updated bmm and matmul for GPT-OSS (#999)

### What does this PR do?

This PR fixes maximum recursion bug for GPT-OSS. It replaces
`torch._bmm` and `torch.matmul` with `torch.ops.aten.bmm` and
`torch.ops.aten.matmul` to avoid recursion


### Usage

```shell
Docker image: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc4

[Repro Steps]:

[gpt-oss]
Step1:
accelerate launch --config_file configs/zero3.yaml sft.py --config configs/sft_full.yaml --model_name_or_path openai/gpt-oss-20b --output_dir /tmp/pytest-of-root/pytest-0/test_gpt_oss_complete_pipeline0/gpt-oss-20b-sft

Step 1 completed: SFT checkpoint at /tmp/pytest-of-root/pytest-0/test_gpt_oss_complete_pipeline0/gpt-oss-20b-sft


Step2:

accelerate launch --config_file configs/zero3.yaml sft.py --config configs/sft_full.yaml --model_name_or_path /tmp/pytest-of-root/pytest-0/test_gpt_oss_complete_pipeline0/gpt-oss-20b-sft --quant_cfg MXFP4_MLP_WEIGHT_ONLY_CFG --output_dir /tmp/pytest-of-root/pytest-0/test_gpt_oss_complete_pipeline0/gpt-oss-20b-qat
```

### Testing
``` python
pytest tests/examples/gpt_oss/test_gpt_oss_qat.py
```

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(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).

- Is this change backward compatible?: ✅ 
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dependency, did you follow guidance in `CONTRIBUTING.md`:N/A
- Did you write any new necessary tests?:  N/A (test already exist)
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A

### Additional Information
<!-- E.g. related issue. -->

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **Bug Fixes**
* Fixed a recursion-related instability in attention quantization that
could cause errors during certain matrix operations, improving
reliability.

* **Performance**
* Improved handling of batched and matrix-multiplication operations
under quantization for more consistent and efficient runtime behavior,
including better support for outputs specified by callers.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>

Author: kinjalpatel27

modelopt/torch/quantization/plugins/huggingface.py

@@ -1040,15 +1040,22 @@ def _setup(self):
 
     @property
     def functionals_to_replace(self):
-        def _quantized_bmm(batch1, batch2):
+        # Use torch.ops.aten to bypass Python dispatch and avoid RecursionError
+        # (torch.matmul / __matmul__ can dispatch to each other)
+        _aten_bmm = torch.ops.aten.bmm
+        _aten_matmul = torch.ops.aten.matmul
+
+        def _quantized_bmm(batch1, batch2, *, out=None):
             batch1 = self.down_proj_input_quantizer(batch1) if self._down_proj_mul else batch1
             self._down_proj_mul = not self._down_proj_mul  # toggle the flag
-            return torch._bmm(batch1, batch2)
+            if out is not None:
+                return torch.ops.aten.bmm.out(batch1, batch2, out=out)
+            return _aten_bmm(batch1, batch2)
 
         def _tensor_matmul(self_t, other):
             self_t = self.down_proj_input_quantizer(self_t) if self._down_proj_mul else self_t
             self._down_proj_mul = not self._down_proj_mul
-            return torch.matmul(self_t, other)
+            return _aten_matmul(self_t, other)
 
         return [
             (torch, "bmm", _quantized_bmm),