Add int8/uint8 support for Max and Min operators

[snnn (snnn)]

Description

This PR adds support for int8, uint8, int16, and uint16 data types to the Max and Min operators for opset 12 and later, resolving issue #26382.

Issue

Fixes #26382

Users encountered the error Could not find an implementation for Max(13) node with name '_0' when trying to use Max or Min operators with int8 or uint8 data types, even though these types are required by the ONNX specification for opset 12+.

Changes

Core Implementation (onnxruntime/core/providers/cpu/math/element_wise_ops.cc)

1. Updated type registration for Max and Min opset 12: Added int8_t, int16_t, uint8_t, and uint16_t to the supported type lists
2. Updated type dispatchers: Modified Min_8::Compute and Max_8::Compute to dispatch to implementations for the new types

Tests (onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc)

Added comprehensive unit tests for all new data types:

• Max_12_Int8, Max_12_UInt8, Max_12_Int16, Max_12_UInt16
• Min_12_Int8, Min_12_UInt8, Min_12_Int16, Min_12_UInt16

Each test verifies:

• Element-wise operations
• Broadcasting functionality
• Multiple input handling
• Appropriate value ranges for each type

Technical Details

• The existing Eigen-based implementation already handles all numeric types generically
• No changes to computation logic were needed
• Only type registration and dispatching were updated
• Changes follow existing code patterns in the codebase

ONNX Specification Compliance

This implementation now fully complies with the ONNX specification for Max and Min operators at opset 12+, which requires support for:

• tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64)
• tensor(int8), tensor(int16), tensor(int32), tensor(int64)
• tensor(float16), tensor(float), tensor(double), tensor(bfloat16)

Impact

• ✅ Enables int8/uint8 quantized models using Max/Min operators
• ✅ Improves ONNX specification compliance
• ✅ Fully backward compatible
• ✅ No breaking changes or performance impact

Testing

./onnxruntime_test_all --gtest_filter="MathOpTest.Max_12_*8*:MathOpTest.Min_12_*8*:MathOpTest.Max_12_*16*:MathOpTest.Min_12_*16*"

Checklist

• I have read the contributing guidelines
• Reference issue is linked (Fixes Support int8 and uint8 data types for Max and Min #26382)
• Added comprehensive unit tests
• Changes follow existing code patterns
• Minimal and surgical changes (12 lines modified + 136 lines of tests)

[snnn (snnn)]

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[github-actions]

You can commit the suggested changes from lintrunner.

[Scott McKay (skottmckay)]

Do any real models need the 16-bit types? Typically we only add required types to keep binary size growth minimal.

[snnn (snnn)]

Wanming Lin (@Honry) , could you help answer this question?

[Wanming Lin (Honry)]

The issue is captured when I tested Max and Min unit tests of 8-bit types with WebNN EP.

Scott McKay (@skottmckay), Do you specify the non-necessity of supporting 16-bit types? That'd be fine to me.

[snnn (snnn)]

Scott McKay (@skottmckay), we have a pipeline to monitor binary size, and there is no change for Android minimal build's binary size. There should be some increases in the default build, but it should be fine?

[Scott McKay (skottmckay)]

it's still code that we have to test and maintain, and if there's no real usage of it that seems to be of limited value.