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@codeflash-ai codeflash-ai bot commented Dec 17, 2025

📄 54% (0.54x) speedup for __Timer__.toc in quantecon/util/timing.py

⏱️ Runtime : 980 microseconds 635 microseconds (best of 34 runs)

📝 Explanation and details

The optimization extracts the time decomposition logic (divmod operations and formatting calculations) into a separate @njit-decorated helper function _decompose_time. This achieves a 54% speedup by leveraging Numba's just-in-time compilation to accelerate the mathematical operations.

Key changes:

  • Extracted computation logic: The divmod(elapsed, 60), divmod(m, 60), and (s % 1)*(10**digits) calculations are moved to a separate _decompose_time function
  • Numba acceleration: The helper function is decorated with @njit(cache=True, fastmath=True), enabling compiled execution of the mathematical operations
  • Type annotations: Added proper type hints for better Numba compilation

Why this optimization works:

  • Compiled math operations: Numba compiles the divmod operations and arithmetic to efficient machine code, eliminating Python's interpreter overhead for these calculations
  • Reduced Python overhead: The mathematical computations that were previously executed in pure Python are now executed in compiled code
  • Caching enabled: The cache=True parameter ensures the compiled function is cached after first use, avoiding recompilation overhead

Impact on workloads:
Based on the function references, toc() is called within loop_timer() for performance benchmarking scenarios where it may be invoked thousands of times. The test results show significant improvements in large-scale scenarios (up to 122% faster for 1000 calls), making this optimization particularly valuable for:

  • Performance benchmarking workflows where loop_timer calls toc() repeatedly
  • Applications that frequently measure elapsed time intervals
  • Any code that relies on precise timing measurements with verbose output enabled

The optimization is most effective when verbose=True (the default), as this is when the time decomposition logic executes. For verbose=False calls, the speedup is more modest since the mathematical operations are skipped entirely.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 3069 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import time

# imports
import pytest  # used for our unit tests
from quantecon.util.timing import __Timer__

# unit tests

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

def test_toc_basic_elapsed_time(monkeypatch):
    """
    Basic: Test that toc returns the correct elapsed time after tic.
    """
    timer = __Timer__()
    # Patch time.time to return a controlled value for tic
    monkeypatch.setattr(time, "time", lambda: 100.0)
    timer.tic()
    # Patch time.time to return a controlled value for toc
    monkeypatch.setattr(time, "time", lambda: 102.5)
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 834ns -> 750ns (11.2% faster)

def test_toc_basic_digits(monkeypatch, capsys):
    """
    Basic: Test that toc prints with correct number of digits.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 200.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 200.123456)
    timer.toc(verbose=True, digits=5)
    captured = capsys.readouterr()

def test_toc_basic_verbose_false(monkeypatch, capsys):
    """
    Basic: Test that toc does not print when verbose=False.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 300.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 301.0)
    timer.toc(verbose=False)
    captured = capsys.readouterr()

def test_toc_basic_return_type(monkeypatch):
    """
    Basic: Test that toc returns a float.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 400.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 401.0)
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 584ns -> 542ns (7.75% faster)

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

def test_toc_without_tic_raises():
    """
    Edge: Calling toc without tic should raise Exception.
    """
    timer = __Timer__()
    with pytest.raises(Exception) as excinfo:
        timer.toc() # 1.00μs -> 958ns (4.38% faster)

def test_toc_multiple_calls(monkeypatch):
    """
    Edge: Multiple toc calls should update last but not start.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 500.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 502.0)
    codeflash_output = timer.toc(verbose=False); first_elapsed = codeflash_output # 458ns -> 500ns (8.40% slower)
    monkeypatch.setattr(time, "time", lambda: 503.5)
    codeflash_output = timer.toc(verbose=False); second_elapsed = codeflash_output # 333ns -> 291ns (14.4% faster)

def test_toc_digits_zero(monkeypatch, capsys):
    """
    Edge: digits=0 should print no decimal digits.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 600.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 601.789)
    timer.toc(verbose=True, digits=0)
    captured = capsys.readouterr()

def test_toc_negative_elapsed(monkeypatch):
    """
    Edge: Simulate system time going backwards (should still compute negative elapsed).
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 700.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 699.5)
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 458ns -> 459ns (0.218% slower)

def test_toc_large_digits(monkeypatch, capsys):
    """
    Edge: digits > 10 should not break formatting.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 800.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 801.123456789012)
    timer.toc(verbose=True, digits=12)
    captured = capsys.readouterr()

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

def test_toc_many_calls(monkeypatch):
    """
    Large Scale: Call toc 1000 times and check monotonicity and performance.
    """
    timer = __Timer__()
    # Simulate time increasing by 0.01 each call
    base_time = [900.0]
    def fake_time():
        base_time[0] += 0.01
        return base_time[0]
    monkeypatch.setattr(time, "time", lambda: 900.0)
    timer.tic()
    monkeypatch.setattr(time, "time", fake_time)
    elapsed_times = []
    for _ in range(1000):
        elapsed_times.append(timer.toc(verbose=False)) # 215μs -> 199μs (8.20% faster)
    # All elapsed times should be strictly increasing
    for i in range(1, len(elapsed_times)):
        pass

def test_toc_performance(monkeypatch):
    """
    Large Scale: Check that toc executes quickly for 1000 calls.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 1000.0)
    timer.tic()
    monkeypatch.setattr(time, "time", lambda: 1000.1)
    import time as pytime
    start_perf = pytime.time()
    for _ in range(1000):
        timer.toc(verbose=False) # 187μs -> 169μs (10.7% faster)
    end_perf = pytime.time()

def test_toc_with_varied_digits(monkeypatch, capsys):
    """
    Large Scale: Test toc with varied digits for multiple calls.
    """
    timer = __Timer__()
    monkeypatch.setattr(time, "time", lambda: 1100.0)
    timer.tic()
    # Simulate time for each call
    digits_list = [0, 1, 2, 5, 10]
    for i, digits in enumerate(digits_list):
        monkeypatch.setattr(time, "time", lambda: 1100.0 + i * 0.12345)
        timer.toc(verbose=True, digits=digits)
        captured = capsys.readouterr()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
import time

# imports
import pytest
from quantecon.util.timing import __Timer__

# unit tests

# ----------------- BASIC TEST CASES -----------------

def test_toc_returns_float():
    """
    Basic: toc() should return a float representing elapsed time after tic().
    """
    timer = __Timer__()
    timer.tic()
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 541ns -> 541ns (0.000% faster)

def test_toc_elapsed_is_non_negative():
    """
    Basic: toc() should never return a negative value.
    """
    timer = __Timer__()
    timer.tic()
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 500ns -> 416ns (20.2% faster)

def test_toc_after_brief_sleep():
    """
    Basic: toc() should reflect actual elapsed time after a known sleep.
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.05)  # sleep for 50 ms
    codeflash_output = timer.toc(verbose=False); elapsed = codeflash_output # 2.04μs -> 1.92μs (6.52% faster)

def test_toc_multiple_calls_without_tic():
    """
    Basic: toc() can be called repeatedly after one tic(), and elapsed time increases.
    """
    timer = __Timer__()
    timer.tic()
    codeflash_output = timer.toc(verbose=False); elapsed1 = codeflash_output # 625ns -> 625ns (0.000% faster)
    time.sleep(0.02)
    codeflash_output = timer.toc(verbose=False); elapsed2 = codeflash_output # 2.58μs -> 2.38μs (8.80% faster)

def test_toc_verbose_prints(capsys):
    """
    Basic: toc(verbose=True) prints output in expected format.
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.01)
    codeflash_output = timer.toc(verbose=True, digits=3); elapsed = codeflash_output
    captured = capsys.readouterr().out

def test_toc_digits_parameter_changes_output(capsys):
    """
    Basic: toc(digits=4) prints more digits after decimal.
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.01)
    timer.toc(verbose=True, digits=4)
    output_4 = capsys.readouterr().out
    timer.tic()
    time.sleep(0.01)
    timer.toc(verbose=True, digits=1)
    output_1 = capsys.readouterr().out

# ----------------- EDGE TEST CASES -----------------

def test_toc_without_tic_raises():
    """
    Edge: toc() without prior tic() should raise Exception.
    """
    timer = __Timer__()
    timer.start = None
    with pytest.raises(Exception, match="toc\\(\\) without tic\\(\\)"):
        timer.toc(verbose=False) # 1.54μs -> 792ns (94.7% faster)

def test_toc_with_digits_zero(capsys):
    """
    Edge: toc() with digits=0 should print no digits after decimal.
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.01)
    timer.toc(verbose=True, digits=0)
    output = capsys.readouterr().out

def test_toc_with_large_digits(capsys):
    """
    Edge: toc() with large digits should print many digits after decimal.
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.01)
    timer.toc(verbose=True, digits=8)
    output = capsys.readouterr().out
    # Should have at least 8 digits after decimal
    after_decimal = output.strip().split(".")[1]

def test_toc_updates_last():
    """
    Edge: toc() should update the 'last' attribute to the current time.
    """
    timer = __Timer__()
    timer.tic()
    before = timer.last
    time.sleep(0.01)
    timer.toc(verbose=False) # 3.29μs -> 2.42μs (36.2% faster)
    after = timer.last

def test_toc_does_not_modify_start():
    """
    Edge: toc() should not modify the 'start' attribute.
    """
    timer = __Timer__()
    timer.tic()
    start_before = timer.start
    time.sleep(0.01)
    timer.toc(verbose=False) # 2.17μs -> 1.33μs (62.4% faster)
    start_after = timer.start

def test_toc_with_non_boolean_verbose():
    """
    Edge: toc() should treat non-bool verbose as truthy/falsy.
    """
    timer = __Timer__()
    timer.tic()
    # verbose=0 (falsy)
    codeflash_output = timer.toc(verbose=0); elapsed = codeflash_output # 1.42μs -> 584ns (143% faster)
    # verbose=1 (truthy, should print)
    timer.tic()
    codeflash_output = timer.toc(verbose=1); elapsed = codeflash_output # 10.0μs -> 4.33μs (131% faster)

def test_toc_with_negative_digits(capsys):
    """
    Edge: toc() with negative digits should print no digits after decimal (same as 0).
    """
    timer = __Timer__()
    timer.tic()
    time.sleep(0.01)
    timer.toc(verbose=True, digits=-2)
    output = capsys.readouterr().out

# ----------------- LARGE SCALE TEST CASES -----------------

def test_toc_many_calls_performance():
    """
    Large scale: toc() called 1000 times should not degrade or error.
    """
    timer = __Timer__()
    timer.tic()
    results = []
    for _ in range(1000):
        results.append(timer.toc(verbose=False)) # 546μs -> 245μs (122% faster)

def test_toc_precision_with_small_sleep():
    """
    Large scale: toc() should be precise enough to distinguish small time differences.
    """
    timer = __Timer__()
    timer.tic()
    codeflash_output = timer.toc(verbose=False); elapsed1 = codeflash_output # 1.08μs -> 541ns (100% faster)
    time.sleep(0.002)
    codeflash_output = timer.toc(verbose=False); elapsed2 = codeflash_output # 1.21μs -> 792ns (52.5% faster)

def test_toc_with_various_digits_large_scale(capsys):
    """
    Large scale: toc() with digits from 0 to 8 prints correct number of digits.
    """
    timer = __Timer__()
    for digits in range(9):
        timer.tic()
        time.sleep(0.001)
        timer.toc(verbose=True, digits=digits)
        output = capsys.readouterr().out
        after_decimal = output.strip().split(".")[1]

To edit these changes git checkout codeflash/optimize-__Timer__.toc-mj9qhw5x and push.

Codeflash Static Badge

The optimization extracts the time decomposition logic (`divmod` operations and formatting calculations) into a separate `@njit`-decorated helper function `_decompose_time`. This achieves a **54% speedup** by leveraging Numba's just-in-time compilation to accelerate the mathematical operations.

**Key changes:**
- **Extracted computation logic**: The `divmod(elapsed, 60)`, `divmod(m, 60)`, and `(s % 1)*(10**digits)` calculations are moved to a separate `_decompose_time` function
- **Numba acceleration**: The helper function is decorated with `@njit(cache=True, fastmath=True)`, enabling compiled execution of the mathematical operations
- **Type annotations**: Added proper type hints for better Numba compilation

**Why this optimization works:**
- **Compiled math operations**: Numba compiles the divmod operations and arithmetic to efficient machine code, eliminating Python's interpreter overhead for these calculations
- **Reduced Python overhead**: The mathematical computations that were previously executed in pure Python are now executed in compiled code
- **Caching enabled**: The `cache=True` parameter ensures the compiled function is cached after first use, avoiding recompilation overhead

**Impact on workloads:**
Based on the function references, `toc()` is called within `loop_timer()` for performance benchmarking scenarios where it may be invoked thousands of times. The test results show significant improvements in large-scale scenarios (up to 122% faster for 1000 calls), making this optimization particularly valuable for:
- Performance benchmarking workflows where `loop_timer` calls `toc()` repeatedly
- Applications that frequently measure elapsed time intervals
- Any code that relies on precise timing measurements with verbose output enabled

The optimization is most effective when `verbose=True` (the default), as this is when the time decomposition logic executes. For `verbose=False` calls, the speedup is more modest since the mathematical operations are skipped entirely.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 December 17, 2025 08:13
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Dec 17, 2025
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