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

📄 6% (0.06x) speedup for OpenRouterChatCompletionStreamingHandler.chunk_parser in litellm/llms/openrouter/chat/transformation.py

⏱️ Runtime : 506 microseconds 477 microseconds (best of 14 runs)

📝 Explanation and details

The optimized code achieves a 5% speedup through two key optimizations:

1. List Comprehension for Choice Processing
The original code used a traditional loop with append():

new_choices = []
for choice in chunk["choices"]:
    choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning")
    new_choices.append(choice)

The optimized version uses a list comprehension with dictionary unpacking:

new_choices = [
    {**choice, "delta": {**choice["delta"], "reasoning_content": choice["delta"].get("reasoning")}}
    for choice in chunk["choices"]
]

This eliminates the overhead of repeated append() calls and creates the list in a single operation, which is more memory-efficient and faster in Python.

2. F-string String Formatting
The original code used .format() for error message construction:

message="Message: {}, Metadata: {}, User ID: {}".format(...)

The optimized version uses f-strings:

message=f"Message: {error_chunk['message']}, Metadata: {error_chunk.get('metadata', {})}, User ID: {error_chunk.get('user_id', '')}"

F-strings are faster than .format() as they're evaluated at compile time rather than runtime.

Performance Impact by Test Cases:

  • The optimizations are most effective for test cases with multiple choices (like test_large_choices_missing_reasoning with 1000 choices), where the list comprehension provides significant speedup
  • Basic single-choice test cases show minimal improvement but still benefit from the cleaner, more efficient code path
  • Error handling cases benefit from the f-string optimization during exception message formatting

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 16 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 2 Passed
📊 Tests Coverage 100.0%
⚙️ Existing Unit Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py::TestOpenRouterChatCompletionStreamingHandler.test_chunk_parser_error_response 99.0μs 97.3μs 1.77%✅
test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py::TestOpenRouterChatCompletionStreamingHandler.test_chunk_parser_key_error 94.8μs 93.4μs 1.58%✅
test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py::TestOpenRouterChatCompletionStreamingHandler.test_chunk_parser_successful 129μs 114μs 12.9%✅
test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py::test_openrouter_cost_tracking_streaming 180μs 169μs 6.30%✅
🔎 Concolic Coverage Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_kt42dg31/tmpm5jj_bb1/test_concolic_coverage.py::test_OpenRouterChatCompletionStreamingHandler_chunk_parser_2 2.44μs 2.47μs -1.26%⚠️

To edit these changes git checkout codeflash/optimize-OpenRouterChatCompletionStreamingHandler.chunk_parser-mhdeyx0c and push.

Codeflash Static Badge

The optimized code achieves a 5% speedup through two key optimizations:

**1. List Comprehension for Choice Processing**
The original code used a traditional loop with `append()`:
```python
new_choices = []
for choice in chunk["choices"]:
    choice["delta"]["reasoning_content"] = choice["delta"].get("reasoning")
    new_choices.append(choice)
```

The optimized version uses a list comprehension with dictionary unpacking:
```python
new_choices = [
    {**choice, "delta": {**choice["delta"], "reasoning_content": choice["delta"].get("reasoning")}}
    for choice in chunk["choices"]
]
```

This eliminates the overhead of repeated `append()` calls and creates the list in a single operation, which is more memory-efficient and faster in Python.

**2. F-string String Formatting**
The original code used `.format()` for error message construction:
```python
message="Message: {}, Metadata: {}, User ID: {}".format(...)
```

The optimized version uses f-strings:
```python
message=f"Message: {error_chunk['message']}, Metadata: {error_chunk.get('metadata', {})}, User ID: {error_chunk.get('user_id', '')}"
```

F-strings are faster than `.format()` as they're evaluated at compile time rather than runtime.

**Performance Impact by Test Cases:**
- The optimizations are most effective for test cases with multiple choices (like `test_large_choices_missing_reasoning` with 1000 choices), where the list comprehension provides significant speedup
- Basic single-choice test cases show minimal improvement but still benefit from the cleaner, more efficient code path
- Error handling cases benefit from the f-string optimization during exception message formatting
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 30, 2025 12:42
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 30, 2025
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