Reduce UMA radial input-gradient work - #2149

UMA radial first layers receive distance features followed by source and
target element embeddings. During force inference only the distance prefix
depends on positions, but the ordinary frozen linear backward materializes all
288 input-gradient columns before the embedding columns are discarded.

Add an opt-in fast-GPU inference path that keeps the forward unchanged while
contracting the backward with only the distance-feature weight prefix. Derive
and validate the prefix from model dimensions, preserve existing parameters
and state dictionaries, and reject Hessian inference because this is a frozen
first-order inference boundary.

On H100 with UMA-S-1p2 at 1,000 atoms, the isolated option reduced dynamic
full-CUDA-graph latency from 17.179976 ms to 16.826578 ms, a 0.353399 ms
(2.06%) improvement averaged across independent fresh- and warm-cache
processes. The configuration used energy, forces, and stress; TF32;
merge_mole=True; external_graph_gen=False; internal graph v3; skin 0;
compile_dynamic_shapes=True; and full CUDA graph replay. The optimization was
also positive at every measured size from 32 through 1,000 atoms.

Test Plan:

```
PYTHONPATH=src /home/mlazos/.conda/envs/pytorch-3.12/bin/python -m pytest -q -c packages/fairchem-core/pyproject.toml tests/core/models/uma/uma_fast/test_radial_prefix_grad.py -m 'not gpu' tests/core/models/uma/nn/test_unified_radial.py tests/core/units/mlip_unit/test_inference_settings.py
pre-commit run --files src/fairchem/core/models/uma/nn/execution_backends.py src/fairchem/core/models/uma/nn/radial.py src/fairchem/core/models/uma/nn/unified_radial.py src/fairchem/core/units/mlip_unit/api/inference.py tests/core/models/uma/uma_fast/test_radial_prefix_grad.py tests/core/units/mlip_unit/test_inference_settings.py
```