add test for worklfow

Signed-off-by: Will Guo <willg@nvidia.com>

Author: willg-nv

tests/unit/onnx/quantization/autotune/test_workflow.py

@@ -0,0 +1,80 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import tempfile
+from pathlib import Path
+
+import onnx
+import pytest
+
+from _test_utils.import_helper import skip_if_no_tensorrt, skip_if_no_trtexec
+import .models as _test_models
+
+from modelopt.onnx.quantization.autotune.workflows import (
+    init_benchmark_instance,
+    region_pattern_autotuning_workflow,
+)
+
+@pytest.fixture
+def simple_conv_model():
+    """Simple ONNX model: Input -> Conv -> Relu -> Output. Created via models.py."""
+    return _test_models._create_simple_conv_onnx_model()
+
+@pytest.mark.parametrize("use_trtexec", [True, False])
+def test_export_quantized_model(use_trtexec, simple_conv_model):
+    """Test exporting quantized model with Q/DQ."""
+    if use_trtexec:
+        skip_if_no_trtexec()
+    else:
+        skip_if_no_tensorrt()
+
+    with tempfile.NamedTemporaryFile(suffix=".onnx", delete=False) as f:
+        baseline_model_path = f.name
+
+    # Save baseline model
+    onnx.save(simple_conv_model, baseline_model_path)
+
+    output_dir = baseline_model_path.strip(".onnx")
+    output_path = output_dir + ".quant.onnx"
+
+    try:
+        init_benchmark_instance(use_trtexec=use_trtexec)
+        autotuner = region_pattern_autotuning_workflow(baseline_model_path, Path(output_dir))
+
+        # Export model with Q/DQ insertion
+        autotuner.export_onnx(output_path, insert_qdq=True)
+
+        # Verify file was created
+        assert os.path.exists(output_path)
+
+        # Verify it's a valid ONNX model
+        exported_model = onnx.load(output_path)
+        assert exported_model is not None
+
+        # Verify that it contains Q/DQ nodes
+        qdq_nodes = [
+            n
+            for n in exported_model.graph.node
+            if n.op_type in ["QuantizeLinear", "DequantizeLinear"]
+        ]
+        assert qdq_nodes, "Q/DQ nodes not found in quantized model"
+
+        print("✓ QDQAutotuner export quantized model")
+    finally:
+        if os.path.exists(output_path):
+            os.unlink(output_path)
+