tracer improvements

.gitignore

@@ -254,4 +254,9 @@ fabric.properties
 
 # Mac
 .DS_Store
-WARP.MD
+WARP.MD
+
+.mcp.json
+.tessl/
+CLAUDE.md
+tessl.json

AGENTS.md

@@ -315,4 +315,8 @@ Language Server Protocol support in `codeflash/lsp/` enables IDE integration dur
 ### Performance Optimization
 - Profile before and after changes
 - Use benchmarks to validate improvements
-- Generate detailed performance reports
+- Generate detailed performance reports
+
+# Agent Rules <!-- tessl-managed -->
+
+@.tessl/RULES.md follow the [instructions](.tessl/RULES.md)

codeflash/benchmarking/function_ranker.py

@@ -2,7 +2,7 @@
 
 from typing import TYPE_CHECKING
 
-from codeflash.cli_cmds.console import console, logger
+from codeflash.cli_cmds.console import logger
 from codeflash.code_utils.config_consts import DEFAULT_IMPORTANCE_THRESHOLD
 from codeflash.discovery.functions_to_optimize import FunctionToOptimize
 from codeflash.tracing.profile_stats import ProfileStats
@@ -12,29 +12,63 @@
 
     from codeflash.discovery.functions_to_optimize import FunctionToOptimize
 
+pytest_patterns = {
+    "<frozen",  # Frozen modules like runpy
+    "<string>",  # Dynamically evaluated code
+    "_pytest/",  # Pytest internals
+    "pytest",  # Pytest files
+    "pluggy/",  # Plugin system
+    "_pydev",  # PyDev debugger
+    "runpy.py",  # Python module runner
+}
+pytest_func_patterns = {"pytest_", "_pytest", "runtest"}
+
+
+def is_pytest_infrastructure(filename: str, function_name: str) -> bool:
+    """Check if a function is part of pytest infrastructure that should be excluded from ranking.
+
+    This filters out pytest internal functions, hooks, and test framework code that
+    would otherwise dominate the ranking but aren't candidates for optimization.
+    """
+    # Check filename patterns
+    for pattern in pytest_patterns:
+        if pattern in filename:
+            return True
+
+    return any(pattern in function_name.lower() for pattern in pytest_func_patterns)
+
 
 class FunctionRanker:
-    """Ranks and filters functions based on a ttX score derived from profiling data.
+    """Ranks and filters functions based on % of addressable time derived from profiling data.
 
-    The ttX score is calculated as:
-        ttX = own_time + (time_spent_in_callees / call_count)
+    The % of addressable time is calculated as:
+        addressable_time = own_time + (time_spent_in_callees / call_count)
 
-    This score prioritizes functions that are computationally heavy themselves (high `own_time`)
-    or that make expensive calls to other functions (high average `time_spent_in_callees`).
+    This represents the runtime of a function plus the runtime of its immediate dependent functions,
+    as a fraction of overall runtime. It prioritizes functions that are computationally heavy themselves
+    (high `own_time`) or that make expensive calls to other functions (high average `time_spent_in_callees`).
 
     Functions are first filtered by an importance threshold based on their `own_time` as a
-    fraction of the total runtime. The remaining functions are then ranked by their ttX score
+    fraction of the total runtime. The remaining functions are then ranked by their % of addressable time
     to identify the best candidates for optimization.
     """
 
     def __init__(self, trace_file_path: Path) -> None:
         self.trace_file_path = trace_file_path
         self._profile_stats = ProfileStats(trace_file_path.as_posix())
         self._function_stats: dict[str, dict] = {}
+        self._function_stats_by_name: dict[str, list[tuple[str, dict]]] = {}
         self.load_function_stats()
 
+        # Build index for faster lookups: map function_name to list of (key, stats)
+        for key, stats in self._function_stats.items():
+            func_name = stats.get("function_name")
+            if func_name:
+                self._function_stats_by_name.setdefault(func_name, []).append((key, stats))
+
     def load_function_stats(self) -> None:
         try:
+            pytest_filtered_count = 0
             for (filename, line_number, func_name), (
                 call_count,
                 _num_callers,
@@ -45,6 +79,10 @@ def load_function_stats(self) -> None:
                 if call_count <= 0:
                     continue
 
+                if is_pytest_infrastructure(filename, func_name):
+                    pytest_filtered_count += 1
+                    continue
+
                 # Parse function name to handle methods within classes
                 class_name, qualified_name, base_function_name = (None, func_name, func_name)
                 if "." in func_name and not func_name.startswith("<"):
@@ -56,8 +94,8 @@ def load_function_stats(self) -> None:
                 own_time_ns = total_time_ns
                 time_in_callees_ns = cumulative_time_ns - total_time_ns
 
-                # Calculate ttX score
-                ttx_score = own_time_ns + (time_in_callees_ns / call_count)
+                # Calculate addressable time (own time + avg time in immediate callees)
+                addressable_time_ns = own_time_ns + (time_in_callees_ns / call_count)
 
                 function_key = f"{filename}:{qualified_name}"
                 self._function_stats[function_key] = {
@@ -70,89 +108,118 @@ def load_function_stats(self) -> None:
                     "own_time_ns": own_time_ns,
                     "cumulative_time_ns": cumulative_time_ns,
                     "time_in_callees_ns": time_in_callees_ns,
-                    "ttx_score": ttx_score,
+                    "addressable_time_ns": addressable_time_ns,
                 }
 
-            logger.debug(f"Loaded timing stats for {len(self._function_stats)} functions from trace using ProfileStats")
+            logger.debug(
+                f"Loaded timing stats for {len(self._function_stats)} functions from trace using ProfileStats "
+                f"(filtered {pytest_filtered_count} pytest infrastructure functions)"
+            )
 
         except Exception as e:
             logger.warning(f"Failed to process function stats from trace file {self.trace_file_path}: {e}")
             self._function_stats = {}
 
-    def _get_function_stats(self, function_to_optimize: FunctionToOptimize) -> dict | None:
+    def get_function_stats_summary(self, function_to_optimize: FunctionToOptimize) -> dict | None:
         target_filename = function_to_optimize.file_path.name
-        for key, stats in self._function_stats.items():
-            if stats.get("function_name") == function_to_optimize.function_name and (
-                key.endswith(f"/{target_filename}") or target_filename in key
-            ):
+        candidates = self._function_stats_by_name.get(function_to_optimize.function_name)
+        if not candidates:
+            logger.debug(
+                f"Could not find stats for function {function_to_optimize.function_name} in file {target_filename}"
+            )
+            return None
+
+        for key, stats in candidates:
+            # The check preserves exact logic: "key.endswith(f"/{target_filename}") or target_filename in key"
+            if key.endswith(f"/{target_filename}") or target_filename in key:
                 return stats
 
         logger.debug(
             f"Could not find stats for function {function_to_optimize.function_name} in file {target_filename}"
         )
         return None
 
-    def get_function_ttx_score(self, function_to_optimize: FunctionToOptimize) -> float:
-        stats = self._get_function_stats(function_to_optimize)
-        return stats["ttx_score"] if stats else 0.0
+    def get_function_addressable_time(self, function_to_optimize: FunctionToOptimize) -> float:
+        """Get the addressable time in nanoseconds for a function.
 
-    def rank_functions(self, functions_to_optimize: list[FunctionToOptimize]) -> list[FunctionToOptimize]:
-        ranked = sorted(functions_to_optimize, key=self.get_function_ttx_score, reverse=True)
-        logger.debug(
-            f"Function ranking order: {[f'{func.function_name} (ttX={self.get_function_ttx_score(func):.2f})' for func in ranked]}"
-        )
-        return ranked
+        Addressable time = own_time + (time_in_callees / call_count)
+        This represents the runtime of the function plus runtime of immediate dependent functions.
+        """
+        stats = self.get_function_stats_summary(function_to_optimize)
+        return stats["addressable_time_ns"] if stats else 0.0
 
-    def get_function_stats_summary(self, function_to_optimize: FunctionToOptimize) -> dict | None:
-        return self._get_function_stats(function_to_optimize)
+    def rank_functions(self, functions_to_optimize: list[FunctionToOptimize]) -> list[FunctionToOptimize]:
+        """Ranks and filters functions based on their % of addressable time and importance.
 
-    def rerank_functions(self, functions_to_optimize: list[FunctionToOptimize]) -> list[FunctionToOptimize]:
-        """Ranks functions based on their ttX score.
+        Filters out functions whose own_time is less than DEFAULT_IMPORTANCE_THRESHOLD
+        of file-relative runtime, then ranks the remaining functions by addressable time.
 
-        This method calculates the ttX score for each function and returns
-        the functions sorted in descending order of their ttX score.
-        """
-        if not self._function_stats:
-            logger.warning("No function stats available to rank functions.")
-            return []
+        Importance is calculated relative to functions in the same file(s) rather than
+        total program time. This avoids filtering out functions due to test infrastructure
+        overhead.
 
-        return self.rank_functions(functions_to_optimize)
+        The addressable time metric (own_time + avg time in immediate callees) prioritizes
+        functions that are computationally heavy themselves or that make expensive calls
+        to other functions.
 
-    def rerank_and_filter_functions(self, functions_to_optimize: list[FunctionToOptimize]) -> list[FunctionToOptimize]:
-        """Reranks and filters functions based on their impact on total runtime.
+        Args:
+            functions_to_optimize: List of functions to rank.
 
-        This method first calculates the total runtime of all profiled functions.
-        It then filters out functions whose own_time is less than a specified
-        percentage of the total runtime (importance_threshold).
+        Returns:
+            Important functions sorted in descending order of their addressable time.
 
-        The remaining 'important' functions are then ranked by their ttX score.
         """
-        stats_map = self._function_stats
-        if not stats_map:
+        if not self._function_stats:
+            logger.warning("No function stats available to rank functions.")
             return []
 
-        total_program_time = sum(s["own_time_ns"] for s in stats_map.values() if s.get("own_time_ns", 0) > 0)
+        # Calculate total time from functions in the same file(s) as functions to optimize
+        if functions_to_optimize:
+            # Get unique files from functions to optimize
+            target_files = {func.file_path.name for func in functions_to_optimize}
+            # Calculate total time only from functions in these files
+            total_program_time = sum(
+                s["own_time_ns"]
+                for s in self._function_stats.values()
+                if s.get("own_time_ns", 0) > 0
+                and any(
+                    str(s.get("filename", "")).endswith("/" + target_file) or s.get("filename") == target_file
+                    for target_file in target_files
+                )
+            )
+            logger.debug(
+                f"Using file-relative importance for {len(target_files)} file(s): {target_files}. "
+                f"Total file time: {total_program_time:,} ns"
+            )
+        else:
+            total_program_time = sum(
+                s["own_time_ns"] for s in self._function_stats.values() if s.get("own_time_ns", 0) > 0
+            )
 
         if total_program_time == 0:
             logger.warning("Total program time is zero, cannot determine function importance.")
-            return self.rank_functions(functions_to_optimize)
-
-        important_functions = []
-        for func in functions_to_optimize:
-            func_stats = self._get_function_stats(func)
-            if func_stats and func_stats.get("own_time_ns", 0) > 0:
-                importance = func_stats["own_time_ns"] / total_program_time
-                if importance >= DEFAULT_IMPORTANCE_THRESHOLD:
-                    important_functions.append(func)
-                else:
-                    logger.debug(
-                        f"Filtering out function {func.qualified_name} with importance "
-                        f"{importance:.2%} (below threshold {DEFAULT_IMPORTANCE_THRESHOLD:.2%})"
-                    )
-
-        logger.info(
-            f"Filtered down to {len(important_functions)} important functions from {len(functions_to_optimize)} total functions"
+            functions_to_rank = functions_to_optimize
+        else:
+            functions_to_rank = []
+            for func in functions_to_optimize:
+                func_stats = self.get_function_stats_summary(func)
+                if func_stats and func_stats.get("own_time_ns", 0) > 0:
+                    importance = func_stats["own_time_ns"] / total_program_time
+                    if importance >= DEFAULT_IMPORTANCE_THRESHOLD:
+                        functions_to_rank.append(func)
+                    else:
+                        logger.debug(
+                            f"Filtering out function {func.qualified_name} with importance "
+                            f"{importance:.2%} (below threshold {DEFAULT_IMPORTANCE_THRESHOLD:.2%})"
+                        )
+
+            logger.info(
+                f"Filtered down to {len(functions_to_rank)} important functions "
+                f"from {len(functions_to_optimize)} total functions"
+            )
+
+        ranked = sorted(functions_to_rank, key=self.get_function_addressable_time, reverse=True)
+        logger.debug(
+            f"Function ranking order: {[f'{func.function_name} (addressable_time={self.get_function_addressable_time(func):.2f}ns)' for func in ranked]}"
         )
-        console.rule()
-
-        return self.rank_functions(important_functions)
+        return ranked

codeflash/benchmarking/replay_test.py

@@ -64,30 +64,23 @@ def get_unique_test_name(module: str, function_name: str, benchmark_name: str, c
 
 
 def create_trace_replay_test_code(
-    trace_file: str,
-    functions_data: list[dict[str, Any]],
-    test_framework: str = "pytest",
-    max_run_count=256,  # noqa: ANN001
+    trace_file: str, functions_data: list[dict[str, Any]], max_run_count: int = 256
 ) -> str:
     """Create a replay test for functions based on trace data.
 
     Args:
     ----
         trace_file: Path to the SQLite database file
         functions_data: List of dictionaries with function info extracted from DB
-        test_framework: 'pytest' or 'unittest'
         max_run_count: Maximum number of runs to include in the test
 
     Returns:
     -------
         A string containing the test code
 
     """
-    assert test_framework in ["pytest", "unittest"]
-
     # Create Imports
-    imports = f"""from codeflash.picklepatch.pickle_patcher import PicklePatcher as pickle
-{"import unittest" if test_framework == "unittest" else ""}
+    imports = """from codeflash.picklepatch.pickle_patcher import PicklePatcher as pickle
 from codeflash.benchmarking.replay_test import get_next_arg_and_return
 """
 
@@ -158,13 +151,7 @@ def create_trace_replay_test_code(
     )
 
     # Create main body
-
-    if test_framework == "unittest":
-        self = "self"
-        test_template = "\nclass TestTracedFunctions(unittest.TestCase):\n"
-    else:
-        test_template = ""
-        self = ""
+    test_template = ""
 
     for func in functions_data:
         module_name = func.get("module_name")
@@ -223,30 +210,26 @@ def create_trace_replay_test_code(
                     filter_variables=filter_variables,
                 )
 
-        formatted_test_body = textwrap.indent(test_body, "        " if test_framework == "unittest" else "    ")
+        formatted_test_body = textwrap.indent(test_body, "    ")
 
-        test_template += "    " if test_framework == "unittest" else ""
         unique_test_name = get_unique_test_name(module_name, function_name, benchmark_function_name, class_name)
-        test_template += f"def test_{unique_test_name}({self}):\n{formatted_test_body}\n"
+        test_template += f"def test_{unique_test_name}():\n{formatted_test_body}\n"
 
     return imports + "\n" + metadata + "\n" + test_template
 
 
-def generate_replay_test(
-    trace_file_path: Path, output_dir: Path, test_framework: str = "pytest", max_run_count: int = 100
-) -> int:
+def generate_replay_test(trace_file_path: Path, output_dir: Path, max_run_count: int = 100) -> int:
     """Generate multiple replay tests from the traced function calls, grouped by benchmark.
 
     Args:
     ----
         trace_file_path: Path to the SQLite database file
         output_dir: Directory to write the generated tests (if None, only returns the code)
-        test_framework: 'pytest' or 'unittest'
         max_run_count: Maximum number of runs to include per function
 
     Returns:
     -------
-        Dictionary mapping benchmark names to generated test code
+        The number of replay tests generated
 
     """
     count = 0
@@ -293,10 +276,7 @@ def generate_replay_test(
                 continue
             # Generate the test code for this benchmark
             test_code = create_trace_replay_test_code(
-                trace_file=trace_file_path.as_posix(),
-                functions_data=functions_data,
-                test_framework=test_framework,
-                max_run_count=max_run_count,
+                trace_file=trace_file_path.as_posix(), functions_data=functions_data, max_run_count=max_run_count
             )
             test_code = sort_imports(code=test_code)
             output_file = get_test_file_path(