This release is a pre-release and may not be stable for production use.
CUTracer Python Module
Python tools for CUTracer trace validation, parsing, and analysis.
Overview
The cutracer Python package provides a comprehensive framework for working with CUTracer trace files. This module is designed to be:
- Reusable: Import and use in your own Python scripts
- Testable: Full unittest suite with real trace data
- Type-safe: Type hints and mypy compatibility
- Extensible: Plugin architecture for future enhancements
Installation
For Development
cd /path/to/CUTracer/python
pip install -e ".[dev]"
For Production Use
cd /path/to/CUTracer/python
pip install .
Features
Trace Validation (Current)
- JSON Validation: Validate NDJSON trace files (mode 2) for syntax and schema compliance
- Text Validation: Validate text-format trace files (mode 0) for format compliance
- Cross-Format Consistency: Compare different trace formats for data consistency
Planned Features
- Trace Parsing: Parse trace files into structured Python objects
- Analysis Tools: Instruction histograms, performance metrics, trace comparison
- Format Conversion: Convert between different trace formats
- Compression Support: Handle zstd-compressed traces (mode 1)
Usage
Python API
from cutracer.validation import (
validate_json_trace,
validate_text_trace,
compare_trace_formats,
)
# Validate JSON trace
result = validate_json_trace("kernel_trace.ndjson")
if result["valid"]:
print(f"✓ Valid JSON trace with {result['record_count']} records")
else:
print(f"✗ Validation failed: {result['errors']}")
# Validate text trace
result = validate_text_trace("kernel_trace.log")
if result["valid"]:
print(f"✓ Valid text trace")
else:
print(f"✗ Validation failed: {result['errors']}")
# Compare two formats
result = compare_trace_formats("kernel_trace.log", "kernel_trace.ndjson")
if result["consistent"]:
print("✓ Formats are consistent")
else:
print(f"✗ Inconsistencies found: {result['differences']}")
Module Structure
python/
├── cutracer/ # Main package
│ ├── __init__.py # Package entry point with version
│ └── validation/ # Validation framework
│ ├── __init__.py # Validation API exports
│ ├── schema_loader.py # JSON Schema loader
│ ├── json_validator.py # JSON syntax & schema validation
│ ├── text_validator.py # Text format validation
│ ├── consistency.py # Cross-format consistency checks
│ └── schemas/ # JSON Schema definitions
│ ├── __init__.py
│ ├── reg_trace.schema.json
│ ├── mem_trace.schema.json
│ ├── opcode_only.schema.json
│ └── delay_config.schema.json
├── tests/ # Unit tests
│ ├── __init__.py
│ ├── test_base.py # Base test class and utilities
│ ├── test_schemas.py # Schema loading tests
│ ├── test_json_validator.py # JSON validation tests
│ ├── test_text_validator.py # Text validation tests
│ ├── test_consistency.py # Consistency check tests
│ └── example_inputs/ # Real trace data for tests
│ ├── reg_trace_sample.ndjson
│ ├── reg_trace_sample.log
│ ├── invalid_syntax.ndjson
│ └── invalid_schema.ndjson
├── pyproject.toml # Modern Python project config
└── README.md # This file
Development
Running Tests
cd python/
# Run all tests
python -m unittest discover -s tests -v
# Run specific test file
python -m unittest tests.test_json_validator -v
Type Checking
cd python/
mypy cutracer/
Code Formatting
# From project root directory
./format.sh format
# Or manually with ufmt
ufmt format python/
usort format python/
Running All Checks
# Format code
./format.sh format
# Type check
mypy cutracer/
# Run tests
python -m unittest discover -s tests -v
Validation Details
JSON Trace Validation
The JSON validator checks:
- Syntax: Valid JSON format on each line (NDJSON)
- Schema: Correct field types and structure per JSON Schema
- Required Fields:
message_type,ctx,kernel_launch_id,trace_index,timestamp,sass, etc. - Register Values: Arrays of integers with proper format
- CTA/Warp IDs: Valid integer ranges
Text Trace Validation
The text validator checks:
- Format Patterns: Correct CTX/CTA/warp header patterns
- Register Output: Proper hex format (e.g.,
Reg0_T00: 0x...) - Memory Access: Valid memory address patterns
Consistency Validation
The consistency validator compares:
- Record Counts: Same number of records in both formats
- Content Matching: Same kernel IDs, trace indices, SASS strings
- Timestamp Order: Consistent ordering between formats
Trace Format Reference
JSON Format (NDJSON - Mode 2)
Each line is a JSON object with the following structure:
{
"message_type": "reg_trace",
"ctx": "0x58a0c0",
"kernel_launch_id": 0,
"trace_index": 0,
"timestamp": 1762026820167834792,
"sass": "LDC R1, c[0x0][0x28] ;",
"pc": 0,
"opcode_id": 0,
"warp": 0,
"cta": [0, 0, 0],
"regs": [[0, 0, 0, ...]]
}
Text Format (Mode 0)
Human-readable format with CTX headers and register values:
CTX 0x58a0c0 - CTA 0,0,0 - warp 0 - LDC R1, c[0x0][0x28] ;:
* Reg0_T00: 0x0000000000000000 Reg0_T01: 0x0000000000000000 ...
Contributing
- Install development dependencies:
pip install -e ".[dev]" - Make your changes
- Run tests:
python -m unittest discover -s tests -v - Run type checker:
mypy cutracer/ - Format code:
./format.sh format - Submit a pull request
License
MIT License - See LICENSE file for details.
Support
For issues and questions, please open an issue on the CUTracer GitHub repository.
1 maintainer
Release files for cutracer 0.1.1.dev20260304073157
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