Integrate Line Profiler in Codeflash CF-470

code_to_optimize/bubble_sort_classmethod.py

@@ -0,0 +1,6 @@
+from code_to_optimize.bubble_sort_in_class import BubbleSortClass
+
+
+def sort_classmethod(x):
+    y = BubbleSortClass()
+    return y.sorter(x)

code_to_optimize/bubble_sort_nested_classmethod.py

@@ -0,0 +1,6 @@
+from code_to_optimize.bubble_sort_in_nested_class import WrapperClass
+
+
+def sort_classmethod(x):
+    y = WrapperClass.BubbleSortClass()
+    return y.sorter(x)

codeflash/api/aiservice.py

@@ -135,6 +135,76 @@ def optimize_python_code(
         console.rule()
         return []
 
+    def optimize_python_code_line_profiler(
+        self,
+        source_code: str,
+        dependency_code: str,
+        trace_id: str,
+        line_profiler_results: str,
+        num_candidates: int = 10,
+        experiment_metadata: ExperimentMetadata | None = None,
+    ) -> list[OptimizedCandidate]:
+        """Optimize the given python code for performance by making a request to the Django endpoint.
+
+        Parameters
+        ----------
+        - source_code (str): The python code to optimize.
+        - dependency_code (str): The dependency code used as read-only context for the optimization
+        - trace_id (str): Trace id of optimization run
+        - num_candidates (int): Number of optimization variants to generate. Default is 10.
+        - experiment_metadata (Optional[ExperimentalMetadata, None]): Any available experiment metadata for this optimization
+
+        Returns
+        -------
+        - List[OptimizationCandidate]: A list of Optimization Candidates.
+
+        """
+        payload = {
+            "source_code": source_code,
+            "dependency_code": dependency_code,
+            "num_variants": num_candidates,
+            "line_profiler_results": line_profiler_results,
+            "trace_id": trace_id,
+            "python_version": platform.python_version(),
+            "experiment_metadata": experiment_metadata,
+            "codeflash_version": codeflash_version,
+        }
+
+        logger.info("Generating optimized candidates…")
+        console.rule()
+        if line_profiler_results=="":
+            logger.info("No LineProfiler results were provided, Skipping optimization.")
+            console.rule()
+            return []
+        try:
+            response = self.make_ai_service_request("/optimize-line-profiler", payload=payload, timeout=600)
+        except requests.exceptions.RequestException as e:
+            logger.exception(f"Error generating optimized candidates: {e}")
+            ph("cli-optimize-error-caught", {"error": str(e)})
+            return []
+
+        if response.status_code == 200:
+            optimizations_json = response.json()["optimizations"]
+            logger.info(f"Generated {len(optimizations_json)} candidates.")
+            console.rule()
+            return [
+                OptimizedCandidate(
+                    source_code=opt["source_code"],
+                    explanation=opt["explanation"],
+                    optimization_id=opt["optimization_id"],
+                )
+                for opt in optimizations_json
+            ]
+        try:
+            error = response.json()["error"]
+        except Exception:
+            error = response.text
+        logger.error(f"Error generating optimized candidates: {response.status_code} - {error}")
+        ph("cli-optimize-error-response", {"response_status_code": response.status_code, "error": error})
+        console.rule()
+        return []
+
+
     def log_results(
         self,
         function_trace_id: str,

codeflash/code_utils/line_profile_utils.py

@@ -0,0 +1,223 @@
+"""Adapted from line_profiler (https://github.com/pyutils/line_profiler) written by Enthought, Inc. (BSD License)"""
+from collections import defaultdict
+from pathlib import Path
+from typing import Union
+
+import isort
+import libcst as cst
+
+from codeflash.code_utils.code_utils import get_run_tmp_file
+
+
+class LineProfilerDecoratorAdder(cst.CSTTransformer):
+    """Transformer that adds a decorator to a function with a specific qualified name."""
+
+    #TODO we don't support nested functions yet so they can only be inside classes, dont use qualified names, instead use the structure
+    def __init__(self, qualified_name: str, decorator_name: str):
+        """Initialize the transformer.
+
+        Args:
+            qualified_name: The fully qualified name of the function to add the decorator to (e.g., "MyClass.nested_func.target_func").
+            decorator_name: The name of the decorator to add.
+
+        """
+        super().__init__()
+        self.qualified_name_parts = qualified_name.split(".")
+        self.decorator_name = decorator_name
+
+        # Track our current context path, only add when we encounter a class
+        self.context_stack = []
+
+    def visit_ClassDef(self, node: cst.ClassDef) -> None:
+        # Track when we enter a class
+        self.context_stack.append(node.name.value)
+
+    def leave_ClassDef(self, original_node: cst.ClassDef, updated_node: cst.ClassDef) -> cst.ClassDef:
+        # Pop the context when we leave a class
+        self.context_stack.pop()
+        return updated_node
+
+    def visit_FunctionDef(self, node: cst.FunctionDef) -> None:
+        # Track when we enter a function
+        self.context_stack.append(node.name.value)
+
+    def leave_FunctionDef(self, original_node: cst.FunctionDef, updated_node: cst.FunctionDef) -> cst.FunctionDef:
+        function_name = original_node.name.value
+
+        # Check if the current context path matches our target qualified name
+        if self.context_stack==self.qualified_name_parts:
+            # Check if the decorator is already present
+            has_decorator = any(
+                self._is_target_decorator(decorator.decorator)
+                for decorator in original_node.decorators
+            )
+
+            # Only add the decorator if it's not already there
+            if not has_decorator:
+                new_decorator = cst.Decorator(
+                    decorator=cst.Name(value=self.decorator_name)
+                )
+
+                # Add our new decorator to the existing decorators
+                updated_decorators = [new_decorator] + list(updated_node.decorators)
+                updated_node = updated_node.with_changes(
+                    decorators=tuple(updated_decorators)
+                )
+
+        # Pop the context when we leave a function
+        self.context_stack.pop()
+        return updated_node
+
+    def _is_target_decorator(self, decorator_node: Union[cst.Name, cst.Attribute, cst.Call]) -> bool:
+        """Check if a decorator matches our target decorator name."""
+        if isinstance(decorator_node, cst.Name):
+            return decorator_node.value == self.decorator_name
+        if isinstance(decorator_node, cst.Call) and isinstance(decorator_node.func, cst.Name):
+            return decorator_node.func.value == self.decorator_name
+        return False
+
+class ProfileEnableTransformer(cst.CSTTransformer):
+    def __init__(self,filename):
+        # Flag to track if we found the import statement
+        self.found_import = False
+        # Track indentation of the import statement
+        self.import_indentation = None
+        self.filename = filename
+
+    def leave_ImportFrom(self, original_node: cst.ImportFrom, updated_node: cst.ImportFrom) -> cst.ImportFrom:
+        # Check if this is the line profiler import statement
+        if (isinstance(original_node.module, cst.Name) and
+                original_node.module.value == "line_profiler" and
+                any(name.name.value == "profile" and
+                    (not name.asname or name.asname.name.value == "codeflash_line_profile")
+                    for name in original_node.names)):
+
+            self.found_import = True
+            # Get the indentation from the original node
+            if hasattr(original_node, "leading_lines"):
+                leading_whitespace = original_node.leading_lines[-1].whitespace if original_node.leading_lines else ""
+                self.import_indentation = leading_whitespace
+
+        return updated_node
+
+    def leave_Module(self, original_node: cst.Module, updated_node: cst.Module) -> cst.Module:
+        if not self.found_import:
+            return updated_node
+
+        # Create a list of statements from the original module
+        new_body = list(updated_node.body)
+
+        # Find the index of the import statement
+        import_index = None
+        for i, stmt in enumerate(new_body):
+            if isinstance(stmt, cst.SimpleStatementLine):
+                for small_stmt in stmt.body:
+                    if isinstance(small_stmt, cst.ImportFrom):
+                        if (isinstance(small_stmt.module, cst.Name) and
+                                small_stmt.module.value == "line_profiler" and
+                                any(name.name.value == "profile" and
+                                    (not name.asname or name.asname.name.value == "codeflash_line_profile")
+                                    for name in small_stmt.names)):
+                            import_index = i
+                            break
+                if import_index is not None:
+                    break
+
+        if import_index is not None:
+            # Create the new enable statement to insert after the import
+            enable_statement = cst.parse_statement(
+                f"codeflash_line_profile.enable(output_prefix='{self.filename}')"
+            )
+
+            # Insert the new statement after the import statement
+            new_body.insert(import_index + 1, enable_statement)
+
+        # Create a new module with the updated body
+        return updated_node.with_changes(body=new_body)
+
+def add_decorator_to_qualified_function(module, qualified_name, decorator_name):
+    """Add a decorator to a function with the exact qualified name in the source code.
+
+    Args:
+        module: The Python source code as a string.
+        qualified_name: The fully qualified name of the function to add the decorator to (e.g., "MyClass.nested_func.target_func").
+        decorator_name: The name of the decorator to add.
+
+    Returns:
+        The modified source code as a string.
+
+    """
+    # Parse the source code into a CST
+
+    # Apply our transformer
+    transformer = LineProfilerDecoratorAdder(qualified_name, decorator_name)
+    modified_module = module.visit(transformer)
+
+    # Convert the modified CST back to source code
+    return modified_module
+
+def add_profile_enable(original_code: str, line_profile_output_file: str) -> str:
+    # TODO modify by using a libcst transformer
+    module = cst.parse_module(original_code)
+    transformer = ProfileEnableTransformer(line_profile_output_file)
+    modified_module = module.visit(transformer)
+    return modified_module.code
+
+
+class ImportAdder(cst.CSTTransformer):
+    def __init__(self, import_statement):
+        self.import_statement = import_statement
+        self.has_import = False
+
+    def leave_Module(self, original_node, updated_node):
+        # If the import is already there, don't add it again
+        if self.has_import:
+            return updated_node
+
+        # Parse the import statement into a CST node
+        import_node = cst.parse_statement(self.import_statement)
+
+        # Add the import to the module's body
+        return updated_node.with_changes(
+            body=[import_node] + list(updated_node.body)
+        )
+
+    def visit_ImportFrom(self, node):
+        # Check if the profile is already imported from line_profiler
+        if node.module and node.module.value == "line_profiler":
+            for import_alias in node.names:
+                if import_alias.name.value == "profile":
+                    self.has_import = True
+
+
+def add_decorator_imports(function_to_optimize, code_context):
+    """Adds a profile decorator to a function in a Python file and all its helper functions."""
+    #self.function_to_optimize, file_path_to_helper_classes, self.test_cfg.tests_root
+    #grouped iteration, file to fns to optimize, from line_profiler import profile as codeflash_line_profile
+    file_paths = defaultdict(list)
+    line_profile_output_file = get_run_tmp_file(Path("baseline_lprof"))
+    file_paths[function_to_optimize.file_path].append(function_to_optimize.qualified_name)
+    for elem in code_context.helper_functions:
+        file_paths[elem.file_path].append(elem.qualified_name)
+    for file_path,fns_present in file_paths.items():
+        #open file
+        file_contents = file_path.read_text("utf-8")
+        # parse to cst
+        module_node = cst.parse_module(file_contents)
+        for fn_name in fns_present:
+            # add decorator
+            module_node = add_decorator_to_qualified_function(module_node, fn_name, "codeflash_line_profile")
+        # add imports
+        # Create a transformer to add the import
+        transformer = ImportAdder("from line_profiler import profile as codeflash_line_profile")
+        # Apply the transformer to add the import
+        module_node = module_node.visit(transformer)
+        modified_code = isort.code(module_node.code, float_to_top=True)
+        # write to file
+        with open(file_path, "w", encoding="utf-8") as file:
+            file.write(modified_code)
+    #Adding profile.enable line for changing the savepath of the data, do this only for the main file and not the helper files
+    file_contents = function_to_optimize.file_path.read_text("utf-8")
+    modified_code = add_profile_enable(file_contents,str(line_profile_output_file))
+    function_to_optimize.file_path.write_text(modified_code,"utf-8")
+    return line_profile_output_file