### 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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Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)
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Make sure you read and follow the [Security Best
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weights_only=False)`, `pickle`, etc.).
- 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
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
## What does this PR do?
**Type of change:** ? Bug fix
**Overview:** ?
1) Remove kv_cache_config in the generate API. It's no longer used in
the code as well. We just estimate KV cache usage from other parameters
2) Add max_seq_len in the generate API to better estimate the real KV
cache usage.
3) Assume default lm_eval max input sequence length to be 4096
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Chenjie Luo