[vllm] [2.12 regression][Inductor] assert_size_stride (8192 vs 512) in BERT-with-rope AOT-compiled path for Nomic v2 MoE pooling model

## Summary

On `torch==2.12.0` + `triton==3.7.0`, the Extended-Pooling pytest job for the `nomic-ai/nomic-embed-text-v2-moe` model fails inside an Inductor AOT-compiled forward with a runtime stride/shape assertion:

```
File "/root/.cache/vllm/torch_compile_cache/torch_aot_compile//inductor_cache/ra/cra7eslwqvpfcdh65siik6ag5goi4wdpxi6ivjnoyjbr4zci2oje.py", line 664
assert_size_stride(arg9_1, (512, 64), (64, 1))
AssertionError: expected size 8192==512, stride 64==64 at dim=0
This error most often comes from a incorrect fake (aka meta) kernel for a custom op.
```

The compiled function's entrypoint is `vllm/model_executor/models/bert_with_rope.py:476::forward`. The path is the `flashinfer_autotune → runner._dummy_run` kernel warmup that runs as part of `_initialize_kv_caches`.

The expected dim-0 size in the compiled artifact is 512 (the configured `max_model_len`) but the runtime input has dim-0 size 8192 — exactly 16× larger, consistent with an MoE expert-broadcast (Nomic v2 MoE has 16 experts) where the fake kernel for the MoE custom op declared the post-MoE shape as `(seq_len, hidden)` while the runtime allocation is `(seq_len * num_experts, hidden)`.

Inductor's hint about a wrong fake kernel matches the diagnosis in vllm-project/vllm#40960 / pytorch/pytorch#182328 (the gpt-oss `moe_forward` case) — likely a similar vLLM-side `register_fake` / `direct_register_custom_op` shape mismatch, specific to the Nomic v2 MoE path. **Root cause is probably in vLLM**, not torch.

## Environment

- `torch==2.12.0+cu130`
- `triton==3.7.0`
- `torchvision==0.27.0`
- CUDA 13.0
- GPU: H200 (Buildkite `h200-ci-6-3`)
- Python 3.12
- vLLM commit `d2792bf2088c` (PR vllm-project/vllm#42848)
- Model: `nomic-ai/nomic-embed-text-v2-moe`, `runner=pooling`, `max_model_len=512`, `dtype=auto` (downcast to fp16)

## Reproduction

```
pytest -x tests/models/language/pooling/test_nomic_max_model_len.py::test_use_rope_scaling_legal[model_info1]
```

`model_info1` selects `nomic-ai/nomic-embed-text-v2-moe`; the test instantiates `VllmRunner` with `hf_overrides={"rope_parameters": {"rope_theta": ..., "rope_type": "yarn", "factor": ...}}` and `max_model_len=512`. Crash happens during `flashinfer_autotune` warmup.

## Traceback (trimmed)

```
vllm/v1/engine/core.py:283 _initialize_kv_caches
→ vllm/v1/executor/abstract.py:124 initialize_from_config
→ vllm/v1/worker/gpu_worker.py:608 compile_or_warm_up_model
→ vllm/model_executor/warmup/kernel_warmup.py:133 flashinfer_autotune
→ runner._dummy_run
→ vllm/model_executor/models/bert_with_rope.py:476 forward
→ AOT compiled fn → Inductor output_code
→ assert_size_stride(arg9_1, (512, 64), (64, 1))
AssertionError: expected size 8192==512, stride 64==64 at dim=0
```

## Diagnosis question

Is the `assert_size_stride` insertion behavior new in 2.12 for this code path (e.g. a `Tag.needs_fixed_stride_order` change that started routing the MoE expert-broadcast through the assertion)? If yes, vLLM should fix the fake kernel; this can be tracked vLLM-side and closed `not_planned` here. If the assertion existed before but the fake kernel was bypassed under 2.11, please confirm so we can prioritize the vLLM-side fake-impl fix.

## Links

- vLLM PR: vllm-project/vllm#42848
- Failing build: https://buildkite.com/vllm/ci/builds/66993#019e4286-221f-478d-b5ec-c27b1732d6a2
- Passing main baselines (torch 2.11): builds #66975, #66835, #66759 (job not present in #67062, #65912 shard)
- Related vLLM-side fake-kernel issue: pytorch/pytorch#182328
- Umbrella: pytorch/pytorch#180899

cc @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @kadeng @muchulee8 @amjames @aakhundov @coconutruben @jataylo @avikchaudhuri @zhxchen17 @tugsbayasgalan @angelayi @ydwu4 @bdhirsh @bobrenjc93 @aorenste @desertfire @yushangdi @iupaikov-amd