Support glob patterns in OnnxKQuantQuantization nodes_to_exclude

olive/passes/onnx/kquant_quantization.py

@@ -12,6 +12,7 @@
 depending on onnxruntime's quantization modules.
 """
 
+import fnmatch
 import logging
 from pathlib import Path
 from typing import Optional
@@ -248,7 +249,11 @@ def _default_config(cls, accelerator_spec: AcceleratorSpec) -> dict[str, PassCon
             "nodes_to_exclude": PassConfigParam(
                 type_=list,
                 default_value=None,
-                description="List of node names to exclude from quantization.",
+                description=(
+                    "List of node names to exclude from quantization. Entries may be exact node "
+                    "names or Unix shell-style glob patterns (e.g. '*/projector/*' to exclude all "
+                    "projector MatMuls). A node is excluded if its name equals or matches any entry."
+                ),
             ),
             **get_external_data_config(),
         }
@@ -296,7 +301,9 @@ def _quantize_model(
         for node in ir_model.graph.all_nodes():
             node_name = node.name
 
-            if node_name in nodes_to_exclude:
+            if node_name in nodes_to_exclude or any(
+                fnmatch.fnmatchcase(node_name or "", pattern) for pattern in nodes_to_exclude
+            ):
                 logger.debug("Exclude quantization of %s as specified by nodes_to_exclude.", node_name)
                 continue
 

test/passes/onnx/test_kquant_quantization.py

@@ -135,3 +135,32 @@ def test_kquant_with_nodes_to_exclude(self, matmul_model_path, tmp_path):
 
         assert len(matmul_nbits_nodes) == 1, "Expected 1 MatMulNBits node (MatMul_2 quantized)"
         assert len(matmul_nodes) == 1, "Expected 1 original MatMul node (MatMul_1 excluded)"
+
+    def test_kquant_with_nodes_to_exclude_glob(self, matmul_model_path, tmp_path):
+        """Test k-quant where nodes_to_exclude uses a glob pattern."""
+        olive_model = ONNXModelHandler(model_path=str(matmul_model_path))
+        accelerator_spec = AcceleratorSpec(
+            accelerator_type="CPU",
+            execution_provider="CPUExecutionProvider",
+        )
+        # "*_1" matches MatMul_1 only; MatMul_2 should still be quantized.
+        pass_config = {
+            "bits": 4,
+            "block_size": 32,
+            "nodes_to_exclude": ["*_1"],
+        }
+        p = create_pass_from_dict(
+            OnnxKQuantQuantization, pass_config, disable_search=True, accelerator_spec=accelerator_spec
+        )
+
+        output_path = tmp_path / "quantized_glob_model.onnx"
+        quantized_model = p.run(olive_model, output_path)
+
+        assert os.path.exists(quantized_model.model_path)
+
+        quantized_onnx = onnx.load(quantized_model.model_path)
+        matmul_nbits_nodes = [n for n in quantized_onnx.graph.node if n.op_type == str(OpType.MatMulNBits)]
+        matmul_nodes = [n for n in quantized_onnx.graph.node if n.op_type == "MatMul"]
+
+        assert len(matmul_nbits_nodes) == 1, "Expected 1 MatMulNBits node (MatMul_2 quantized)"
+        assert len(matmul_nodes) == 1, "Expected 1 original MatMul node (MatMul_1 excluded via glob)"