Integrate Automated QDQ benchmark - part 3.1

modelopt/onnx/quantization/autotune/__init__.py

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+# 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.
+
+"""Pattern-Based Q/DQ Autotuning for ONNX Models.
+
+This package provides automated optimization of Quantize/Dequantize (Q/DQ) node placement
+in ONNX computation graphs to minimize TensorRT inference latency. It uses pattern-based
+region analysis to efficiently explore and optimize Q/DQ insertion strategies.
+"""
+
+# Core data structures
+from .benchmark import TensorRTPyBenchmark, TrtExecBenchmark
+from .common import (
+    AutotunerError,
+    AutotunerNotInitializedError,
+    InsertionScheme,
+    InvalidSchemeError,
+    Region,
+    RegionType,
+)
+from .insertion_points import (
+    ChildRegionInputInsertionPoint,
+    ChildRegionOutputInsertionPoint,
+    NodeInputInsertionPoint,
+    ResolvedInsertionPoint,
+)
+from .region_pattern import RegionPattern
+from .region_search import CombinedRegionSearch
+
+__all__ = [
+    "AutotunerError",
+    "AutotunerNotInitializedError",
+    "ChildRegionInputInsertionPoint",
+    "ChildRegionOutputInsertionPoint",
+    "CombinedRegionSearch",
+    "InsertionScheme",
+    "InvalidSchemeError",
+    "NodeInputInsertionPoint",
+    "Region",
+    "RegionPattern",
+    "RegionType",
+    "ResolvedInsertionPoint",
+    "TensorRTPyBenchmark",
+    "TrtExecBenchmark",
+]