Integrate Automated QDQ placement tool - Part 1

[willg-nv]

What does this PR do?

Type of change: new feature

Overview: This PR integrates an automatical QDQ placment tool into ModelOpt.

This PR is the 1/4 parts of the change, it contains the following changes:

1. Defines common types: Region, RegionType, Error types
2. Defines InsertionPoints (the logical localtion to place QDQ pairs), InsertionScheme (a set of insertion points)
3. Unit tests for new types

Part 1: #701
Part 2: #702
Part 3: #703
Part 4: #704

Usage

        # Region type usage:
        region = Region(region_id=1, level=0, region_type=RegionType.LEAF)
        assert region.get_id() == 1
        assert region.get_level() == 0
        region.add_node(1) # 1 is the index of ONNX graph node
        ...

        point = NodeInputInsertionPoint(node_index=0, input_index=2)
        assert point.node_index == 0 # relative node index in region
        assert point.input_index == 2 # relative input tensor index in specific node
        resolved = point.resolve(region, graph)
        ...

Testing

Implement unit tests, all tests could get passed.

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?: No, document change will be included in part 4.
• Did you update Changelog?: No, this could be done when all parts of the change are merged.

Additional Information

Summary by CodeRabbit

• New Features

  • Added foundational autotuner infrastructure for quantization optimization, including region hierarchies and insertion scheme management.
  • Introduced insertion point system for managing quantize/dequantize operation placement across ONNX graph regions.
  • Added utility functions for tensor consumer mapping and boolean operation identification.

• Tests

  • Added comprehensive test coverage for autotuner components, insertion points, and region management.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

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[willg-nv]

Hi @gcunhase, could you help me review this PR? thanks!

[ajrasane]

LGTM from my side. Will wait for @gcunhase review.

[gcunhase]

LGTM, added a few comments, thanks.

[ajrasane]

LGTM

[gcunhase]

LGTM, other changes can be done in #702.

[codecov]

Codecov Report

❌ Patch coverage is 91.28065% with 32 lines in your changes missing coverage. Please review.
✅ Project coverage is 74.16%. Comparing base (81b67dd) to head (09a91a8).
⚠️ Report is 4 commits behind head on main.

Files with missing lines	Patch %	Lines
modelopt/onnx/quantization/autotune/common.py	81.81%	24 Missing ⚠️
...opt/onnx/quantization/autotune/insertion_points.py	96.61%	7 Missing ⚠️
modelopt/onnx/quantization/graph_utils.py	92.30%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #701      +/-   ##
==========================================
+ Coverage   73.82%   74.16%   +0.33%     
==========================================
  Files         193      195       +2     
  Lines       19745    20111     +366     
==========================================
+ Hits        14577    14915     +338     
- Misses       5168     5196      +28     

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[gcunhase]

/ok to test 09a91a8