Fix TEGroupedLinear quantization for expert parallelism (EP > 1) (#833)

## What does this PR do?

**Type of change:** Bug fix / Compatibility update

**Overview:**

Fix `te_grouped_quantized_linear_fn` argument parsing for
TEGroupedLinear quantization when parallelism configuration results in
fewer local experts per GPU.

### Problem
TransformerEngine changed the _GroupedLinear.forward signature in PR
#2377 (released in TE 2.10):
Old signature (TE < 2.10): forward(ctx, inp, m_splits: List[int],
use_bias, is_first_microbatch, ...)
New signature (TE >= 2.10): forward(ctx, inp, non_tensor_args: Tuple,
*weights_and_biases) where non_tensor_args = (m_splits, use_bias,
is_first_microbatch, ...)
Without this fix, ModelOpt's quantization code fails with newer TE
versions because it tries to access m_splits directly from args[idx +
1], but in TE >= 2.10, that position contains the non_tensor_args tuple
instead.


### Root Cause
The code assumed m_splits was always directly accessible at args[idx +
1], but TransformerEngine PR #2377 changed the signature to pack all
non-tensor arguments into a tuple.
Taking Qwen3-30B-A3B (with `num_gemms=21`, threshold=44) as an example:

### Solution
Added version checking to handle both signatures:
```python
if Version("2.10") <= _TE_VERSION:
    # New signature: non_tensor_args is a tuple, m_splits is the first element
    num_gemms = len(args[idx + 1][0])
else:
    # Old signature: m_splits is directly args[idx + 1]
    num_gemms = len(args[idx + 1])
```

## Usage
<!-- You can potentially add a usage example below. -->
Works seamlessly with any TransformerEngine version:

```python
# High EP quantization - previously failed, now works
torchrun --nproc_per_node 8 examples/quantization/quantize.py \
  --hf-model-id /models/Qwen3-30B-A3B \
  --export-quant-cfg fp8 \
  --megatron-save-path /models/Qwen3-30B-A3B_fp8_mlm \
  --tp 8 \
  --ep 8

# High EP inference - previously failed, now works  
torchrun --nproc_per_node 8 examples/quantization/ptq_generate.py \
  --megatron-load-path /models/Qwen3-30B-A3B_fp8_mlm \
  --hf-model-id /models/Qwen3-30B-A3B \
  --tp 8 \
  --ep 8
```

## Testing
<!-- Mention how have you tested your change if applicable. -->
```python
# High EP quantization - previously failed, now works
torchrun --nproc_per_node 8 examples/quantization/quantize.py \
  --hf-model-id /models/Qwen3-30B-A3B \
  --export-quant-cfg fp8 \
  --megatron-save-path /models/Qwen3-30B-A3B_fp8_mlm \
  --tp 8 \
  --ep 8

# High EP inference - previously failed, now works  
torchrun --nproc_per_node 8 examples/quantization/ptq_generate.py \
  --megatron-load-path /models/Qwen3-30B-A3B_fp8_mlm \
  --hf-model-id /models/Qwen3-30B-A3B \
  --tp 8 \
  --ep 8
```

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

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


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

## Summary by CodeRabbit

* **Bug Fixes**
* Enhanced Mixture of Experts (MoE) calibration validation and
synchronization to ensure consistency across distributed training
setups.
* Improved grouped linear quantization robustness to handle varying
input patterns and tensor dimensions.

* **Improvements**
* Better error handling for incomplete MoE expert calibration detection.
  * More flexible argument parsing for quantization operations.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: James Shen <yueshen@nvidia.com>

Author: yueshen2016

modelopt/torch/quantization/plugins/transformer_engine.py

@@ -156,7 +156,17 @@ def te_grouped_quantized_linear_fn(package, func_name, self, *args):
         _assert_te_fp8_enabled()
         idx = 1 if func_name == "_forward" else 0
         inp = args[idx]
-        num_gemms = len(args[idx + 1])
+
+        # Handle both old and new TE signatures (changed in PR #2377 in TE 2.10)
+        # New signature (TE >= 2.10): forward(ctx, inp, non_tensor_args: Tuple, *weights_and_biases)
+        # Old signature (TE < 2.10): forward(ctx, inp, m_splits: List[int], use_bias, ...)
+        if Version("2.10") <= _TE_VERSION:
+            # New signature: non_tensor_args is a tuple, m_splits is the first element
+            num_gemms = len(args[idx + 1][0])
+        else:
+            # Old signature: m_splits is directly args[idx + 1]
+            num_gemms = len(args[idx + 1])
+
         weights_and_biases = args[-2 * num_gemms :]
         weights, biases = weights_and_biases[:num_gemms], weights_and_biases[num_gemms:]
         quantized_inputs = self.input_quantizer(inp)