⚡️ Speed up method Usage.opentelemetry_attributes by 85%

[misrasaurabh1]

📄 85% (0.85x) speedup for Usage.opentelemetry_attributes in pydantic_ai_slim/pydantic_ai/usage.py

⏱️ Runtime : 2.09 milliseconds → 1.13 milliseconds (best of 77 runs)

📝 Explanation and details

Here is the optimized version of your program for maximal runtime efficiency and reduced memory allocations, especially for the bottleneck section.

Performance improvements:

• Avoided unconditional allocation and population of dictionary keys with possibly falsy values, which were later filtered out at return. This eliminates the need to create then filter unnecessary key-value pairs, and the dict comprehension at the end.
• Reuses the string prefix instead of f-strings in the tight loop for minor efficiency improvement.
• Now we only touch details if it's not None (no allocation of empty dict).
• No post-process dict comprehension; only desired keys added directly.

The function returns exactly the same result as the original, but avoids unnecessary work and memory churn.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 2004 Passed
⏪ Replay Tests	✅ 243 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

from collections import namedtuple
# function to test
from copy import copy
from dataclasses import dataclass
from typing import Optional

# imports
import pytest  # used for our unit tests
from pydantic_ai.usage import Usage


# Dummy _utils for repr to avoid import error
class _utils:
    @staticmethod
    def dataclasses_no_defaults_repr(self):
        return f"Usage(requests={self.requests}, request_tokens={self.request_tokens}, response_tokens={self.response_tokens}, total_tokens={self.total_tokens}, details={self.details})"
from pydantic_ai.usage import Usage

# unit tests

# Basic Test Cases





def test_empty_usage():
    # All fields are None or 0, should return empty dict
    usage = Usage()
    codeflash_output = usage.opentelemetry_attributes(); attrs = codeflash_output # 1.76μs -> 668ns (163% faster)

# Edge Test Cases

















from copy import copy
from dataclasses import dataclass

# imports
import pytest  # used for our unit tests
from pydantic_ai.usage import Usage


# Dummy _utils for repr to avoid dependency on pydantic_ai._utils
class _utils:
    @staticmethod
    def dataclasses_no_defaults_repr(self):
        return f"Usage({self.requests}, {self.request_tokens}, {self.response_tokens}, {self.total_tokens}, {self.details})"
from pydantic_ai.usage import Usage

# unit tests

# 1. Basic Test Cases











def test_edge_all_none():
    # All fields None or default
    usage = Usage()
    codeflash_output = usage.opentelemetry_attributes(); attrs = codeflash_output # 1.73μs -> 614ns (181% faster)









def test_large_scale_tokens_and_details_none():
    # All fields None, large number of Usage objects
    usages = [Usage() for _ in range(1000)]
    for usage in usages:
        codeflash_output = usage.opentelemetry_attributes() # 417μs -> 178μs (134% faster)



from pydantic_ai.usage import Usage

def test_Usage_opentelemetry_attributes():
    Usage.opentelemetry_attributes(Usage(requests=0, request_tokens=None, response_tokens=None, total_tokens=None, details={'': -1}))

def test_Usage_opentelemetry_attributes_2():
    Usage.opentelemetry_attributes(Usage(requests=0, request_tokens=None, response_tokens=0, total_tokens=None, details=None))

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
codeflash_concolic_t7q2nx26/tmpwofuw3vl/test_concolic_coverage.py::test_Usage_opentelemetry_attributes	2.32μs	1.41μs	✅65.2%
codeflash_concolic_t7q2nx26/tmpwofuw3vl/test_concolic_coverage.py::test_Usage_opentelemetry_attributes_2	1.81μs	691ns	✅163%
test_pytest_inlinesnapshotdisable_testsproviderstest_bedrock_py_testsproviderstest_google_gla_py_teststes__replay_test_0.py::test_pydantic_ai_usage_Usage_opentelemetry_attributes	138μs	82.8μs	✅67.6%
test_pytest_inlinesnapshotdisable_teststest_messages_py_teststest_mcp_py_teststest_deps_py__replay_test_0.py::test_pydantic_ai_usage_Usage_opentelemetry_attributes	9.00μs	4.72μs	✅90.4%

To edit these changes git checkout codeflash/optimize-Usage.opentelemetry_attributes-md0bn5hj and push.