  4. build_tools/
  5. compiler/
  6. docs/
  7. experimental/
  8. integrations/
  9. lib/
  10. llvm-external-projects/
  11. runtime/
  12. samples/
  13. tests/
  14. third_party/
  15. tools/
  16. .bazel_to_cmake.cfg.py
  17. .bazelignore
  18. .bazelrc
  19. .bazelversion
  20. .clang-format
  21. .dockerignore
  22. .gitignore
  23. .gitmodules
  24. .pylintrc
  25. .style.yapf
  26. .yamllint.yml
  27. AUTHORS
  28. BUILD.bazel
  29. CITATION.cff
  30. CMakeLists.txt
  31. configure_bazel.py
  32. CONTRIBUTING.md
  33. LICENSE
  34. README.md
  35. WORKSPACE
README.md

IREE: Intermediate Representation Execution Environment

IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.

See our website for project details, user guides, and instructions on building from source.

CI Status

Project Status

IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!

Communication Channels

Related Project Channels

  • MLIR topic within LLVM Discourse: IREE is enabled by and heavily relies on MLIR. IREE sometimes is referred to in certain MLIR discussions. Useful if you are also interested in MLIR evolution.

Architecture Overview

IREE Architecture IREE Architecture

See our website for more information.

Presentations and Talks

  • 2021-06-09: IREE Runtime Design Tech Talk (recording and slides)
  • 2020-08-20: IREE CodeGen: MLIR Open Design Meeting Presentation (recording and slides)
  • 2020-03-18: Interactive HAL IR Walkthrough (recording)
  • 2020-01-31: End-to-end MLIR Workflow in IREE: MLIR Open Design Meeting Presentation (recording and slides)

License

IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.