Constraints and Precautions for MindStudio Kernel Performance Prediction

Development Constraints

When using the msKPP library to implement operator simulation, pay attention to the following points:
- Before modeling a simulation operator, import Tensor, Chip, and instructions (in lowercase) required for operator implementation from the msKPP library.
- Refer to the sample sample_vadd.py or sample_mmad.py in the project. Use the with statement to enable the entry of the operator implementation code. The enable_trace and enable_metrics APIs can enable the trace dotting and instruction statistics functions.

Performance modeling results depend on time estimation based on input/output scales. No actual computation is performed, and the results are for peak performance reference only.
To generate the instruction proportion pie chart (instruction_cycle_consumption.html), the third-party Python library plotly must be installed in advance:
```
pip3 install plotly
```

Ensure that the input data is reliable and secure during secondary development.
The tool involves dynamic Python module loading during runtime. Ensure that dependency libraries in the runtime environment are from trusted sources to avoid arbitrary code injection risks.