Integrate Automated QDQ benchmark - part 3.1 (#837)

## What does this PR do?

This PR integrates benchmark module to QDQ autotunner. This benchamrk
module is used to evaluate ONNX model perf.
This PR is 1/3 of #703.
Once all small PRs are merged #703 could be closed.

PR 3.1: #837
PR 3.2 #838
PR 3.3: #839

## Testing
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## Before your PR is "*Ready for review*"
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- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No, document
will be added in part 4.
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No, change log will be updated when all changes are merged.
## Additional Information
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<!-- This is an auto-generated comment: release notes by coderabbit.ai
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## Summary by CodeRabbit

* **New Features**
* Added ONNX quantization autotuning capabilities with a consolidated
module providing streamlined import paths for core components.
* Introduced unified benchmarking framework supporting TensorRT-based
model evaluation with both command-line and Python API implementations.
* Added support for timing cache persistence, custom plugin libraries,
shape validation, and dynamic input shape configuration for flexible
model testing and optimization.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

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

Author: willg-nv

modelopt/onnx/quantization/autotune/__init__.py

@@ -0,0 +1,57 @@
+# 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",
+]