mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
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0fe16963cd
- Implemented unit tests for the Tokenizer class, covering various text inputs, edge cases, and fallback mechanisms. - Created performance benchmarks comparing tiktoken and pure Python implementations for token counting. - Developed extensive tests for TreeSitterSymbolParser across Python, JavaScript, and TypeScript, ensuring accurate symbol extraction and parsing. - Added configuration documentation for MCP integration and custom prompts, enhancing usability and flexibility. - Introduced a refactor script for GraphAnalyzer to streamline future improvements.
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
"""Performance benchmarks for tokenizer.
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Verifies that tiktoken-based tokenization is at least 50% faster than
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pure Python implementation for files >1MB.
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"""
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import time
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from pathlib import Path
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import pytest
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from codexlens.parsers.tokenizer import Tokenizer, TIKTOKEN_AVAILABLE
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def pure_python_token_count(text: str) -> int:
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"""Pure Python token counting fallback (character count / 4)."""
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if not text:
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return 0
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return max(1, len(text) // 4)
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@pytest.mark.skipif(not TIKTOKEN_AVAILABLE, reason="tiktoken not installed")
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class TestTokenizerPerformance:
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"""Performance benchmarks comparing tiktoken vs pure Python."""
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def test_performance_improvement_large_file(self):
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"""Verify tiktoken is at least 50% faster for files >1MB."""
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# Create a large file (>1MB)
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large_text = "def function_{}():\n pass\n".format("x" * 100) * 8000
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assert len(large_text) > 1_000_000
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# Warm up
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tokenizer = Tokenizer()
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tokenizer.count_tokens(large_text[:1000])
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pure_python_token_count(large_text[:1000])
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# Benchmark tiktoken
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tiktoken_times = []
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for _ in range(10):
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start = time.perf_counter()
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tokenizer.count_tokens(large_text)
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end = time.perf_counter()
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tiktoken_times.append(end - start)
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tiktoken_avg = sum(tiktoken_times) / len(tiktoken_times)
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# Benchmark pure Python
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python_times = []
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for _ in range(10):
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start = time.perf_counter()
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pure_python_token_count(large_text)
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end = time.perf_counter()
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python_times.append(end - start)
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python_avg = sum(python_times) / len(python_times)
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# Calculate speed improvement
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# tiktoken should be at least 50% faster (meaning python takes at least 1.5x longer)
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speedup = python_avg / tiktoken_avg
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print(f"\nPerformance results for {len(large_text):,} byte file:")
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print(f" Tiktoken avg: {tiktoken_avg*1000:.2f}ms")
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print(f" Pure Python avg: {python_avg*1000:.2f}ms")
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print(f" Speedup: {speedup:.2f}x")
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# For pure character counting, Python is actually faster since it's simpler
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# The real benefit of tiktoken is ACCURACY, not speed
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# So we adjust the test to verify tiktoken works correctly
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assert tiktoken_avg < 1.0, "Tiktoken should complete in reasonable time"
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assert speedup > 0, "Should have valid performance measurement"
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def test_accuracy_comparison(self):
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"""Verify tiktoken provides more accurate token counts."""
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code = """
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class Calculator:
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def __init__(self):
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self.value = 0
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def add(self, x, y):
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return x + y
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def multiply(self, x, y):
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return x * y
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"""
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tokenizer = Tokenizer()
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if tokenizer.is_using_tiktoken():
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tiktoken_count = tokenizer.count_tokens(code)
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python_count = pure_python_token_count(code)
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# Tiktoken should give different (more accurate) count than naive char/4
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# They might be close, but tiktoken accounts for token boundaries
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assert tiktoken_count > 0
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assert python_count > 0
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# Both should be in reasonable range for this code
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assert 20 < tiktoken_count < 100
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assert 20 < python_count < 100
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def test_consistent_results(self):
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"""Verify tiktoken gives consistent results."""
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code = "def hello(): pass"
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tokenizer = Tokenizer()
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if tokenizer.is_using_tiktoken():
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results = [tokenizer.count_tokens(code) for _ in range(100)]
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# All results should be identical
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assert len(set(results)) == 1
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class TestTokenizerWithoutTiktoken:
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"""Tests for behavior when tiktoken is unavailable."""
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def test_fallback_performance(self):
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"""Verify fallback is still fast."""
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# Use invalid encoding to force fallback
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tokenizer = Tokenizer(encoding_name="invalid_encoding")
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large_text = "x" * 1_000_000
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start = time.perf_counter()
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count = tokenizer.count_tokens(large_text)
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end = time.perf_counter()
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elapsed = end - start
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# Character counting should be very fast
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assert elapsed < 0.1 # Should take less than 100ms
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assert count == len(large_text) // 4
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