2.2.1

Summary

This PR delivers a broad flatbuffer_direct upgrade focused on three goals:

1. feature parity with the TF-converter path where practical,
2. safer dtype/shape handling for dynamic graphs,
3. stronger final-stage graph optimization for layout-heavy models.

It includes the two commits in fix-sgsch:

• d20dba8 Optimize redundant transpose chains in flatbuffer_direct
• e7641fa Update flatbuffer_direct converters, metadata handling, and tests

Key Improvements

1) FP16 accumulation metadata parity between TF-converter and flatbuffer_direct

• Added propagation of enable_accumulation_type_float16 into flatbuffer-direct export calls.
• Added model-level metadata plumbing to serialize reduced_precision_support in direct-written TFLite flatbuffers.
• Metadata now correctly reflects:
  • fp16accfp16 when -eatfp16 is enabled,
  • fp16accfp32 otherwise.
• This addresses mismatch cases where *_float16.tflite metadata stayed fp16accfp32 despite -eatfp16.

2) New/expanded builtin op coverage

• Added CumProd builtin lowering path (build_cumprod_op) using a TFLite decomposition:
  • RANGE, LESS/LESS_EQUAL, RESHAPE, TILE, FILL, SELECT_V2, REDUCE_PROD, optional REVERSE_V2.
• Added Unique builtin lowering path (build_unique_op) with validation constraints.
• Added CastLike builtin lowering path (build_castlike_op).
• Added Upsample dispatch entry to reuse Resize lowering/validation path.
• Added UNIQUE options serialization support in model writer (UniqueOptions).

3) Scatter/Index robustness upgrades

• ScatterND:
  • support numeric updates dtype (with casting to data dtype as needed),
  • normalize negative indices to ONNX semantics before scatter,
  • improve dynamic-shape compatibility for shape/signature handling.
• ScatterElements:
  • relaxed/expanded rank handling for indices/updates,
  • runtime shape handling for dynamic updates,
  • improved coordinate assembly for mismatched rank scenarios.

4) Dtype harmonization and 64-bit normalization improvements

• Added consistent INT64 -> INT32 and UINT64 -> UINT32 normalization in critical paths.
• Improved binary op dtype selection for mixed integer/float inputs and comparison ops.
• Added unary/reduce dtype harmonization helpers to prevent runtime mismatches after upstream dtype normalization.
• Improved Where/control-flow mux dtype normalization for signed/unsigned integer outputs.
• Added constant-input CAST folding and redundant INT64->INT32 CAST chain cleanup in post-lowering optimization.

5) Dynamic shape and reshape stability fixes

• Reworked dynamic reshape resolution behavior:
  • preserves runtime-driven newShape=[] intent when required,
  • uses safer template sanitization for 0 / multiple -1 cases,
  • avoids over-resolving shapes that should remain runtime-dynamic,
  • better treatment of onnxRawNewShape with existing concrete newShape.
• Improved replacement logic for EXPAND_DIMS / SQUEEZE to RESHAPE:
  • dynamic cases are preserved where static rewrite would be unsafe,
  • dynamic squeeze rewrite path uses runtime shape plumbing when valid.
• Improved static-shape reconciliation and signature sanitation for edge dynamic-lineage cases.

6) Layout/transpose optimization expansion

Added and integrated multiple strict/final-stage optimization passes in lower_from_onnx2tf.py, including:

• transpose-split mixed pre-concat consolidation,
• channel-shuffle reshape/transpose rewrite to gather,
• transpose-gather-transpose axis-remap cleanup,
• post-transpose InstanceNorm/bias/add chain simplification,
• affine chain folding (mul/add/mul forms),
• split-conv-concat bridge rewrite to single post adapter,
• concat/mul/add/transpose(+add/mean/reshape-tail) bridge reductions,
• additional terminal/final-pass reapplication for late-introduced patterns.

These reduce redundant transposes/casts and stabilize final graph topology after late rewrites.

7) GridSample and pooling support expansion

• GridSample:
  • expanded support to rank-5 border padding path (padding_mode in {zeros, border}),
  • refined indexing/clipping behavior and flattened indexing paths for border/zero modes.
• MaxPool1D:
  • expanded kernel/stride handling constraints (positive kernel/stride with more explicit indices-mode checks).

8) Accuracy evaluation and error-report runtime resilience

• Accuracy evaluator now supports safer dynamic-dimension defaults and layout-aware dynamic dimension inference.
• Added runtime TFLite input resize helper usage in evaluator and direct-op error report path.
• Prevents shape-signature incompatibility issues during validation/inference when model inputs are dynamic.

9) Control-flow handling improvements

• Extended supported If NMS-guard subpattern variant.
• Improved generic If branch mux dtype normalization.
• Strengthened Loop state output remapping/casting logic for dtype/shape consistency.

10) Documentation and release updates

• Updated README builtin coverage/constraints table to reflect current direct-lowering capabilities.
• Clarified that accumulation-type option applies to both tf_converter and flatbuffer_direct paths.
• Version bumped:
  • onnx2tf package version 2.2.0 -> 2.2.1
  • Docker tags in README updated accordingly.

Validation

Executed full direct-builder regression:

• pytest -q tests/test_tflite_builder_direct.py
• Result: 554 passed, 1 warning, 0 failures.

Also added/updated extensive tests for:

• CumProd builtin dispatch,
• Unique builtin lowering,
• ScatterND negative-index normalization and dtype casting,
• Upsample opset9 / Pad opset9 compatibility,
• dynamic reshape/signature preservation,
• transpose/layout optimization rewrites,
• integer dtype normalization in control-flow and elementwise paths,
• GridSample rank-5 border validation.

What's Changed

• flatbuffer_direct: add CumProd/Unique/CastLike, fp16 metadata parity, and shape/dtype optimizations by @PINTO0309 in https://github.com/PINTO0309/onnx2tf/pull/898

Full Changelog: https://github.com/PINTO0309/onnx2tf/compare/2.2.0...2.2.1