Add QNN (Qualcomm HTP) backend to the ExecuTorch exporter
Author: psiddh
Pull request: yes

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Shift-left of QNN export from optimum-executorch into transformers: exporting an HF model with ExecutorchConfig(backend="qnn") now produces a QNN-delegated .pte directly.

New src/transformers/exporters/qnn_utils.py with three hooks, wired into the exporter's backend dispatch:
- prepare_for_qnn: pre-export prep — static-KV-cache wrap for decoder-only LMs (decode-capable graph; avoids the constant-folded-mask "No graph inputs present" QNN serialization failure) + HTP compiler spec (fp16, or int for the quantized path).
- quantize_for_qnn (_POST_EXPORT_TRANSFORM): PT2E 8a8w/16a4w quantization AFTER torch.export, which is where QNN quantizes (QnnQuantizer annotates the exported graph, then convert_pt2e).
- _lower_for_qnn (_BACKEND_LOWER): backend-specific lowering that runs QNN's pre-export + edge pass pipeline inside a QnnManagerContext. The exporter's generic to_edge_transform_and_lower can't lower QNN (hits KeyError aten.alias_copy.default / rank-3 FullyConnected / "No QnnManager active") because those passes never run.

Exporter plumbing:
- Add a _POST_EXPORT_TRANSFORM hook (runs on the ExportedProgram between export and lowering) and a _BACKEND_LOWER table (backend supplies its own lowering; default stays the generic path).
- ExecutorchConfig gains soc_model / quantize / static_cache / max_cache_len / calibration_inputs (all QNN-only; ignored by xnnpack/cuda).
- integrations/executorch.py: TorchExportableModuleForDecoderOnlyLM.forward now threads cache_position into the static-cache module (it was dropped, breaking the decode graph).

Validated on Qwen2.5-0.5B -> fully QnnBackend-delegated .pte (FP16, SM8750 and SM8550).

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