Fix LLM deploy test failure by defaulting expert parallelism to 1 (#1273)

### What does this PR do?

Type of change: Bug fix

Fixes TRT-LLM DeepEP kernel failures during LLM deployment on
unsupported GPUs (e.g. Blackwell SM 12.0) by defaulting expert
parallelism (`ep`) to 1 instead of auto-setting it to the GPU count for
MoE models.

Previously, when the model config contained expert-related keys, `ep`
was automatically set to `torch.cuda.device_count()`, which triggered
DeepEP kernel failures on GPUs that don't support it. Now `ep` defaults
to 1 while still enabling attention data parallelism for MoE models.
Expert parallelism can be enabled explicitly by the caller when the
environment is known to support it.

### Testing

- [x] Verified that the `llm_ptq` test passes with this fix on Blackwell
GPUs.
- [x] 2-gpu CI test triggered:
https://github.com/NVIDIA/Model-Optimizer/actions/runs/24495054531/job/71588037727

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- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A
- Did you write any new necessary tests?: N/A
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[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A

Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>

Author: cjluo-nv

modelopt/deploy/llm/generate.py

@@ -109,12 +109,13 @@ def _find_max_position_embeddings(cfg: dict) -> int | None:
         if tp < 1:
             tp = torch.cuda.device_count()
 
-        # Check if any key in config contains both "num" and "experts"
+        # Force ep=1 to avoid TRT-LLM DeepEP kernel failures on unsupported GPUs
+        # (e.g. Blackwell SM 12.0). Expert parallelism can be enabled explicitly
+        # by the caller when the environment is known to support it.
         ep = 1
         enable_attention_dp = False
         for k in config:
             if "num" in k and "experts" in k:
-                ep = torch.cuda.device_count()
                 enable_attention_dp = True
                 break