feat(flatbuffer_direct): add QLinear ops and quant-chain fusion

[PINTO0309]

Summary

This PR extends flatbuffer_direct for quantized ONNX graphs and documents the updated support matrix.

face_recognition_sface_2021dec_int8.onnx.zip

What was added

1. Added direct lowering for quantized operators in flatbuffer_direct:

   • QuantizeLinear -> QUANTIZE
   • DequantizeLinear -> DEQUANTIZE
   • QLinearAdd -> ADD
   • QLinearMul -> MUL
   • QLinearConv -> CONV_2D / DEPTHWISE_CONV_2D
   • QLinearMatMul -> FULLY_CONNECTED

2. Added flatbuffer_direct-only preprocess fusion:

   • DequantizeLinear -> BatchNormalization -> PRelu -> QuantizeLinear
   • BN params are folded and rewritten into Mul + Add form before lowering.

3. Added supporting direct builders and registry coverage:

   • BatchNormalization direct builder (MUL + ADD form)
   • Flatten direct builder (RESHAPE form)
   • op registry validators for quantized op constraints (const inputs, rank/shape checks, axis checks)

4. Updated README support status section for flatbuffer_direct.

Implementation details

Quantized builder layer

• New file: onnx2tf/tflite_builder/op_builders/quantized.py
• Implements quant parameter handling (scale, zero_point, quantized_dimension) for both:
  • raw quantized path (QLinear*)
  • dequant/quant bridge path (DequantizeLinear / QuantizeLinear)
• Handles:
  • dtype derivation from zero_point
  • quant metadata propagation across intermediate tensors
  • bias scale derivation for int32 bias (input_scale * weight_scale)

Dispatch/validation layer

• Updated onnx2tf/tflite_builder/op_registry.py with new DispatchEntrys and validators:
  • _validate_quantize_dequantize_linear
  • _validate_qlinear_binary
  • _validate_qlinear_conv
  • _validate_qlinear_matmul
  • _validate_batch_norm
  • _validate_flatten

Preprocess rules

• New rule file: onnx2tf/tflite_builder/preprocess/rules/quant_chain_fusion.py
• Registered via:
  • onnx2tf/tflite_builder/preprocess/rules/__init__.py
  • onnx2tf/tflite_builder/preprocess/__init__.py
• Rule ID: quant_chain_fusion_wave3

Tests

Added/updated tests

• tests/test_tflite_builder_direct.py
  • Added qlinear conv chain model
  • Added qlinear matmul/fc chain model
  • Included both in direct operator smoke coverage
• tests/test_tflite_builder_preprocess.py
  • Added test_quant_chain_fusion_wave3_rewrites_dq_bn_prelu_q_chain

Test results

• pytest -q tests/test_tflite_builder_preprocess.py tests/test_tflite_builder_direct.py
  • 72 passed

Real model verification

Validated with:

• face_recognition_sface_2021dec_int8.onnx
• command: python -m onnx2tf -i face_recognition_sface_2021dec_int8.onnx -o /tmp/onnx2tf_sface_pr_check -tb flatbuffer_direct --report_op_coverage -n

Coverage report highlights:

• conversion_error = null
• graph_summary.supported_nodes = 278 / 278
• graph_summary.unsupported_nodes = 0
• graph_summary.coverage_ratio = 1
• quant_chain_fusion_wave3 applied (matched_patterns=26, rewritten_patterns=26)

Additional updates

• Version bump:
  • pyproject.toml: 2.0.11
  • onnx2tf/__init__.py: 2.0.11
• README Docker tag examples aligned to 2.0.11.

[PINTO0309]

Supplement for additional commit 1090987.

Additional implementation (more INT8-oriented)

• Added direct lowering of PRelu to PRELU in flatbuffer_direct
  • onnx2tf/tflite_builder/op_builders/elementwise.py:215
• For quantized input (INT8/UINT8), alpha (slope) is converted to a quantized constant with quantization metadata
  • onnx2tf/tflite_builder/op_builders/elementwise.py:185
• Added PRelu dispatch and validator in the registry
  • onnx2tf/tflite_builder/op_registry.py:664
  • onnx2tf/tflite_builder/op_registry.py:940
• Removed PRelu expansion from pseudo_ops_wave1; PRelu is now handled by direct builder without decomposition
  • onnx2tf/tflite_builder/preprocess/rules/pseudo_ops.py:489
• Added PRELU builtin options mapping in model writer
  • onnx2tf/tflite_builder/model_writer.py:202
• Updated README support matrix
  • README.md:280 (builtin count 38)
  • README.md:315 (PRelu row)

Tests

• Existing + newly added tests:
  • tests/test_tflite_builder_direct.py:881 (verifies PRELU builtin emission)
• Result:
  • pytest -q tests/test_tflite_builder_preprocess.py tests/test_tflite_builder_direct.py tests/test_tflite_builder_op_coverage.py
  • 76 passed

Real model verification (face_recognition_sface_2021dec_int8.onnx)

• Conversion succeeded (--report_op_coverage): unsupported 0 / coverage 1.0
• All 27 PRelu nodes are dispatch_mode=builtin
• Number of float-output ops in generated TFLite:
  • Before: 223 / 335
  • After: 115 / 227
  • Main effect: decomposed PRelu (Neg/Relu/Mul/Sub) was consolidated into a single PRELU op