Skip to content

⚡️ Speed up method Tracer.trace_dispatch_return by 25% in PR #215 (tracer-optimization) #256

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: tracer-optimization
Choose a base branch
from

Conversation

codeflash-ai[bot]
Copy link
Contributor

@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

…`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
Copy link
Contributor

wow, is this real @KRRT7

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
⚡️ codeflash Optimization PR opened by Codeflash AI
Projects
None yet
Development

Successfully merging this pull request may close these issues.

1 participant