⚡️ Speed up method `AiServiceClient.optimize_python_code_refinement` by 115% in PR #962 (`limit-refined-candidates`) by codeflash-ai[bot] · Pull Request #963 · codeflash-ai/codeflash

codeflash-ai · 2025-12-10T14:56:40Z

⚡️ This pull request contains optimizations for PR #962

If you approve this dependent PR, these changes will be merged into the original PR branch limit-refined-candidates.

This PR will be automatically closed if the original PR is merged.

📄 115% (1.15x) speedup for `AiServiceClient.optimize_python_code_refinement` in `codeflash/api/aiservice.py`

⏱️ Runtime : 32.8 milliseconds → 15.3 milliseconds (best of 65 runs)

📝 Explanation and details

The optimized code achieves a 114% speedup through two key optimizations:

1. humanize_runtime Function Rewrite (Major Impact)
The original implementation was heavily bottlenecked by calling humanize.precisedelta() and regex parsing for every time conversion ≥1000ns (73.6% of function time). The optimized version:

Uses direct arithmetic conversions with simple conditional branches for different time units
Eliminates expensive datetime.timedelta construction and re.split() operations
Provides fast-path handling for common small values (<1000ns) without any external library calls
Results in ~15x faster execution for humanize_runtime (83ms → 5ms total time)

2. _get_valid_candidates Loop Optimization (Secondary Impact)
Replaced the traditional for-loop with append/continue pattern with a list comprehension using walrus operator:

Eliminates per-iteration list.append() overhead
Improves memory locality and reduces function call overhead
Leverages Python's optimized list comprehension implementation
Results in ~4% improvement for this method (43ms → 41ms)

Impact on Workloads:
Based on the test results, the optimization is particularly effective for:

Large-scale processing: 732% speedup on 500-candidate batches, 312-385% speedup on multiple candidate scenarios
High-frequency time formatting: Since humanize_runtime is called twice per refinement request, the 15x improvement compounds significantly
Error handling paths: 16-22% improvements even in error scenarios due to faster payload construction

The optimizations maintain identical functionality while dramatically reducing computational overhead, making them especially valuable for batch processing workflows where these functions are called repeatedly.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 46 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	84.2%

🌀 Generated Regression Tests and Runtime

# imports
from codeflash.api.aiservice import AiServiceClient


class AIServiceRefinerRequest:
    def __init__(
        self,
        optimization_id,
        original_source_code,
        read_only_dependency_code,
        original_line_profiler_results,
        original_code_runtime,
        optimized_source_code,
        optimized_explanation,
        optimized_line_profiler_results,
        optimized_code_runtime,
        speedup,
        trace_id,
        function_references,
    ):
        self.optimization_id = optimization_id
        self.original_source_code = original_source_code
        self.read_only_dependency_code = read_only_dependency_code
        self.original_line_profiler_results = original_line_profiler_results
        self.original_code_runtime = original_code_runtime
        self.optimized_source_code = optimized_source_code
        self.optimized_explanation = optimized_explanation
        self.optimized_line_profiler_results = optimized_line_profiler_results
        self.optimized_code_runtime = optimized_code_runtime
        self.speedup = speedup
        self.trace_id = trace_id
        self.function_references = function_references


# --------------------- UNIT TESTS ---------------------


# Helper for generating a valid AIServiceRefinerRequest
def make_refiner_request(
    optimization_id="opt1234abcd",
    original_source_code="print('hello')",
    read_only_dependency_code="def dep(): pass",
    original_line_profiler_results="func1 0.1s",
    original_code_runtime=100000,
    optimized_source_code="print('hi')",
    optimized_explanation="Shorter print",
    optimized_line_profiler_results="func1 0.05s",
    optimized_code_runtime=50000,
    speedup=2.0,
    trace_id="trace123",
    function_references=["func1"],
):
    return AIServiceRefinerRequest(
        optimization_id=optimization_id,
        original_source_code=original_source_code,
        read_only_dependency_code=read_only_dependency_code,
        original_line_profiler_results=original_line_profiler_results,
        original_code_runtime=original_code_runtime,
        optimized_source_code=optimized_source_code,
        optimized_explanation=optimized_explanation,
        optimized_line_profiler_results=optimized_line_profiler_results,
        optimized_code_runtime=optimized_code_runtime,
        speedup=speedup,
        trace_id=trace_id,
        function_references=function_references,
    )


# Basic Test Cases


def test_refinement_success_single_candidate(monkeypatch):
    """Test basic success scenario with one candidate."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    # Mock the API response
    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "```file.py\ncode here\n```",
                        "explanation": "Refined explanation",
                        "optimization_id": "opt1234abcd",
                    }
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output
    candidate = result[0]


def test_refinement_success_multiple_candidates(monkeypatch):
    """Test basic success scenario with multiple candidates."""
    client = AiServiceClient()
    req = [make_refiner_request(optimization_id=f"opt{i:04d}abcd") for i in range(3)]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "```file.py\ncode here\n```",
                        "explanation": f"Refined explanation {i}",
                        "optimization_id": f"opt{i:04d}abcd",
                    }
                    for i in range(3)
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 171μs -> 35.4μs (385% faster)
    for i, candidate in enumerate(result):
        pass


def test_refinement_empty_candidates(monkeypatch):
    """Test basic scenario: empty input list should return empty result."""
    client = AiServiceClient()
    req = []

    class MockResponse:
        status_code = 200

        def json(self):
            return {"refinements": []}

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 3.12μs -> 3.17μs (1.86% slower)


# Edge Test Cases


def test_refinement_api_error(monkeypatch):
    """Test API error (non-200 status code) returns empty list."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    class MockResponse:
        status_code = 500

        def json(self):
            return {"error": "Internal Server Error"}

        text = "Internal Server Error"

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 919μs -> 754μs (21.9% faster)


def test_refinement_api_exception(monkeypatch):
    """Test network/API exception returns empty list."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    def raise_exception(*a, **kw):
        raise Exception("Network error")

    monkeypatch.setattr(client, "make_ai_service_request", raise_exception)
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output


def test_refinement_no_code_block(monkeypatch):
    """Test edge case: API returns a refinement with no code block, should be skipped."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {"source_code": "NO_CODE_BLOCK", "explanation": "No code block", "optimization_id": "opt1234abcd"}
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 88.7μs -> 21.5μs (312% faster)


def test_refinement_malformed_json(monkeypatch):
    """Test edge case: API returns malformed JSON, should return empty list."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    class MockResponse:
        status_code = 200

        def json(self):
            raise ValueError("Malformed JSON")

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output


def test_refinement_missing_fields(monkeypatch):
    """Test edge case: API returns refinement missing required fields, should skip."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        # Missing source_code and explanation
                        "optimization_id": "opt1234abcd"
                    }
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    # Should skip this candidate, result should be empty
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output


def test_refinement_non_string_fields(monkeypatch):
    """Test edge case: API returns refinement with non-string fields."""
    client = AiServiceClient()
    req = [make_refiner_request()]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": 12345,  # Not a string
                        "explanation": None,  # Not a string
                        "optimization_id": "opt1234abcd",
                    }
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    # Should skip this candidate, result should be empty
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output


def test_refinement_optimization_id_short(monkeypatch):
    """Test edge case: optimization_id too short to slice, should not crash."""
    client = AiServiceClient()
    req = [make_refiner_request(optimization_id="abc")]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "```file.py\ncode here\n```",
                        "explanation": "Refined explanation",
                        "optimization_id": "abc",
                    }
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 107μs -> 23.4μs (361% faster)


# Large Scale Test Cases


def test_refinement_large_input(monkeypatch):
    """Test large scale: 500 candidates input, should process all."""
    client = AiServiceClient()
    N = 500
    req = [make_refiner_request(optimization_id=f"opt{i:04d}abcd") for i in range(N)]

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "```file.py\ncode here\n```",
                        "explanation": f"Refined explanation {i}",
                        "optimization_id": f"opt{i:04d}abcd",
                    }
                    for i in range(N)
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 17.6ms -> 2.11ms (732% faster)
    for i, candidate in enumerate(result):
        pass


def test_refinement_large_output(monkeypatch):
    """Test large scale: API returns 999 refinements, should process all."""
    client = AiServiceClient()
    N = 999
    req = [make_refiner_request(optimization_id=f"opt{i:04d}abcd") for i in range(10)]  # input 10, output 999

    class MockResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "```file.py\ncode here\n```",
                        "explanation": f"Refined explanation {i}",
                        "optimization_id": f"opt{i:04d}abcd",
                    }
                    for i in range(N)
                ]
            }

    monkeypatch.setattr(client, "make_ai_service_request", lambda *a, **kw: MockResponse())
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 3.35ms -> 2.99ms (11.9% faster)
    for i, candidate in enumerate(result):
        pass


def test_refinement_payload_fields(monkeypatch):
    """Test that all payload fields are correctly constructed and sent."""
    client = AiServiceClient()
    req = [make_refiner_request()]
    captured_payload = {}

    def mock_make_ai_service_request(endpoint, method="POST", payload=None, timeout=None):
        captured_payload["payload"] = payload

        class MockResponse:
            status_code = 200

            def json(self):
                return {
                    "refinements": [
                        {
                            "source_code": "```file.py\ncode here\n```",
                            "explanation": "Refined explanation",
                            "optimization_id": "opt1234abcd",
                        }
                    ]
                }

        return MockResponse()

    monkeypatch.setattr(client, "make_ai_service_request", mock_make_ai_service_request)
    client.optimize_python_code_refinement(req)  # 86.8μs -> 29.6μs (193% faster)
    payload = captured_payload["payload"]
    item = payload[0]
    # Check all expected fields are present
    expected_fields = [
        "optimization_id",
        "original_source_code",
        "read_only_dependency_code",
        "original_line_profiler_results",
        "original_code_runtime",
        "optimized_source_code",
        "optimized_explanation",
        "optimized_line_profiler_results",
        "optimized_code_runtime",
        "speedup",
        "trace_id",
        "function_references",
        "python_version",
    ]
    for field in expected_fields:
        pass


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

# Patch/mocks for external dependencies

# imports
from codeflash.api.aiservice import AiServiceClient


class DummyAIServiceRefinerRequest:
    def __init__(
        self,
        optimization_id,
        original_source_code,
        read_only_dependency_code,
        original_line_profiler_results,
        original_code_runtime,
        optimized_source_code,
        optimized_explanation,
        optimized_line_profiler_results,
        optimized_code_runtime,
        speedup,
        trace_id,
        function_references,
    ):
        self.optimization_id = optimization_id
        self.original_source_code = original_source_code
        self.read_only_dependency_code = read_only_dependency_code
        self.original_line_profiler_results = original_line_profiler_results
        self.original_code_runtime = original_code_runtime
        self.optimized_source_code = optimized_source_code
        self.optimized_explanation = optimized_explanation
        self.optimized_line_profiler_results = optimized_line_profiler_results
        self.optimized_code_runtime = optimized_code_runtime
        self.speedup = speedup
        self.trace_id = trace_id
        self.function_references = function_references


# ========== BASIC TEST CASES ==========


def test_single_valid_candidate(monkeypatch):
    """Test a single valid optimization refinement returns the correct candidate."""
    client = AiServiceClient()

    # Simulate API response
    class DummyResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {"source_code": "print('Hello')", "explanation": "Optimized print", "optimization_id": "abc123xyz0"}
                ]
            }

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)

    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="abc123xyz0",
            original_source_code="print('Hello')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="print('Hello')",
            optimized_explanation="Optimized print",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 19.2μs -> 16.8μs (14.3% faster)


def test_multiple_candidates(monkeypatch):
    """Test multiple valid candidates are returned and ids are mapped correctly."""
    client = AiServiceClient()

    class DummyResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {"source_code": "print('A')", "explanation": "A", "optimization_id": "id1abcd"},
                    {"source_code": "print('B')", "explanation": "B", "optimization_id": "id2efgh"},
                ]
            }

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)

    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="print('A')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="print('A')",
            optimized_explanation="A",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        ),
        DummyAIServiceRefinerRequest(
            optimization_id="id2efgh",
            original_source_code="print('B')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=200,
            optimized_source_code="print('B')",
            optimized_explanation="B",
            optimized_line_profiler_results="",
            optimized_code_runtime=100,
            speedup=2.0,
            trace_id="trace2",
            function_references=["main"],
        ),
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 24.0μs -> 21.4μs (12.5% faster)


def test_empty_input_returns_empty(monkeypatch):
    """Test that an empty input list returns an empty list."""
    client = AiServiceClient()

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        # Should not be called for empty input, but if it is, return empty
        class DummyResponse:
            status_code = 200

            def json(self):
                return {"refinements": []}

        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    codeflash_output = client.optimize_python_code_refinement([])
    result = codeflash_output  # 13.1μs -> 13.1μs (0.305% slower)


# ========== EDGE TEST CASES ==========


def test_api_returns_no_code_block(monkeypatch):
    """Test that if the API returns a refinement with no code block, it is skipped."""
    client = AiServiceClient()

    class DummyResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "NO_CODE",  # Will be parsed as empty
                        "explanation": "No code",
                        "optimization_id": "id1abcd",
                    }
                ]
            }

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="NO_CODE",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="NO_CODE",
            optimized_explanation="No code",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 17.9μs -> 16.0μs (11.4% faster)


def test_api_returns_400(monkeypatch):
    """Test that if the API returns a 400 error, the function returns empty and logs error."""
    client = AiServiceClient()

    class DummyResponse:
        status_code = 400

        def json(self):
            return {"error": "Bad request"}

        text = "Bad request"

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="print('A')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="print('A')",
            optimized_explanation="A",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 924μs -> 795μs (16.2% faster)


def test_make_ai_service_request_raises(monkeypatch):
    """Test that if make_ai_service_request raises an exception, function returns empty."""
    client = AiServiceClient()

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        raise RuntimeError("network error")

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="print('A')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="print('A')",
            optimized_explanation="A",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output


def test_missing_error_field(monkeypatch):
    """Test that if the error field is missing in error response, falls back to response.text."""
    client = AiServiceClient()

    class DummyResponse:
        status_code = 500

        def json(self):
            raise ValueError("No JSON")

        text = "Internal Server Error"

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="print('A')",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code="print('A')",
            optimized_explanation="A",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 857μs -> 761μs (12.5% faster)


def test_payload_fields_are_serialized(monkeypatch):
    """Test that all payload fields are serialized and passed to the API."""
    client = AiServiceClient()
    called = {}

    class DummyResponse:
        status_code = 200

        def json(self):
            return {"refinements": []}

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        called["payload"] = payload
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id="id1abcd",
            original_source_code="print('A')",
            read_only_dependency_code="dep code",
            original_line_profiler_results="profile",
            original_code_runtime=123,
            optimized_source_code="print('A opt')",
            optimized_explanation="faster",
            optimized_line_profiler_results="profile opt",
            optimized_code_runtime=45,
            speedup=2.7,
            trace_id="trace1",
            function_references=["main"],
        )
    ]
    client.optimize_python_code_refinement(req)  # 8.03μs -> 6.36μs (26.2% faster)
    payload = called["payload"][0]


# ========== LARGE SCALE TEST CASES ==========


def test_large_batch(monkeypatch):
    """Test handling of a large batch of requests (up to 1000)."""
    client = AiServiceClient()
    N = 1000

    class DummyResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {"source_code": f"print({i})", "explanation": f"exp{i}", "optimization_id": f"id{i:04d}abcd"}
                    for i in range(N)
                ]
            }

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id=f"id{i:04d}abcd",
            original_source_code=f"print({i})",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code=f"print({i})",
            optimized_explanation=f"exp{i}",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id=f"trace{i}",
            function_references=["main"],
        )
        for i in range(N)
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 4.37ms -> 3.86ms (13.2% faster)
    # Check a few random indices
    for idx in [0, 10, 500, 999]:
        pass


def test_large_batch_with_some_invalid(monkeypatch):
    """Test large batch with some refinements containing no code blocks (should be skipped)."""
    client = AiServiceClient()
    N = 1000

    class DummyResponse:
        status_code = 200

        def json(self):
            return {
                "refinements": [
                    {
                        "source_code": "NO_CODE" if i % 10 == 0 else f"print({i})",
                        "explanation": f"exp{i}",
                        "optimization_id": f"id{i:04d}abcd",
                    }
                    for i in range(N)
                ]
            }

    def dummy_make_ai_service_request(endpoint, payload=None, **kwargs):
        return DummyResponse()

    monkeypatch.setattr(client, "make_ai_service_request", dummy_make_ai_service_request)
    req = [
        DummyAIServiceRefinerRequest(
            optimization_id=f"id{i:04d}abcd",
            original_source_code=f"print({i})",
            read_only_dependency_code="",
            original_line_profiler_results="",
            original_code_runtime=100,
            optimized_source_code=f"print({i})",
            optimized_explanation=f"exp{i}",
            optimized_line_profiler_results="",
            optimized_code_runtime=50,
            speedup=2.0,
            trace_id=f"trace{i}",
            function_references=["main"],
        )
        for i in range(N)
    ]
    codeflash_output = client.optimize_python_code_refinement(req)
    result = codeflash_output  # 4.30ms -> 3.84ms (12.1% faster)
    # Check that none of the returned candidates have a code string "NO_CODE"
    for cand in result:
        pass


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr962-2025-12-10T14.56.33 and push.

The optimized code achieves a **114% speedup** through two key optimizations: **1. `humanize_runtime` Function Rewrite (Major Impact)** The original implementation was heavily bottlenecked by calling `humanize.precisedelta()` and regex parsing for every time conversion ≥1000ns (73.6% of function time). The optimized version: - Uses **direct arithmetic conversions** with simple conditional branches for different time units - Eliminates expensive `datetime.timedelta` construction and `re.split()` operations - Provides **fast-path handling** for common small values (<1000ns) without any external library calls - Results in ~15x faster execution for `humanize_runtime` (83ms → 5ms total time) **2. `_get_valid_candidates` Loop Optimization (Secondary Impact)** Replaced the traditional for-loop with append/continue pattern with a **list comprehension using walrus operator**: - Eliminates per-iteration list.append() overhead - Improves memory locality and reduces function call overhead - Leverages Python's optimized list comprehension implementation - Results in ~4% improvement for this method (43ms → 41ms) **Impact on Workloads:** Based on the test results, the optimization is particularly effective for: - **Large-scale processing**: 732% speedup on 500-candidate batches, 312-385% speedup on multiple candidate scenarios - **High-frequency time formatting**: Since `humanize_runtime` is called twice per refinement request, the 15x improvement compounds significantly - **Error handling paths**: 16-22% improvements even in error scenarios due to faster payload construction The optimizations maintain identical functionality while dramatically reducing computational overhead, making them especially valuable for batch processing workflows where these functions are called repeatedly.

codeflash-ai · 2025-12-19T02:55:07Z

This PR has been automatically closed because the original PR #962 by mohammedahmed18 was closed.

codeflash-ai Bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Dec 10, 2025

codeflash-ai Bot mentioned this pull request Dec 10, 2025

[Enhancement] Use weighted ranking to cap refinement candidates (CF-931) #962

Merged

aseembits93 requested a review from mohammedahmed18 December 15, 2025 16:25

codeflash-ai Bot closed this Dec 19, 2025

Base automatically changed from limit-refined-candidates to main December 19, 2025 02:55

codeflash-ai Bot deleted the codeflash/optimize-pr962-2025-12-10T14.56.33 branch December 19, 2025 02:55

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⚡️ Speed up method `AiServiceClient.optimize_python_code_refinement` by 115% in PR #962 (`limit-refined-candidates`)#963

⚡️ Speed up method `AiServiceClient.optimize_python_code_refinement` by 115% in PR #962 (`limit-refined-candidates`)#963
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codeflash-ai Bot commented Dec 10, 2025

⚡️ This pull request contains optimizations for PR #962

📄 115% (1.15x) speedup for AiServiceClient.optimize_python_code_refinement in codeflash/api/aiservice.py

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📄 115% (1.15x) speedup for `AiServiceClient.optimize_python_code_refinement` in `codeflash/api/aiservice.py`