FP16 and BF16 TensorRT Networks

Work in the owning families/<family>/ implementation. Read its model.py, local graph helpers, manifests, and tests before changing dtype behavior.

Preserve the typed graph

• Create the network with trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED.
• Resolve request.precision explicitly and reject unsupported values.
• Track checkpoint storage dtype, TensorRT tensor dtype, shape, and layout at every cast-sensitive boundary.
• Create constants in an intentional storage dtype and use network.add_cast(...) when the runtime dtype differs.
• Keep all inputs to an elementwise operation dtype-compatible, including epsilon, masks, scales, and affine parameters.
• Preserve compact K/V projection and cache shapes for grouped or multi-query attention.

Use FP32 only where the family implementation or measured reference evidence requires it, commonly reductions, normalization, probability arithmetic, or comparison-sensitive outputs. Cast back explicitly. Do not apply a blanket FP32 layer list or remove an established family boundary without evidence.

Precision and quantization are separate request values. Change one at a time, and do not add a shared model helper merely because another family has similar code. Copy a small proven pattern into the owner when needed.

Prove the change

Build through the public Python build command with the exact checkpoint and precision. Inspect the resulting bundle, run focused family Python/C++ tests, then run the declared family E2E testcase against its existing oracle.

Bundle size is only a signal. Qualification requires the requested graph path to execute and the unchanged family correctness contract to pass. Report any hardware or precision variant not run.