[Minor] Force 'fuse_wgrad_accumulation' to false for TE GroupedLinear (#814)

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## Summary by CodeRabbit

* **Bug Fixes**
* Automatically disables fuse_wgrad_accumulation when using ModelOpt
quantization with Transformer Engine-based quantization paths. A warning
is now displayed to notify users when this adjustment occurs.

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settings.</sub>

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Signed-off-by: realAsma <akuriparambi@nvidia.com>

Author: realAsma

modelopt/torch/quantization/plugins/transformer_engine.py

@@ -120,6 +120,13 @@ def _functionals_to_replace(self, value):
         self._functionals_to_replace = value
 
     def _setup(self):
+        if getattr(self, "fuse_wgrad_accumulation", False):
+            warnings.warn(
+                "fuse_wgrad_accumulation is not supported with ModelOpt quantization. "
+                "Setting fuse_wgrad_accumulation to False."
+            )
+            self.fuse_wgrad_accumulation = False
+
         # GroupedMLP stores the weights as weight0, weight1, etc. To run setup in order to
         # initialize the quantizer states, self.weight is used to extract shape, dtype etc. Assigning
         # self.weight0 to self.weight to run the quantizer states initialization.
@@ -131,6 +138,9 @@ def _setup(self):
         # Remove self.weight after setup.
         delattr(self, "weight")
 
+        # TODO: GroupedLinear supports weights split by `num_gemms`, to support quantization
+        # with static parameters beyond per-tensor, we need to support a unique quantizer for each gemm.
+
     def modelopt_post_restore(self, prefix: str = ""):
         # GroupedMLP stores the weights as weight0, weight1, etc. To run post_restore in order to
         # initialize the quantizer states, self.weight is used to extract shape, dtype etc. Assigning