2.2.2

2.2.2

Summary

This PR strengthens flatbuffer_direct conversion quality for DAMO-style graphs and broadens built-in operator coverage while keeping model contracts stable at graph boundaries.

Functional Improvements

• Added robust Float32 export path from FP16-heavy IR:
  • Introduced clone_model_ir_with_float32 to promote FP16 tensors/options into FP32.
  • Added prune_identity_cast_operators and optimize_redundant_transpose_operators to simplify generated graphs before writing TFLite.
  • Applied the same transpose cleanup to FP16 export and preserved reduced-precision metadata handling.
• Improved late-stage dynamic shape correctness:
  • _resolve_dynamic_reshape_shapes now supports prefer_runtime_inferable_from_onnx_raw=True to preserve ONNX runtime-inferable -1 templates instead of stale concretized shapes.
  • Extended dynamic-lineage preservation for RANGE outputs with runtime-dependent lengths.
  • Prevented aggressive reshape passthrough folding when preserveDynamicShape=True is set.
• Added LiteRT compatibility fallback for unsupported SPLIT input dtypes:
  • Rewrites unsupported SPLIT into SLICE chains (with optional cast in/out) so conversion succeeds for dtype combinations not accepted by LiteRT SPLIT.
• Added a new graph optimizer for DAMO-like normalization/padding layouts:
  • _optimize_transpose_flatten_globalnorm_pad_prepost_nhwc_chains removes redundant pre/post transpose wrappers and rewrites affected shape/pad metadata into NHWC-consistent form.

Operator Coverage Extensions

• Inverse:
  • Expanded built-in lowering from only 2x2/3x3 to generic square NxN (up to 16x16) by Gauss-Jordan-style tensor-op decomposition.
• GatherND:
  • Added lowering support for batch_dims > 0 by flattening batch prefix, synthesizing batch indices (RANGE + TILE + CONCAT), gathering, then reshaping back.
• Gemm/MatMul -> FULLY_CONNECTED path:
  • Added explicit I/O cast handling so FP16 interfaces can safely execute with internal FP32 compute.
• Bidirectional LSTM path:
  • Added FP16<->FP32 cast bridges to keep built-in kernel use while preserving external dtype contracts.
• Slice shape inference:
  • Improved static metadata resolution for strided slicing with known bounds; avoids incorrect oversized static lengths when runtime clipping may occur.
• GridSample:
  • Improved handling of partially-unknown shape metadata by merging with ONNX raw shape hints and inferring expected output dimensions when valid.

Validation/Registry Alignment

• Updated validators (Inverse, GatherND, GridSample) to match new lowering capabilities and constraints.
• Expanded op registry declarations for GatherND and shape/index helper ops (RANGE, SHAPE) required by new lowerings.

Tests

• Expanded tests/test_tflite_builder_direct.py with targeted regression tests covering:
  • FP16->FP32 IR promotion and cast/transpose cleanup
  • Inverse static 8x8 lowering
  • GatherND(batch_dims>0) and GridSample unknown-shape scenarios
  • FP16 FC cast bridges
  • unsupported-SPLIT fallback rewriting
  • reshape runtime -1 preference behavior
  • transpose/flatten/globalnorm/pad chain optimization
• Local test result:
  • pytest -q tests/test_tflite_builder_direct.py
  • 568 passed, 1 warning

Release Metadata

• Bumped package version to 2.2.2 (pyproject.toml, onnx2tf/__init__.py).
• Updated Docker tag examples in README to 2.2.2.

What's Changed

• Improve flatbuffer_direct coverage and DAMO conversion robustness by @PINTO0309 in #899

Full Changelog: 2.2.1...2.2.2