2.3.11

2.3.11

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.

Summary

This PR brings the feat-torch7 improvements into main as the 2.3.11 update for the PyTorch export path.

The main focus of this branch is exporter robustness around layout bridges, rank-3 reshape/transpose edge cases, and Dynamo ONNX post-processing. In practice, it makes the generated PyTorch package, TorchScript / ExportedProgram artifacts, and sanitized Dynamo ONNX outputs much more stable for models that mix channel-first runtime values with channel-last logical layouts.

What this PR improves

1. Native PyTorch export canonicalization is more robust

• Fixes rank-3 reshape materialization when the runtime tensor is effectively channel-first but the logical tensor metadata is channel-last.
• Preserves required NHWC materialization before rank-3 reshapes instead of collapsing it back to an unsafe alias during later rewrite passes.
• Restores correct public output resize target shapes in generated packages.
• Improves rank-3 transpose bridge handling in generated PyTorch code.

2. ExportedProgram rewrite passes are now safer

• Prevents _rewrite_generated_model_source_for_exported_program() from undoing already-correct materializations for tensors that must stay in channel-last form before reshape.
• Keeps the generated model.py semantically aligned with the original ONNX / lowered IR instead of reintroducing layout mismatches during artifact export.

3. Better handling for channel-last GAP / conv and gated bridge patterns

• Repairs channel-last global-average-pooling outputs before they are consumed by following convolution blocks.
• Adds canonicalization for channel-first hardsigmoid gate patterns so that bridge-heavy code is simplified without breaking downstream binary alignment.
• Improves bridge folding around GAP, conv, unary, and binary patterns that previously produced fragile generated code.

4. Dynamo ONNX sanitization is stronger and less shape-fragile

• Replaces rank-4-only output-shape repair with rank-agnostic static-shape recovery.
• Preserves output metadata for rank-3 transpose outputs and unary outputs that were previously at risk of losing valid shape information.
• Expands transpose/unary/binary/reduce-mean bridge folding so the exported ONNX graph is cleaner and carries more reliable boundary metadata.
• Adds folding for Mul + Add + Clip -> HardSigmoid style patterns.

5. Regression coverage is significantly expanded

This branch adds focused tests for:

• runtime channel-first aliases feeding rank-3 reshapes
• ExportedProgram rewrite safety for already-materialized NHWC aliases
• rank-3 transpose output-shape preservation in sanitized Dynamo ONNX
• unary / binary / reduce-mean layout-bridge folding
• GAP-to-conv channel-last repair
• hardsigmoid bridge folding
• model-backed regressions such as yolox_s output-shape preservation and existing bread helper behavior

Real regressions addressed by this branch

yolox_s.onnx

The generated *_dynamo.onnx could lose its final output shape metadata after sanitization. This branch preserves the expected output shape and adds regression coverage for it.

iat_llie_180x320.onnx

The generated PyTorch package could drift badly from ONNX because a required NHWC materialization was rewritten back into a raw channel-first alias before a rank-3 reshape.

After the fix, the conversion command below now passes the ONNX/PyTorch comparison:

python -m onnx2tf -i iat_llie_180x320.onnx -o /tmp/iat_llie_fix2_run -tb flatbuffer_direct -cotof -fdopt -fdots -fdodo -fdoep

Observed result after the fix:

• max_abs=4.76837e-07
• rmse=1.40854e-07
• cosine=1
• pass=True

Validation

I validated the branch with targeted exporter / sanitizer regressions, including:

pytest -q tests/test_pytorch_exporter.py -k 'runtime_cf_alias_before_rank3_reshape or exported_program_preserves_rank3_reshape_materialize_without_model_ir or preserves_transpose_rank3_output_shape or preserves_atan_rank4_output_shape'

pytest -q tests/test_pytorch_exporter.py -k 'exported_program_preserves_rank3_reshape_materialize_without_model_ir or runtime_cf_alias_before_rank3_reshape or preserves_transpose_rank3_output_shape or preserves_atan_rank4_output_shape or preserves_yolox_output_shape_when_model_is_available or helper_postprocess_is_noop_for_bread_nonfm_when_model_is_available'

Both validation sets passed locally, and the branch also includes the version bump to 2.3.11 in:

• onnx2tf/__init__.py
• pyproject.toml
• README.md

Notes for reviewers

Most of the code volume in this PR comes from two areas:

• broader layout-bridge folding / shape-repair support inside onnx2tf/tflite_builder/pytorch_exporter.py
• the matching regression suite added to tests/test_pytorch_exporter.py

The intention is not to change the overall export architecture, but to make the existing PyTorch export and Dynamo ONNX cleanup pipeline much more reliable for real-world bridge-heavy models.

What's Changed

• Improve PyTorch exporter layout bridge rewrites and Dynamo ONNX sanitization by @PINTO0309 in #913

Full Changelog: 2.3.10...2.3.11