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@codeflash-ai codeflash-ai bot commented May 30, 2025

⚡️ This pull request contains optimizations for PR #215

If you approve this dependent PR, these changes will be merged into the original PR branch tracer-optimization.

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


📄 25% (0.25x) speedup for Tracer.trace_dispatch_return in codeflash/tracer.py

⏱️ Runtime : 92.6 microseconds 74.4 microseconds (best of 80 runs)

📝 Explanation and details

Here is your optimized code. The optimization targets the trace_dispatch_return function specifically, which you profiled. The key performance wins are.

  • Eliminate redundant lookups: When repeatedly accessing self.cur and self.cur[-2], assign them to local variables to avoid repeated list lookups and attribute dereferencing.
  • Rearrange logic: Move cheapest, earliest returns to the top so unnecessary code isn't executed.
  • Localize attribute/cache lookups: Assign self.timings to a local variable.
  • Inline and combine conditions: Combine checks to avoid unnecessary attribute lookups or hasattr() calls.
  • Inline dictionary increments: Use dict.get() for fast set-or-increment semantics.

No changes are made to the return value or side effects of the function.

Summary of improvements:

  • All repeated list and dict lookups changed to locals for faster access.
  • All guards and returns are now at the top and out of the main logic path.
  • Increments and dict assignments use get and one-liners.
  • Removed duplicate lookups of self.cur, self.cur[-2], and self.timings for maximum speed.
  • Kept the function trace_dispatch_return identical in behavior and return value.

No other comments/code outside the optimized function have been changed.


If this function is in a hot path, this will measurably reduce the call overhead in Python.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 329 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage
🌀 Generated Regression Tests Details
from types import FrameType
from typing import Any

# imports
import pytest  # used for our unit tests
from codeflash.tracer import Tracer


# Minimal FakeFrame and FakeCode for testing
class FakeCode:
    def __init__(self, filename, lineno, name):
        self.co_filename = filename
        self.co_firstlineno = lineno
        self.co_name = name

class FakeFrame:
    def __init__(self, code, f_back=None):
        self.f_code = code
        self.f_back = f_back

# Helper to build cur/rcur structures
def build_cur_stack(depth, base_fn="f", base_frame=None):
    """
    Build a nested cur/rcur structure of given depth.
    Returns (cur, timings, frames)
    """
    timings = {}
    frames = []
    prev = None
    for i in range(depth):
        code = FakeCode(f"file{i}.py", 10 + i, f"{base_fn}{i}")
        frame = FakeFrame(code, prev)
        frames.append(frame)
        prev = frame
    # rcur: (ppt, pit, pet, pfn, pframe, pcur)
    rcur = None
    for i in reversed(range(depth)):
        rcur = [i, i, i, f"{base_fn}{i}", frames[i], rcur]
    return rcur, timings, frames

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
















from types import FrameType

# imports
import pytest  # used for our unit tests
from codeflash.tracer import Tracer


# Minimal fake frame and code objects to simulate frame stack
class FakeCode:
    def __init__(self, co_name, co_filename, co_qualname):
        self.co_name = co_name
        self.co_filename = co_filename
        self.co_qualname = co_qualname

class FakeFrame:
    def __init__(self, code, f_back=None):
        self.f_code = code
        self.f_back = f_back

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

def test_basic_single_return_updates_timings():
    """
    Basic: Single return, timings updated, new function in timings.
    """
    tracer = Tracer()
    # Prepare cur stack: (rpt, rit, ret, rfn, frame, rcur)
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    # rcur: parent frame tuple
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {}
    # Call with t=7
    codeflash_output = tracer.trace_dispatch_return(frame, 7); result = codeflash_output
    # Check timings for rfn
    cc, ns, tt, ct, callers = tracer.timings[rfn]

def test_basic_existing_timings_ns_nonzero():
    """
    Basic: Existing timings, ns!=0, so cc and ct not incremented.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {rfn: (3, 2, 100, 50, {pfn: 4})}
    codeflash_output = tracer.trace_dispatch_return(frame, 8); result = codeflash_output
    cc, ns, tt, ct, callers = tracer.timings[rfn]

def test_basic_callers_new_parent():
    """
    Basic: Parent function not in callers, should be added with count 1.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "baz"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("baz", "file.py", "baz"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {rfn: (0, 0, 0, 0, {})}
    codeflash_output = tracer.trace_dispatch_return(frame, 3); result = codeflash_output
    cc, ns, tt, ct, callers = tracer.timings[rfn]

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

def test_edge_cur_is_none():
    """
    Edge: cur is None, should return 0.
    """
    tracer = Tracer()
    tracer.cur = None
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    codeflash_output = tracer.trace_dispatch_return(frame, 1)

def test_edge_cur_minus2_is_none():
    """
    Edge: cur[-2] is None, should return 0.
    """
    tracer = Tracer()
    tracer.cur = (1, 2, 3, "foo", None, None)
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    codeflash_output = tracer.trace_dispatch_return(frame, 1)

def test_edge_frame_mismatch_and_f_back_match():
    """
    Edge: frame is not cur[-2], but frame.f_back == cur[-2].f_back, should recurse.
    """
    tracer = Tracer()
    # cur[-2] is frame2, frame is frame1, but frame1.f_back == frame2.f_back
    shared_f_back = FakeFrame(FakeCode("shared", "file.py", "shared"))
    frame1 = FakeFrame(FakeCode("foo", "file.py", "foo"), shared_f_back)
    frame2 = FakeFrame(FakeCode("foo", "file.py", "foo"), shared_f_back)
    rfn = "foo"
    pfn = "bar"
    rcur = (2, 20, 200, pfn, shared_f_back, None)
    tracer.cur = (1, 5, 10, rfn, frame2, rcur)
    tracer.timings = {}
    # Should recurse, and then return 1
    codeflash_output = tracer.trace_dispatch_return(frame1, 4); result = codeflash_output

def test_edge_frame_mismatch_and_no_f_back_match():
    """
    Edge: frame is not cur[-2], and no f_back match, should return 0.
    """
    tracer = Tracer()
    frame1 = FakeFrame(FakeCode("foo", "file.py", "foo"))
    frame2 = FakeFrame(FakeCode("foo", "file.py", "foo"))
    rfn = "foo"
    pfn = "bar"
    rcur = (2, 20, 200, pfn, frame2, None)
    tracer.cur = (1, 5, 10, rfn, frame2, rcur)
    tracer.timings = {}
    # frame1.f_back != frame2.f_back (both None)
    codeflash_output = tracer.trace_dispatch_return(frame1, 6)

def test_edge_rcur_is_none():
    """
    Edge: rcur is None, should return 0.
    """
    tracer = Tracer()
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    tracer.cur = (1, 5, 10, "foo", frame, None)
    codeflash_output = tracer.trace_dispatch_return(frame, 2)

def test_edge_rfn_not_in_timings():
    """
    Edge: rfn not in timings, should initialize.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {}
    tracer.trace_dispatch_return(frame, 3)
    cc, ns, tt, ct, callers = tracer.timings[rfn]

def test_edge_parent_fn_in_callers_increments():
    """
    Edge: Parent function already in callers, increments count.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {rfn: (0, 0, 0, 0, {pfn: 7})}
    tracer.trace_dispatch_return(frame, 2)
    cc, ns, tt, ct, callers = tracer.timings[rfn]

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

def test_large_scale_many_functions():
    """
    Large scale: Simulate a chain of 500 functions, ensure timings are correct for all.
    """
    tracer = Tracer()
    n = 500
    frames = [FakeFrame(FakeCode(f"foo{i}", f"file{i}.py", f"foo{i}")) for i in range(n)]
    rfns = [f"foo{i}" for i in range(n)]
    pfns = [f"foo{i-1}" if i > 0 else "root" for i in range(n)]
    # Build cur stack for the topmost function
    cur = None
    for i in reversed(range(n)):
        cur = (1, 1, 1, rfns[i], frames[i], cur)
    tracer.cur = cur
    tracer.timings = {}
    # Now, walk down the stack, simulating returns for each function
    # We'll only test the topmost return
    frame = frames[-1]
    codeflash_output = tracer.trace_dispatch_return(frame, 2); result = codeflash_output
    # After the first return, timings for top function should exist
    cc, ns, tt, ct, callers = tracer.timings[rfns[-1]]

def test_large_scale_nested_returns():
    """
    Large scale: Simulate 100 nested returns, ensure all timings are updated.
    """
    tracer = Tracer()
    n = 100
    # Build stack
    cur = None
    frames = []
    rfns = []
    pfns = []
    for i in reversed(range(n)):
        frame = FakeFrame(FakeCode(f"foo{i}", f"file{i}.py", f"foo{i}"))
        frames.append(frame)
        rfns.append(f"foo{i}")
        pfns.append(f"foo{i-1}" if i > 0 else "root")
        cur = (1, 1, 1, f"foo{i}", frame, cur)
    tracer.cur = cur
    tracer.timings = {}
    # Simulate returns for all frames
    for i in range(n):
        frame = frames[n-1-i]
        tracer.trace_dispatch_return(frame, i)
    # Check that all timings are present and correct
    for i in range(n):
        rfn = f"foo{i}"
        cc, ns, tt, ct, callers = tracer.timings[rfn]
        if i > 0:
            pass
        else:
            pass

def test_large_scale_callers_increment():
    """
    Large scale: Simulate repeated returns from same parent, callers count increments.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 5, 10, rfn, frame, rcur)
    tracer.timings = {rfn: (0, 0, 0, 0, {pfn: 0})}
    for i in range(100):
        tracer.trace_dispatch_return(frame, i)
    cc, ns, tt, ct, callers = tracer.timings[rfn]

def test_large_scale_timings_accumulate():
    """
    Large scale: Simulate 100 returns with increasing t, timings accumulate.
    """
    tracer = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer.cur = (1, 1, 1, rfn, frame, rcur)
    tracer.timings = {rfn: (0, 0, 0, 0, {pfn: 0})}
    total_tt = 0
    for i in range(100):
        tracer.trace_dispatch_return(frame, i)
        total_tt += 1 + i
    cc, ns, tt, ct, callers = tracer.timings[rfn]

# -------------------- DETERMINISM TEST --------------------

def test_determinism_multiple_runs_same_result():
    """
    Determinism: Multiple runs with same input produce same output.
    """
    tracer1 = Tracer()
    tracer2 = Tracer()
    rfn = "foo"
    pfn = "bar"
    frame1 = FakeFrame(FakeCode("foo", "file.py", "foo"))
    frame2 = FakeFrame(FakeCode("foo", "file.py", "foo"))
    pframe = FakeFrame(FakeCode("bar", "file.py", "bar"))
    rcur = (2, 20, 200, pfn, pframe, None)
    tracer1.cur = (1, 5, 10, rfn, frame1, rcur)
    tracer2.cur = (1, 5, 10, rfn, frame2, rcur)
    tracer1.timings = {}
    tracer2.timings = {}
    tracer1.trace_dispatch_return(frame1, 7)
    tracer2.trace_dispatch_return(frame2, 7)
# 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-pr215-2025-05-30T05.11.50 and push.

Codeflash

KRRT7 and others added 2 commits May 29, 2025 22:02
…`tracer-optimization`)

Here is your optimized code. The optimization targets the **`trace_dispatch_return`** function specifically, which you profiled. The key performance wins are.

- **Eliminate redundant lookups**: When repeatedly accessing `self.cur` and `self.cur[-2]`, assign them to local variables to avoid repeated list lookups and attribute dereferencing.
- **Rearrange logic**: Move cheapest, earliest returns to the top so unnecessary code isn't executed.
- **Localize attribute/cache lookups**: Assign `self.timings` to a local variable.
- **Inline and combine conditions**: Combine checks to avoid unnecessary attribute lookups or `hasattr()` calls.
- **Inline dictionary increments**: Use `dict.get()` for fast set-or-increment semantics.

No changes are made to the return value or side effects of the function.



**Summary of improvements:**  
- All repeated list and dict lookups changed to locals for faster access.
- All guards and returns are now at the top and out of the main logic path.
- Increments and dict assignments use `get` and one-liners.
- Removed duplicate lookups of `self.cur`, `self.cur[-2]`, and `self.timings` for maximum speed.
- Kept the function `trace_dispatch_return` identical in behavior and return value.

**No other comments/code outside the optimized function have been changed.**

---

**If this function is in a hot path, this will measurably reduce the call overhead in Python.**
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label May 30, 2025
@codeflash-ai codeflash-ai bot mentioned this pull request May 30, 2025
@misrasaurabh1
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wow, is this real @KRRT7

@KRRT7 KRRT7 force-pushed the tracer-optimization branch from ee4c7ad to a34c6aa Compare June 10, 2025 01:50
@KRRT7 KRRT7 closed this Jun 10, 2025
@codeflash-ai codeflash-ai bot deleted the codeflash/optimize-pr215-2025-05-30T05.11.50 branch June 10, 2025 04:44
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