⚡️ Speed up function `process_validation_response` by 36% #47

codeflash-ai · 2025-11-05T06:19:06Z

📄 36% (0.36x) speedup for `process_validation_response` in `gradio/queueing.py`

⏱️ Runtime : 2.40 milliseconds → 1.76 milliseconds (best of 90 runs)

📝 Explanation and details

The optimized code achieves a 36% speedup through several targeted micro-optimizations that reduce overhead in Python's interpreter:

Key Optimizations:

Localized method lookups: Moving dict.get and validation_data.append to local variables (get_type, append) eliminates repeated attribute lookups in the hot loop, reducing per-iteration overhead.
Template dictionary reuse: The valid_tpl = {"is_valid": True, "message": ""} is created once and reused instead of creating new dictionaries repeatedly in the loop, saving object allocation overhead.
Efficient dictionary copying: Replaced {**data, "parameter_name": param_name} with data.copy() followed by direct key assignment, which is faster than dictionary unpacking.
String formatting optimization: Switched from f-strings to old-style % formatting ("parameter_%d" % i) for the parameter name fallback, which has lower overhead.
Early-exit validation loop: Replaced the all() generator expression with an explicit loop that breaks early on the first invalid item, avoiding unnecessary iterations when validation fails.

Performance Impact by Test Category:

Large-scale tests show the biggest gains (60-95% faster) due to reduced per-iteration overhead
Single dict operations benefit significantly (35-48% faster) from template reuse
Mixed validation scenarios see moderate improvements (3-16% faster)

Hot Path Context:
Based on the function reference, this optimization is particularly valuable since process_validation_response is called during request validation in Gradio's queueing system - a critical path that processes user inputs. The improvements will compound when handling multiple concurrent requests or large validation datasets, making the UI more responsive.

The optimizations are especially effective for workloads with large lists of validation responses or frequent validation operations, as evidenced by the dramatic speedups in the large-scale test cases.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 52 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	93.8%

🌀 Generated Regression Tests and Runtime

import inspect
from typing import Any

# imports
import pytest
from gradio.queueing import process_validation_response


# Minimal BlockFunction class for testing
class BlockFunction:
    def __init__(self, fn=None):
        self.fn = fn
from gradio.queueing import process_validation_response

# unit tests

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

def test_all_valid_list():
    # All items are valid, no __type__ key
    resp = [{"is_valid": True, "message": ""}, {"is_valid": True, "message": ""}]
    result, data = process_validation_response(resp) # 3.18μs -> 2.74μs (16.3% faster)

def test_single_valid_dict():
    # Single dict, valid
    resp = {"is_valid": True, "message": ""}
    result, data = process_validation_response(resp) # 2.06μs -> 1.41μs (46.0% faster)

def test_single_invalid_dict():
    # Single dict, invalid
    resp = {"is_valid": False, "message": "Error"}
    result, data = process_validation_response(resp) # 2.02μs -> 1.49μs (35.4% faster)

def test_list_with_validate_type_and_fn():
    # List with __type__ == "validate", and a fn with parameter names
    def foo(a, b): pass
    fn = BlockFunction(foo)
    resp = [
        {"__type__": "validate", "is_valid": False, "message": "bad"},
        {"__type__": "validate", "is_valid": True, "message": ""},
    ]
    result, data = process_validation_response(resp, fn) # 28.1μs -> 27.0μs (3.98% faster)

def test_list_with_validate_type_and_fn_extra_params():
    # More validations than parameters
    def foo(a): pass
    fn = BlockFunction(foo)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": ""},
        {"__type__": "validate", "is_valid": True, "message": ""},
    ]
    result, data = process_validation_response(resp, fn) # 24.6μs -> 24.6μs (0.045% slower)

def test_list_with_mixed_types():
    # Mix of validate and normal dicts
    def foo(x, y): pass
    fn = BlockFunction(foo)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": ""},
        {"is_valid": True, "message": ""}
    ]
    result, data = process_validation_response(resp, fn) # 25.9μs -> 24.9μs (4.09% faster)

# ---- Edge Test Cases ----

def test_empty_list():
    # Empty list input
    resp = []
    result, data = process_validation_response(resp) # 1.93μs -> 2.02μs (4.31% slower)

def test_none_input():
    # None input should be treated as valid (default else branch)
    result, data = process_validation_response(None) # 2.15μs -> 1.48μs (45.8% faster)

def test_dict_without_is_valid():
    # Dict missing is_valid key
    resp = {"foo": "bar"}
    result, data = process_validation_response(resp) # 2.17μs -> 1.54μs (41.1% faster)

def test_list_with_non_dict_elements():
    # List with elements that are not dicts
    resp = [1, "hello", None]
    result, data = process_validation_response(resp) # 3.24μs -> 2.79μs (16.1% faster)

def test_validate_type_missing_is_valid():
    # __type__ == "validate" but missing is_valid
    def foo(a): pass
    fn = BlockFunction(foo)
    resp = [{"__type__": "validate", "message": "oops"}]
    result, data = process_validation_response(resp, fn) # 25.3μs -> 24.8μs (2.01% faster)

def test_validate_type_with_extra_keys():
    # __type__ == "validate" with extra keys
    def foo(a): pass
    fn = BlockFunction(foo)
    resp = [{"__type__": "validate", "is_valid": True, "message": "", "extra": 42}]
    result, data = process_validation_response(resp, fn) # 23.6μs -> 24.1μs (2.13% slower)

def test_fn_with_no_parameters():
    # BlockFunction with fn with no parameters
    def foo(): pass
    fn = BlockFunction(foo)
    resp = [{"__type__": "validate", "is_valid": True, "message": ""}]
    result, data = process_validation_response(resp, fn) # 19.7μs -> 19.5μs (1.30% faster)

def test_validate_type_and_missing_fn():
    # __type__ == "validate" but no fn provided
    resp = [{"__type__": "validate", "is_valid": True, "message": ""}]
    result, data = process_validation_response(resp) # 3.36μs -> 3.63μs (7.44% slower)

def test_dict_is_not_validation_and_is_valid_missing():
    # Dict not validation, missing is_valid
    resp = {"foo": "bar"}
    result, data = process_validation_response(resp) # 2.14μs -> 1.46μs (46.8% faster)

def test_list_with_all_invalid():
    # All items invalid
    resp = [{"is_valid": False, "message": "bad"}, {"is_valid": False, "message": "worse"}]
    result, data = process_validation_response(resp) # 3.21μs -> 2.87μs (11.8% faster)

def test_list_with_some_invalid():
    # Some items invalid
    resp = [{"is_valid": True, "message": ""}, {"is_valid": False, "message": "bad"}]
    result, data = process_validation_response(resp) # 3.21μs -> 2.90μs (10.6% faster)

def test_dict_is_empty():
    # Empty dict input
    resp = {}
    result, data = process_validation_response(resp) # 2.22μs -> 1.51μs (46.7% faster)

def test_dict_is_valid_missing_message():
    # Dict with is_valid but missing message
    resp = {"is_valid": True}
    result, data = process_validation_response(resp) # 2.13μs -> 1.43μs (48.5% faster)

# ---- Large Scale Test Cases ----

def test_large_list_all_valid():
    # Large list, all valid
    resp = [{"is_valid": True, "message": ""} for _ in range(1000)]
    result, data = process_validation_response(resp) # 190μs -> 97.4μs (95.9% faster)

def test_large_list_some_invalid():
    # Large list, some invalid
    resp = [{"is_valid": True, "message": ""} for _ in range(999)] + [{"is_valid": False, "message": "bad"}]
    result, data = process_validation_response(resp) # 166μs -> 97.4μs (71.4% faster)

def test_large_list_validate_type_with_fn():
    # Large list of validate type with fn
    def foo(*args): pass
    fn = BlockFunction(foo)
    resp = [{"__type__": "validate", "is_valid": True, "message": ""} for _ in range(1000)]
    result, data = process_validation_response(resp, fn) # 345μs -> 326μs (5.68% faster)
    for i, d in enumerate(data):
        pass

def test_large_list_mixed():
    # Large list with mixed valid/invalid and validate types
    def foo(*args): pass
    fn = BlockFunction(foo)
    resp = []
    for i in range(1000):
        if i % 10 == 0:
            resp.append({"__type__": "validate", "is_valid": False, "message": "fail"})
        else:
            resp.append({"is_valid": True, "message": ""})
    result, data = process_validation_response(resp, fn) # 183μs -> 114μs (61.1% faster)
    fail_count = sum(1 for d in data if d.get("is_valid") is False)

def test_large_list_non_dict_elements():
    # Large list with non-dict elements
    resp = [None for _ in range(1000)]
    result, data = process_validation_response(resp) # 144μs -> 81.0μs (78.3% faster)

def test_large_list_validate_type_with_fn_param_names():
    # Validate type, fn with parameter names
    def foo(a, b, c, d, e, f, g, h, i, j):
        pass
    fn = BlockFunction(foo)
    resp = [{"__type__": "validate", "is_valid": True, "message": ""} for _ in range(10)]
    result, data = process_validation_response(resp, fn) # 37.0μs -> 36.0μs (2.62% faster)
    names = [d["parameter_name"] for d in data]

# ---- Mutation Testing Guards ----

def test_mutation_guard_all_true_vs_one_false():
    # If any is_valid is False, result should be False
    resp = [{"is_valid": True, "message": ""}, {"is_valid": False, "message": "bad"}]
    result, data = process_validation_response(resp) # 3.15μs -> 2.90μs (8.72% faster)

def test_mutation_guard_parameter_name_assignment():
    # __type__ == "validate" must assign correct parameter_name
    def foo(x, y): pass
    fn = BlockFunction(foo)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": ""},
        {"__type__": "validate", "is_valid": True, "message": ""}
    ]
    result, data = process_validation_response(resp, fn) # 26.3μs -> 26.0μs (1.33% faster)

def test_mutation_guard_else_branch():
    # When input is not list or invalid dict, default to valid
    resp = "not a dict or list"
    result, data = process_validation_response(resp) # 2.12μs -> 1.59μs (33.4% faster)

def test_mutation_guard_validate_type_only_if_dict():
    # __type__ == "validate" only triggers if dict
    resp = ["not a dict", {"__type__": "validate", "is_valid": True, "message": ""}]
    result, data = process_validation_response(resp) # 3.80μs -> 3.87μs (1.71% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import inspect
from typing import Any

# imports
import pytest
from gradio.queueing import process_validation_response


# Minimal BlockFunction stub for testing
class BlockFunction:
    def __init__(self, fn):
        self.fn = fn
from gradio.queueing import process_validation_response

# unit tests

# --- Basic Test Cases ---

def test_single_valid_dict():
    # Single dict, is_valid True
    resp = {"is_valid": True, "message": ""}
    result, data = process_validation_response(resp) # 2.10μs -> 1.46μs (43.8% faster)

def test_single_invalid_dict():
    # Single dict, is_valid False
    resp = {"is_valid": False, "message": "error"}
    result, data = process_validation_response(resp) # 2.12μs -> 1.46μs (45.8% faster)

def test_single_dict_missing_is_valid():
    # Single dict, missing is_valid key
    resp = {"message": "no is_valid"}
    result, data = process_validation_response(resp) # 2.05μs -> 1.54μs (32.8% faster)

def test_list_all_valid():
    # List of valid dicts
    resp = [{"is_valid": True, "message": ""}, {"is_valid": True, "message": ""}]
    result, data = process_validation_response(resp) # 3.24μs -> 3.00μs (8.24% faster)

def test_list_one_invalid():
    # List with one invalid dict
    resp = [{"is_valid": True, "message": ""}, {"is_valid": False, "message": "bad"}]
    result, data = process_validation_response(resp) # 3.14μs -> 2.88μs (9.20% faster)

def test_list_mixed_missing_keys():
    # List with dict missing is_valid
    resp = [{"is_valid": True, "message": ""}, {"foo": "bar"}]
    result, data = process_validation_response(resp) # 3.30μs -> 2.84μs (16.2% faster)

def test_list_empty():
    # Empty list
    resp = []
    result, data = process_validation_response(resp) # 1.96μs -> 2.06μs (4.75% slower)

# --- Edge Test Cases ---

def test_none_input():
    # None as input
    result, data = process_validation_response(None) # 2.12μs -> 1.54μs (38.1% faster)

def test_list_with_non_dict():
    # List with non-dict element
    resp = [{"is_valid": True, "message": ""}, "not_a_dict"]
    result, data = process_validation_response(resp) # 3.26μs -> 2.89μs (12.7% faster)

def test_dict_with_extra_keys():
    # Dict with extra keys
    resp = {"is_valid": True, "message": "", "extra": 123}
    result, data = process_validation_response(resp) # 2.10μs -> 1.46μs (43.9% faster)

def test_list_with_validate_type_and_fn():
    # List with __type__ == "validate" and BlockFunction provided
    def dummy_fn(a, b, c): pass
    fn = BlockFunction(dummy_fn)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": "ok"},
        {"__type__": "validate", "is_valid": False, "message": "bad"},
        {"is_valid": True, "message": ""}
    ]
    result, data = process_validation_response(resp, fn) # 30.5μs -> 29.3μs (3.82% faster)

def test_list_with_validate_type_and_fn_shorter():
    # Validate type, but fn signature is shorter than list
    def dummy_fn(a): pass
    fn = BlockFunction(dummy_fn)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": "ok"},
        {"__type__": "validate", "is_valid": False, "message": "bad"},
        {"is_valid": True, "message": ""}
    ]
    result, data = process_validation_response(resp, fn) # 25.9μs -> 25.0μs (3.63% faster)

def test_dict_with_no_is_valid_and_no_message():
    # Dict with neither is_valid nor message
    resp = {"foo": "bar"}
    result, data = process_validation_response(resp) # 2.12μs -> 1.55μs (36.8% faster)

def test_list_with_none_elements():
    # List with None elements
    resp = [None, {"is_valid": True, "message": ""}]
    result, data = process_validation_response(resp) # 3.23μs -> 2.92μs (10.6% faster)

def test_dict_is_not_dict_or_list():
    # Input is not dict or list (e.g. int)
    resp = 42
    result, data = process_validation_response(resp) # 2.13μs -> 1.56μs (36.4% faster)

def test_list_with_all_validate_type_and_fn():
    # All elements have __type__ == "validate"
    def dummy_fn(x, y): pass
    fn = BlockFunction(dummy_fn)
    resp = [
        {"__type__": "validate", "is_valid": True, "message": "ok"},
        {"__type__": "validate", "is_valid": True, "message": "ok2"}
    ]
    result, data = process_validation_response(resp, fn) # 27.3μs -> 26.0μs (5.10% faster)

# --- Large Scale Test Cases ---

def test_large_list_all_valid():
    # Large list, all valid
    resp = [{"is_valid": True, "message": ""} for _ in range(1000)]
    result, data = process_validation_response(resp) # 173μs -> 96.7μs (79.3% faster)

def test_large_list_one_invalid():
    # Large list, one invalid at the end
    resp = [{"is_valid": True, "message": ""} for _ in range(999)] + [{"is_valid": False, "message": "bad"}]
    result, data = process_validation_response(resp) # 165μs -> 97.3μs (69.5% faster)

def test_large_list_mixed_types():
    # Large list, alternating valid/invalid
    resp = []
    for i in range(1000):
        if i % 2 == 0:
            resp.append({"is_valid": True, "message": ""})
        else:
            resp.append({"is_valid": False, "message": "fail"})
    result, data = process_validation_response(resp) # 162μs -> 97.5μs (67.0% faster)
    # All odd indices must be invalid
    for i in range(1, 1000, 2):
        pass

def test_large_list_with_validate_type_and_fn():
    # Large list with __type__ == "validate" and BlockFunction
    def dummy_fn(*args): pass
    fn = BlockFunction(dummy_fn)
    resp = [{"__type__": "validate", "is_valid": True, "message": "ok"} for _ in range(1000)]
    result, data = process_validation_response(resp, fn) # 335μs -> 312μs (7.32% faster)
    for i in range(1000):
        pass

def test_large_list_with_non_dict_elements():
    # Large list with some non-dict elements
    resp = [{"is_valid": True, "message": ""} if i % 2 == 0 else None for i in range(1000)]
    result, data = process_validation_response(resp) # 158μs -> 89.7μs (77.1% faster)
    for i in range(1000):
        pass

# --- Determinism and Consistency ---

def test_repeatability():
    # Ensure repeated calls give same output
    resp = [{"is_valid": True, "message": ""}, {"is_valid": False, "message": "bad"}]
    result1, data1 = process_validation_response(resp) # 3.22μs -> 2.87μs (12.5% faster)
    result2, data2 = process_validation_response(resp) # 1.23μs -> 1.07μs (14.7% faster)
# 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-process_validation_response-mhllxn69 and push.

The optimized code achieves a **36% speedup** through several targeted micro-optimizations that reduce overhead in Python's interpreter: **Key Optimizations:** 1. **Localized method lookups**: Moving `dict.get` and `validation_data.append` to local variables (`get_type`, `append`) eliminates repeated attribute lookups in the hot loop, reducing per-iteration overhead. 2. **Template dictionary reuse**: The `valid_tpl = {"is_valid": True, "message": ""}` is created once and reused instead of creating new dictionaries repeatedly in the loop, saving object allocation overhead. 3. **Efficient dictionary copying**: Replaced `{**data, "parameter_name": param_name}` with `data.copy()` followed by direct key assignment, which is faster than dictionary unpacking. 4. **String formatting optimization**: Switched from f-strings to old-style `%` formatting (`"parameter_%d" % i`) for the parameter name fallback, which has lower overhead. 5. **Early-exit validation loop**: Replaced the `all()` generator expression with an explicit loop that breaks early on the first invalid item, avoiding unnecessary iterations when validation fails. **Performance Impact by Test Category:** - **Large-scale tests** show the biggest gains (60-95% faster) due to reduced per-iteration overhead - **Single dict operations** benefit significantly (35-48% faster) from template reuse - **Mixed validation scenarios** see moderate improvements (3-16% faster) **Hot Path Context:** Based on the function reference, this optimization is particularly valuable since `process_validation_response` is called during request validation in Gradio's queueing system - a critical path that processes user inputs. The improvements will compound when handling multiple concurrent requests or large validation datasets, making the UI more responsive. The optimizations are especially effective for workloads with large lists of validation responses or frequent validation operations, as evidenced by the dramatic speedups in the large-scale test cases.

codeflash-ai bot requested a review from mashraf-222 November 5, 2025 06:19

codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 5, 2025

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⚡️ Speed up function `process_validation_response` by 36% #47

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⚡️ Speed up function process_validation_response by 36% #47

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codeflash-ai bot commented Nov 5, 2025

📄 36% (0.36x) speedup for process_validation_response in gradio/queueing.py

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⚡️ Speed up function `process_validation_response` by 36% #47

⚡️ Speed up function `process_validation_response` by 36% #47

📄 36% (0.36x) speedup for `process_validation_response` in `gradio/queueing.py`