2.3.8

2.3.8

Important

Starting with onnx2tf v2.4.0, tf_converter will be deprecated and the default backend will be switched to flatbuffer_direct. With the v2.3.3 update, all backward compatible conversion options have been migrated to flatbuffer_direct, so I will only be doing minor bug fixes until April. If you provide us with ONNX sample models, I will consider incorporating them into flatbuffer_direct. I'll incorporate ai-edge-quantizer when I feel like it, but that will probably be about 10 years from now.

1. Content and background

This PR continues the feat-torch* line of work by tightening the native export path around flatbuffer_direct and the generated PyTorch artifacts.

The branch focuses on practical reliability improvements for the direct ModelIR-based path that is intended to become the main conversion flow:

• fixes native PyTorch export failures and numerical regressions seen in real models such as Faster R-CNN style graphs and finder.onnx
• improves flatbuffer_direct lowering and validation behavior for shape/index/control-flow related edge cases
• expands direct-path test coverage so regressions are caught earlier in both export and evaluation stages
• updates the package version to 2.3.8
• refreshes the publish workflow dependency baseline for Node 24

From a feature-improvement perspective, the main outcome is that flatbuffer_direct now behaves more like a first-class backend end-to-end: direct export, native PyTorch package generation, accuracy checking, and related validation/reporting are more consistent and substantially more robust on non-trivial graphs.

2. Summary of corrections

Key improvements included in this branch:

• Fixed native torch export regressions affecting flatbuffer_direct output packages.

  • corrected reshape/layout handling in generated PyTorch code so rank-2 flatten paths and singleton-axis drops no longer introduce invalid channel-last permutations
  • resolved a GridSample-adjacent accuracy regression where coordinate helper reshapes were reordered incorrectly in the generated package
  • improved native export behavior for Faster R-CNN related graph patterns

• Strengthened flatbuffer_direct lowering and runtime shape handling.

  • extended lowering support in shape/index/control-flow related builders and registry validation
  • improved ModelIR-to-PyTorch codegen around reshape, slice/strided-slice, gather/gather_nd, integer division, and related dynamic-shape cases
  • added runtime-side support needed by the updated generated code paths

• Expanded test coverage for direct export and native artifact generation.

  • added focused PyTorch exporter regression tests for reshape/layout preservation and singleton-axis handling
  • added/extended flatbuffer_direct tests covering direct lowering, validation, and numerical/runtime edge cases
  • kept README and package metadata aligned with the new behavior and release version

• Updated release and CI metadata.

  • bumped version metadata to 2.3.8
  • updated the publish workflow dependency baseline for Node 24

3. Before/After (If there is an operating log that can be used as a reference)

A representative real-model regression fixed in this branch was the finder.onnx direct export flow.

Command:

onnx2tf -i finder.onnx -o tmp_finder -tb flatbuffer_direct -cotof -fdopt -fdots -fdodo -fdoep

Before:

• ONNX/TFLite: pass
• ONNX/PyTorch: fail
• representative metrics:
  • max_abs=0.043901
  • rmse=0.01257
  • cosine=0.999059

After:

• ONNX/TFLite: pass
• ONNX/PyTorch: pass
• representative metrics:
  • max_abs=6.84522e-08
  • rmse=2.1783e-08
  • cosine=1

Validation performed for this branch:

• targeted PyTorch exporter regression tests for the new reshape/layout fixes: passed
• full flatbuffer_direct focused test run:
  • pytest -q tests -k flatbuffer_direct
  • result: 685 passed, 393 deselected, 3 warnings

4. Issue number (only if there is a related issue)

No related issue was linked for this change set.