PyTorch geometric quantization support

[i-riyad]

What does this PR do?

Type of change: New feature

Overview: Support quantization of PyTorch Geometric

# Add a code snippet demonstrating how to use this

Testing

python -m pytest tests/unit/torch/quantization/plugins/test_pytorch_geometric_plugin.py -v

Before your PR is "Ready for review"

• Make sure you read and follow Contributor guidelines and your commits are signed.
• Is this change backward compatible?: Yes
• Did you write any new necessary tests?: Yes
• Did you add or update any necessary documentation?: Yes
• Did you update Changelog?: Yes

Additional Information

• Support for quantize the graph neural networks (GNNs) #298

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 74.36%. Comparing base (e74a468) to head (15022ad).
⚠️ Report is 8 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #494   +/-   ##
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  Coverage   74.36%   74.36%           
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  Files         182      182           
  Lines       18216    18216           
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  Hits        13547    13547           
  Misses       4669     4669           

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[kevalmorabia97]

Exact date might get pushed

Suggested change

	0.40 (2025-12-09)
	0.40 (2025-12-xx)

[kevalmorabia97]

LGTM on a high level. Approving as codeowner

[realAsma]

nit: This might not be needed

Suggested change

	def forward(self, input, *args, **kwargs):
	"""Forward pass with quantization.
	Args:
	input: Input tensor to the linear layer
	*args: Additional positional arguments
	**kwargs: Additional keyword arguments
	Returns:
	Quantized output tensor
	"""
	# Quantize input activations
	input_q = self.input_quantizer(input)
	# Quantize weights
	weight_q = self.weight_quantizer(self.weight)
	# Perform linear operation
	output = torch.nn.functional.linear(
	input_q,
	weight_q,
	self.bias if hasattr(self, "bias") and self.bias is not None else None,
	)
	# Quantize output (typically disabled by default)
	return self.output_quantizer(output)

[realAsma]

For example, see the definition for ConvNd layers: https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/ed58324a3cebbf3ec4886ad4761c414e835df978/modelopt/torch/quantization/nn/modules/quant_conv.py#L39

The forward path should work (inherited from QuantLinearConvBase)

[realAsma]

nit:

For debugging, I directly run the unitests using VSCode/Cursor test explorer.

Suggested change

	if __name__ == "__main__":
	pytest.main([__file__])