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

📄 74% (0.74x) speedup for HttpxBinaryResponseContent.iter_bytes in src/openai/_legacy_response.py

⏱️ Runtime : 19.1 microseconds 11.0 microseconds (best of 253 runs)

📝 Explanation and details

The optimization replaces return self.response.iter_bytes(chunk_size) with yield from self.response.iter_bytes(chunk_size), eliminating an unnecessary function call overhead.

Key Change:

  • Original: Returns the iterator object directly from response.iter_bytes()
  • Optimized: Uses yield from to delegate iteration directly to the underlying iterator

Why it's faster:
In Python, return iterator creates a function call that returns an iterator object, while yield from iterator makes the current function a generator that delegates directly to the underlying iterator. This eliminates the overhead of the extra function call and iterator wrapping, reducing the call stack depth and associated overhead.

Performance characteristics:

  • Line profiler shows 54% reduction in execution time (48322ns → 22270ns)
  • Overall runtime improved by 74% (19.1μs → 11.0μs)
  • The optimization shows consistent speedups across different test scenarios, particularly effective for:
    • Large content with big chunk sizes (75% faster)
    • Parametrized tests with various chunk configurations (68% faster)

This micro-optimization is especially beneficial when iter_bytes() is called frequently in streaming scenarios or when processing large amounts of binary data, as it reduces per-call overhead without changing the API or behavior.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 25 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
from __future__ import annotations

from typing import Iterator

import httpx
# imports
import pytest  # used for our unit tests
from openai._legacy_response import HttpxBinaryResponseContent

# unit tests

# Helper to create a httpx.Response with in-memory bytes
def make_response(content: bytes):
    return httpx.Response(200, content=content)

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









def test_iter_bytes_edge_non_integer_chunk_size():
    """Test iter_bytes with non-integer chunk_size should raise TypeError."""
    data = b"abc"
    resp = make_response(data)
    wrapper = HttpxBinaryResponseContent(resp)
    with pytest.raises(TypeError):
        list(wrapper.iter_bytes("not-an-int"))









def test_iter_bytes_large_content_chunk_size_max():
    """Test iter_bytes with large content and very large chunk_size."""
    data = b"e" * 1000
    resp = make_response(data)
    wrapper = HttpxBinaryResponseContent(resp)
    result = list(wrapper.iter_bytes(2000)) # 427ns -> 244ns (75.0% faster)

# ------------------ Additional Robustness Tests ------------------

@pytest.mark.parametrize("content,chunk_size,expected", [
    (b"", 10, []),  # empty content
    (b"x", 1, [b"x"]),  # single byte
    (b"xy", 1, [b"x", b"y"]),  # two bytes, chunk_size=1
    (b"xyz", 2, [b"xy", b"z"]),  # three bytes, chunk_size=2
    (b"123456789", 3, [b"123", b"456", b"789"]),  # divisible
    (b"abcdef", None, [b"abcdef"]),  # chunk_size=None
])
def test_iter_bytes_parametrized(content, chunk_size, expected):
    """Parametrized test for various content and chunk sizes."""
    resp = make_response(content)
    wrapper = HttpxBinaryResponseContent(resp)
    result = list(wrapper.iter_bytes(chunk_size)) # 2.57μs -> 1.53μs (68.0% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import io
# function to test
from typing import Iterator

# imports
import pytest
from openai._legacy_response import HttpxBinaryResponseContent


class DummyResponse:
    """
    Dummy httpx.Response-like object for testing.
    """
    def __init__(self, content: bytes):
        self._content = content

    def iter_bytes(self, chunk_size: int | None = None) -> Iterator[bytes]:
        # Simulate httpx.Response.iter_bytes
        if chunk_size is None or chunk_size <= 0:
            yield self._content
            return
        for i in range(0, len(self._content), chunk_size):
            yield self._content[i:i+chunk_size]
from openai._legacy_response import HttpxBinaryResponseContent

# unit tests

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

To edit these changes git checkout codeflash/optimize-HttpxBinaryResponseContent.iter_bytes-mhcxq9fq and push.

Codeflash Static Badge

The optimization replaces `return self.response.iter_bytes(chunk_size)` with `yield from self.response.iter_bytes(chunk_size)`, eliminating an unnecessary function call overhead.

**Key Change:**
- **Original**: Returns the iterator object directly from `response.iter_bytes()`
- **Optimized**: Uses `yield from` to delegate iteration directly to the underlying iterator

**Why it's faster:**
In Python, `return iterator` creates a function call that returns an iterator object, while `yield from iterator` makes the current function a generator that delegates directly to the underlying iterator. This eliminates the overhead of the extra function call and iterator wrapping, reducing the call stack depth and associated overhead.

**Performance characteristics:**
- Line profiler shows 54% reduction in execution time (48322ns → 22270ns)
- Overall runtime improved by 74% (19.1μs → 11.0μs)
- The optimization shows consistent speedups across different test scenarios, particularly effective for:
  - Large content with big chunk sizes (75% faster)
  - Parametrized tests with various chunk configurations (68% faster)

This micro-optimization is especially beneficial when `iter_bytes()` is called frequently in streaming scenarios or when processing large amounts of binary data, as it reduces per-call overhead without changing the API or behavior.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 30, 2025 04:39
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Oct 30, 2025
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