2.1.4

2.1.4

Summary

Currently, flatbuffer_direct has better conversion stability than tf_converter mode.

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

• expand built-in lowering coverage,
• improve dynamic-shape correctness,
• harden optimization/runtime stability on large real-world models.

Functional Improvements

1. Built-in op coverage expansion

• Einsum

  • Added a specialized built-in lowering path for bchw,bnc->bnhw.
  • Added a generic 2-input Einsum decomposition path (for non-repeated-label equations) via built-in ops (SUM, TRANSPOSE, RESHAPE, BATCH_MATMUL, CAST).
  • Extended validation so unsupported equations fail early with clearer diagnostics.

• MatMul

  • Added low-rank built-in handling for vector-lhs, vector-dot, and scalar-multiply forms.
  • Improved behavior when metadata incorrectly collapses dynamic tensors to scalar-like shapes.

• Pooling

  • Added AveragePool rank-3 (1D) built-in lowering (EXPAND_DIMS -> AVERAGE_POOL_2D -> SQUEEZE).
  • Added rank-5 (3D) MaxPool/AveragePool lowering through staged reshape/transpose decomposition.

• ConvTranspose

  • Added grouped 2D ConvTranspose lowering using split-per-group + per-group transpose-conv + concat.
  • Added safer dilation and channel consistency handling.

• TopK

  • When k is scalar-constant, it is now constantized directly to INT32 for TOPK_V2.
  • Eliminates unnecessary constant-case runtime prep chains such as CAST -> SQUEEZE.

2. Dynamic-shape correctness improvements

• Reduce axis robustness

  • Improved rank resolution from signatures/metadata in dynamic paths.
  • Preserves raw negative axes (e.g. -1) when rank metadata is unreliable, preventing incorrect axis remapping.

• Reshape allowzero semantics

  • Added runtime rewrite for dynamic shape tensors so ONNX allowzero=0 dim-copy semantics are preserved in TFLite lowering.

• Reshape-chain safety

  • Prevented unsafe bypass of reshapes when shape vectors depend on input dimensions (0/-1 behavior).

3. Optimization and runtime stability hardening

• Added conservative guards and rollback logic in aggressive transpose/fanout rewrites to avoid introducing unbound runtime tensors.
• Extended unbound-input repair to include SHAPE consumers (not only reshape-related patterns).
• Improved channelwise constant layout repair to support rank-3 constants and apply rotation only when broadcast semantics stay correct.

4. Memmap/path safety improvements

• Added deterministic sanitize+truncate+hash filename generation to prevent path-component overflow (File name too long) in:
  • accuracy evaluator memmap outputs,
  • ORT output memmap files,
  • flatbuffer_direct op-error report input dumps.

5. Automatic scalar-Gather index normalization (without -rtpo)

• Added automatic normalization for Gather indices when the positions tensor is scalar-like:
  • constant scalar indices,
  • runtime scalar indices (metadata-normalized scalar signatures).
• The lowering now ensures indices are handled as 1D shape [1] for runtime compatibility, while preserving ONNX output-rank semantics by inserting a final shape restore where needed.

6. Version/docs alignment

• Bumped package version to 2.1.4.
• Updated README Docker image tags to 2.1.4.

Validation

• Full flatbuffer_direct direct-lowering test suite:

  • pytest -q tests/test_tflite_builder_direct.py
  • Result: 492 passed, 1 warning.

• Added/updated targeted regression tests, including:

  • AveragePool1D dynamic built-in lowering,
  • new Einsum built-in paths,
  • TopK constant-k constantization,
  • unbound SHAPE input repair,
  • channelwise broadcast-constant layout repair.

Expected Impact

• Fewer CUSTOM fallbacks for common patterns.
• Better numerical/shape consistency in dynamic models.
• Improved robustness against optimizer-induced invalid graph states.
• More stable large-model evaluation flows with safer memmap file handling.

What's Changed

• flatbuffer_direct: expand built-in lowering and harden dynamic-shape/runtime stability by @PINTO0309 in #895

Full Changelog: 2.1.3...2.1.4