2.3.4

2.3.4

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.

Summary

This PR significantly expands flatbuffer_direct builtin support and tightens runtime safety for the direct TFLite export path.

The main goal of this branch is to move a large set of README-listed ONNX operators from partial or custom-only handling into native builtin lowering, while also making the generated LiteRT models materially safer to execute.

In practical terms, this branch:

• broadens builtin ONNX operator coverage in flatbuffer_direct
• adds new validator-driven constrained lowerings for many previously missing ops
• hardens DeformConv so the builtin path no longer aborts LiteRT on invoke()
• expands direct-path runtime and coverage tests accordingly
• updates public package metadata and README support tables to reflect the new baseline

Why this change matters

The flatbuffer_direct backend is most valuable when it can emit TFLite builtins instead of relying on TensorFlow conversion fallback or custom ops. This branch improves that value proposition in two ways:

1. Coverage: many more README-supported ONNX ops now have builtin or builtin-decomposition paths.
2. Reliability: DeformConv now uses a LiteRT-safe lowering strategy for the supported standard case, instead of producing models that could serialize and allocate but still abort at runtime.

Key improvements

1. Large builtin coverage expansion

This branch adds or completes builtin lowering coverage across multiple builder families, including signal, reduction, elementwise, indexing, normalization, pooling, attention, and loss paths.

Notable areas improved include:

• random/window ops
  • Bernoulli, BlackmanWindow, HammingWindow, HannWindow, RandomNormal, RandomUniform, RandomUniformLike
• reduction and numeric ops
  • LeakyRelu, Mean, ReduceLogSum, ReduceLogSumExp, ReduceSumSquare, ThresholdedRelu, IsInf, IsNaN, Shrink
• shape/indexing/spatial ops
  • AffineGrid, CenterCropPad, Compress, ReverseSequence, Scatter, TensorScatter
• normalization / pooling / matrix ops
  • GroupNormalization, LpPool, GlobalLpPool, Det
• attention / signal / embedding / losses
  • Attention, DFT, STFT, MelWeightMatrix, RotaryEmbedding, NegativeLogLikelihoodLoss, SoftmaxCrossEntropyLoss
• ROI / unpool / deformable paths
  • MaxRoiPool, MaxUnpool, DeformConv

These additions are backed by registry-level dispatch/validation work and corresponding builder implementations.

2. DeformConv LiteRT runtime safety

DeformConv received a dedicated runtime hardening pass.

The new builtin lowering is intentionally constrained to the standard 2D float pattern:

• group=1
• offset_group=1
• rank-4 input/output/offset(/mask)
• FLOAT16/FLOAT32
• constant weights and optional constant bias

The previous path could generate a model that converted successfully but aborted the LiteRT interpreter during invoke(). The new path fixes that by:

• removing the GATHER(batchDims=1) dependency from the sampling path
• replacing it with a global-index gather approach
• reducing the high-rank grouped transpose chain
• lowering the sampling path to a LiteRT-safer rank profile

Unsupported grouped DeformConv patterns are still preserved as explicit custom-op candidates when custom ops are enabled and allowlisted.

3. Better registry, reporting, and docs alignment

The branch updates the source of truth for builtin support in op_registry.py, then propagates that state into:

• README builtin support table
• README custom-op candidate policy table
• op coverage tests and snapshots

The README builtin summary count is also refreshed to the current table contents.

4. Version and release metadata update

Package and container-facing version references are updated from 2.3.3 to 2.3.4 to match the functional expansion in this branch.

Testing

The branch includes substantial test expansion for flatbuffer_direct, including:

• direct conversion tests
• LiteRT runtime smoke/parity tests
• coverage report assertions
• fallback/custom-op policy tests

A notable addition is subprocess-isolated runtime verification for DeformConv, so native LiteRT abort regressions fail safely inside tests instead of taking down the full pytest worker.

Test result used for this branch

pytest -q tests/test_tflite_builder_direct.py tests/test_tflite_builder_op_coverage.py

Result:

• 646 passed, 2 warnings in 129.48s

Files of interest

The core changes are concentrated in:

• onnx2tf/tflite_builder/op_registry.py
• onnx2tf/tflite_builder/op_builders/conv.py
• onnx2tf/tflite_builder/op_builders/shape.py
• onnx2tf/tflite_builder/op_builders/reduce.py
• onnx2tf/tflite_builder/op_builders/elementwise.py
• onnx2tf/tflite_builder/op_builders/pool.py
• onnx2tf/tflite_builder/op_builders/index.py
• onnx2tf/tflite_builder/op_builders/norm.py
• onnx2tf/tflite_builder/op_builders/recurrent.py
• tests/test_tflite_builder_direct.py
• tests/test_tflite_builder_op_coverage.py
• README.md

Compatibility notes

This PR intentionally favors explicit constrained builtin support over broad but unsafe lowering.

The clearest example is DeformConv: the supported builtin path is now narrower, but it is meaningfully more correct because it survives actual LiteRT execution. Patterns outside that safe envelope still retain the existing custom-op fallback policy.

What's Changed

• feat: expand flatbuffer_direct builtin coverage and runtime safety by @PINTO0309 in #904

Full Changelog: 2.3.3...2.3.4