[Performance] Performance regression in Clip operator for int64 inputs between v1.20.0 and v1.21.0

[junghyunpark2001]

Describe the issue

Description

We observed a performance regression in the Clip operator for int64 data type with only minimum bound specified (no maximum bound) between ONNXRuntime v1.20.0 and v1.21.0.

Affected Operator

Clip

• Opset Version: 13
• Data Type: int64
• Configuration: min bound only (no max bound)
• Input Shape: [2, 3, 32, 32] (4D tensor)
• Regression: Performance degradation observed

Test Case Details

Test Case: clip_13_v2_clip_int64_no_max

Input:

• X (input tensor):

  • Shape: [2, 3, 32, 32] (4D tensor)
  • Data type: int64
  • Total elements: 6,144

• min (minimum bound):

  • Shape: [] (scalar)
  • Data type: int64
  • Value: -100

• max (maximum bound):

  • Not provided (unbounded on upper end)

Output:

• Name: output
• Shape: [2, 3, 32, 32]
• Data type: int64

Operation:
Clips input values to be >= -100 (no upper bound).

Performance:

• v1.20.0: baseline
• v1.21.0: regression observed

Regression Characteristics

• Data type specific: int64 input and bounds
• Partial bounds: Only minimum bound specified (no maximum)
• Moderate tensor size: 6,144 elements (2×3×32×32)
• 4D input tensor: Standard tensor shape for vision-related operations

To reproduce

1. Download zip

2. Run benchmark using the provided script:

python profile_operator.py clip_13_v2_clip_int64_no_max 1.20.0 1.21.0

[Archive.zip](https://github.com/user-attachments/files/24695593/Archive.zip)

### Urgency

_No response_

### Platform

Linux

### OS Version

Ubuntu 24.04.3 LTS

### ONNX Runtime Installation

Released Package

### ONNX Runtime Version or Commit ID

1.21.0

### ONNX Runtime API

Python

### Architecture

X64

### Execution Provider

Default CPU

### Execution Provider Library Version

_No response_

### Model File

_No response_

### Is this a quantized model?

Yes