⚡️ Speed up method LRUCache.get by 145%

[codeflash-ai]

📄 145% (1.45x) speedup for LRUCache.get in inference/core/cache/lru_cache.py

⏱️ Runtime : 5.29 microseconds → 2.16 microseconds (best of 27 runs)

📝 Explanation and details

The optimization achieves a 144% speedup by replacing an expensive try/except pattern with a more efficient conditional check for cache lookups.

Key optimizations applied:

1. Eliminated expensive .pop() operation: The original code used self.cache.pop(key) followed by self.cache[key] = value to move the key to the end of the OrderedDict. This approach requires removing and re-inserting the key-value pair, which is costly for OrderedDict operations.

2. Used .move_to_end() method: The optimized version leverages OrderedDict's built-in .move_to_end(key) method, which efficiently updates the ordering without removing and re-inserting the entry.

3. Replaced try/except with conditional check: Changed from exception handling (try/except KeyError) to a simple membership test (if key in self.cache), avoiding the overhead of exception creation and handling when keys are missing.

Performance impact analysis:

• The line profiler shows the original code spent 58.4% of time in the .pop() operation alone
• The optimized version reduces total execution time from 14.462μs to 6.319μs
• Cache miss scenarios (testing non-existent keys) show particularly strong improvements: 148-204% faster according to the annotated tests

Why this optimization works:
In Python, exception handling has significant overhead, especially when exceptions are frequently raised. For cache implementations where cache misses are common, avoiding KeyError exceptions provides substantial performance benefits. Additionally, OrderedDict's .move_to_end() is specifically optimized for LRU cache patterns, making it much more efficient than manual pop/reassign operations.

This optimization is especially valuable for cache-heavy workloads where frequent lookups of missing keys occur, as evidenced by the strong performance gains in the "empty cache" and "missing key" test scenarios.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 124 Passed
🌀 Generated Regression Tests	✅ 6 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	66.7%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
inference/unit_tests/usage_tracking/test_collector.py::test_record_usage_with_exception_on_GCP	349ns	348ns	0.287%✅

🌀 Generated Regression Tests and Runtime

import collections
import random
import string

# imports
import pytest
from inference.core.cache.lru_cache import LRUCache

# unit tests

# ----------------------------
# Basic Test Cases
# ----------------------------


def test_get_on_empty_cache_returns_none():
    # Test that get returns None when cache is empty
    cache = LRUCache(2)
    codeflash_output = cache.get("x")  # 2.02μs -> 665ns (204% faster)

import collections

# imports
import pytest
from inference.core.cache.lru_cache import LRUCache

# unit tests

# ---------------------------
# Basic Test Cases
# ---------------------------


def test_get_non_existing_key_returns_none():
    # Test that get returns None for a missing key
    cache = LRUCache(capacity=2)
    codeflash_output = cache.get("missing")  # 1.56μs -> 598ns (160% faster)


def test_get_on_empty_cache():
    # Test get on a cache that has never had any items
    cache = LRUCache(capacity=2)
    codeflash_output = cache.get("foo")  # 1.36μs -> 550ns (148% faster)

To edit these changes git checkout codeflash/optimize-LRUCache.get-miqoz0o7 and push.