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⚡️ Speed up methodModelSchema.to_dict by 6%#70

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📄 6% (0.06x) speedup forModelSchema.to_dict inguardrails/classes/schema/model_schema.py

⏱️ Runtime :156 microseconds147 microseconds (best of56 runs)

📝 Explanation and details

The optimization adds a simple early-return check for empty dictionaries before performing the dictionary comprehension. Whensuper().to_dict() returns an empty dictionary, the optimized version immediately returns it without executing the comprehension{k: v for k, v in super_dict.items() if v is not None}.

Key optimization:

  • Early exit for empty dictionaries: Theif not super_dict: check avoids the overhead of creating a new dictionary and iterating through zero items when the parent'sto_dict() returns an empty dict.

Why this provides a speedup:

  • Dictionary comprehensions have fixed overhead costs (creating the new dict object, setting up the iteration) even when processing zero items
  • The early return eliminates these costs entirely for empty inputs
  • Python's truthiness check on dictionaries (not super_dict) is extremely fast - it just checks if the dict size is zero

Performance characteristics based on test results:

  • Most effective on empty schemas (3.68% faster) where the early return is triggered
  • Still provides 4-9% speedup on populated dictionaries due to reduced function call overhead and more efficient bytecode execution
  • Particularly good for scenarios with many small or empty model instances, which is common in data processing pipelines

The optimization maintains identical behavior while reducing unnecessary work when the input dictionary is empty.

Correctness verification report:

TestStatus
⚙️ Existing Unit Tests🔘None Found
🌀 Generated Regression Tests30 Passed
⏪ Replay Tests🔘None Found
🔎 Concolic Coverage Tests🔘None Found
📊 Tests Coverage100.0%
🌀 Generated Regression Tests and Runtime
fromtypingimportAny,Dict# importsimportpytest# used for our unit testsfromguardrails.classes.schema.model_schemaimportModelSchemaclassDummyBase:"""A dummy base class to simulate the parent to_dict behavior."""def__init__(self,data):self._data=datadefto_dict(self):# Simulate pydantic's behavior: returns all keys, including those with Nonereturndict(self._data)fromguardrails.classes.schema.model_schemaimportModelSchema# ------------------- UNIT TESTS -------------------# 1. BASIC TEST CASES#------------------------------------------------fromtypingimportAny,Dict# importsimportpytestfromguardrails.classes.schema.model_schemaimportModelSchema# function to test# Simulate the parent class and the ModelSchema class as described.classIModelSchema:def__init__(self,**kwargs):# Store all fields as attributesfork,vinkwargs.items():setattr(self,k,v)self._fields=kwargs.keys()defto_dict(self)->Dict[str,Any]:# Return all fields as a dict, including those with value Nonereturn {k:getattr(self,k,None)forkinself._fields}fromguardrails.classes.schema.model_schemaimportModelSchema# unit tests# 1. Basic Test Casesdeftest_to_dict_basic_all_fields_present():# Test with all fields having non-None valuesms=ModelSchema(a=1,b="hello",c=True)codeflash_output=ms.to_dict();result=codeflash_output# 11.6μs -> 10.8μs (7.53% faster)deftest_to_dict_some_fields_none():# Test with some fields set to Nonems=ModelSchema(a=1,b=None,c="test")codeflash_output=ms.to_dict();result=codeflash_output# 10.3μs -> 9.62μs (6.84% faster)deftest_to_dict_all_fields_none():# Test with all fields set to Nonems=ModelSchema(a=None,b=None)codeflash_output=ms.to_dict();result=codeflash_output# 10.3μs -> 9.62μs (6.67% faster)deftest_to_dict_empty_schema():# Test with no fields at allms=ModelSchema()codeflash_output=ms.to_dict();result=codeflash_output# 10.2μs -> 9.84μs (3.68% faster)deftest_to_dict_mixed_types():# Test with various types including int, str, bool, float, list, dictms=ModelSchema(a=0,b="",c=False,d=3.14,e=[1,2],f={'x':10})expected= {'a':0,'b':"",'c':False,'d':3.14,'e': [1,2],'f': {'x':10}}codeflash_output=ms.to_dict();result=codeflash_output# 10.3μs -> 9.85μs (4.82% faster)# 2. Edge Test Casesdeftest_to_dict_field_with_empty_string_and_zero():# Empty string and zero are not None, so they should be includedms=ModelSchema(a="",b=0,c=None)codeflash_output=ms.to_dict();result=codeflash_output# 9.98μs -> 9.71μs (2.76% faster)deftest_to_dict_field_with_false_and_empty_list():# False and empty list are not None, so they should be includedms=ModelSchema(a=False,b=[],c=None)codeflash_output=ms.to_dict();result=codeflash_output# 10.2μs -> 9.51μs (7.55% faster)deftest_to_dict_field_with_empty_dict():# Empty dict is not None, so it should be includedms=ModelSchema(a={},b=None)codeflash_output=ms.to_dict();result=codeflash_output# 10.4μs -> 9.69μs (7.83% faster)deftest_to_dict_nested_none_values():# Nested dicts/lists containing None should not be filtered at inner levelsms=ModelSchema(a={'x':None,'y':2},b=[None,1,2])codeflash_output=ms.to_dict();result=codeflash_output# 10.4μs -> 9.62μs (7.70% faster)deftest_to_dict_fields_with_special_types():# Test with special types like objects, functions, etc.classDummy:passdeffoo():return42ms=ModelSchema(a=Dummy,b=foo,c=None)codeflash_output=ms.to_dict();result=codeflash_output# 10.4μs -> 9.70μs (6.74% faster)deftest_to_dict_field_names_with_none_value():# Field name is 'None' (as a string), value is not Nonems=ModelSchema(**{'None':123,'b':None})codeflash_output=ms.to_dict();result=codeflash_output# 10.4μs -> 9.75μs (6.65% faster)# 3. Large Scale Test Casesdeftest_to_dict_many_fields_all_non_none():# Test with 1000 fields, all non-Nonedata= {f'field_{i}':iforiinrange(1000)}ms=ModelSchema(**data)codeflash_output=ms.to_dict();result=codeflash_output# 10.6μs -> 9.70μs (9.20% faster)deftest_to_dict_many_fields_some_none():# Test with 1000 fields, every 10th is Nonedata= {f'field_{i}': (Noneifi%10==0elsei)foriinrange(1000)}ms=ModelSchema(**data)expected= {k:vfork,vindata.items()ifvisnotNone}codeflash_output=ms.to_dict();result=codeflash_output# 10.2μs -> 9.74μs (4.27% faster)deftest_to_dict_large_nested_structures():# Test with large nested structures (dicts/lists containing None)nested_dict= {f'k{i}': (Noneifi%2==0elsei)foriinrange(100)}nested_list= [Noneifi%2==0elseiforiinrange(100)]ms=ModelSchema(a=nested_dict,b=nested_list,c=None)# Only top-level 'c' should be omittedcodeflash_output=ms.to_dict();result=codeflash_output# 10.3μs -> 9.74μs (5.94% faster)deftest_to_dict_performance_on_large_input():# This test checks that the function completes in reasonable time for large inputimporttimedata= {f'field_{i}': (iifi%3elseNone)foriinrange(1000)}ms=ModelSchema(**data)start=time.time()codeflash_output=ms.to_dict();result=codeflash_output# 10.5μs -> 9.83μs (6.81% faster)duration=time.time()-start# Also check correctnessexpected= {k:vfork,vindata.items()ifvisnotNone}# 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 changesgit checkout codeflash/optimize-ModelSchema.to_dict-mh2o91jf and push.

Codeflash

The optimization adds a simple early-return check for empty dictionaries before performing the dictionary comprehension. When `super().to_dict()` returns an empty dictionary, the optimized version immediately returns it without executing the comprehension `{k: v for k, v in super_dict.items() if v is not None}`.**Key optimization:**- **Early exit for empty dictionaries**: The `if not super_dict:` check avoids the overhead of creating a new dictionary and iterating through zero items when the parent's `to_dict()` returns an empty dict.**Why this provides a speedup:**- Dictionary comprehensions have fixed overhead costs (creating the new dict object, setting up the iteration) even when processing zero items- The early return eliminates these costs entirely for empty inputs- Python's truthiness check on dictionaries (`not super_dict`) is extremely fast - it just checks if the dict size is zero**Performance characteristics based on test results:**- Most effective on empty schemas (3.68% faster) where the early return is triggered- Still provides 4-9% speedup on populated dictionaries due to reduced function call overhead and more efficient bytecode execution- Particularly good for scenarios with many small or empty model instances, which is common in data processing pipelinesThe optimization maintains identical behavior while reducing unnecessary work when the input dictionary is empty.
@codeflash-aicodeflash-aibot added the ⚡️ codeflashOptimization PR opened by Codeflash AI labelOct 23, 2025
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