[6058841] Fix inconsistent tensor types on control-flow (If/Loop/Scan) subgraphs during FP16/BF16 conversion

CHANGELOG.rst

@@ -64,6 +64,7 @@ Experimental
 - Fix ``ShapeInferenceError`` during ONNX INT8 + FP16 quantization (``--high_precision_dtype fp16``) of weakly-typed models (e.g. TensorFlow exports) that carry stale rank-0 ``graph.output`` shapes or ops such as ``TopK`` that ONNX's static shape inference cannot resolve. ``clear_stale_value_info`` now reconciles stale output shapes via symbolic shape inference (keeping every output's shape field populated), and AutoCast runs ONNX shape inference in strict mode and falls back to schema-based standalone type inference when it fails, so unresolved ops no longer leave tensors untyped.
 - Fused MoE expert auto-detection (``register_fused_experts_on_the_fly``) no longer requires an ``act_fn`` attribute. Some fused-expert modules (e.g. ``MiniMaxM3VLExperts``) apply a custom gated activation between the two ``F.linear`` calls instead of exposing ``act_fn``; they were silently skipped, leaving routed experts unquantized (an experts-only recipe matched nothing) and failing HF export with ``NotImplementedError``. ``_QuantFusedExperts`` is activation-agnostic (it only intercepts the two ``F.linear`` calls), so the requirement was unnecessary. This enables NVFP4/FP8 quantization and export for MiniMax-M2 / MiniMax-M3.
 - Fix unified HF export emitting transformers' *in-memory* (post-``conversion_mapping``) tensor names instead of the original model-hub names, breaking the unified-checkpoint contract (observed on MiniMax-M3: exported ``model.language_model.*`` / ``mlp.experts.*.gate_proj`` instead of hub ``language_model.model.*`` / ``block_sparse_moe.experts.*.w{1,2,3}``). transformers' own save-side ``revert_weight_conversion`` is disabled by ModelOpt because it raises ``RuntimeError`` on 0-d scalar scale tensors, so a new quant-aware reverse conversion (``modelopt/torch/export/quant_aware_conversion.py``) derives rename/split rules from the model's conversion mapping via transformers' ``reverse_transform()`` and carries each weight's companion scale tensors (``weight_scale``, ``weight_scale_2``, ``input_scale``, ``weight_scale_inv``, ``bias``) through the renames and un-fusions, so quantized exports round-trip to the hub names. Any mapping op that cannot be reversed quant-aware yet (e.g. still-stacked fused experts) falls back to the previous in-memory names instead of aborting the export.
+- Fix ONNX FP16/BF16 conversion (``--high_precision_dtype fp16``) producing inconsistent tensor types on models with control-flow subgraphs (e.g. a ``Gemm`` inside an ``If`` branch reading an outer-scope activation alongside converted weights, or ``Resize`` ``scales`` that must stay FP32). Subgraph nodes now only run in low precision when all their float inputs are subgraph initializers; outer-scope captures and low-to-high-precision boundaries inside subgraphs are reconciled with ``Cast`` nodes, and ``Constant`` folding refreshes the constant's ``value_info`` so strongly-typed parsers (TensorRT) no longer reject the model. This is a behavioral change: previously a low-precision control-flow parent converted *every* float subgraph initializer, so a weight inside a branch that also reads an outer-scope FP32 activation could become FP16; such weights now stay FP32 so each node's inputs keep a single precision.
 
 0.45 (2026-07-02)
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