Split shared cache in backend and frontend #443
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WalkthroughThe changes in this pull request involve modifications to the import paths and function definitions related to handling HDF5 files within the Changes
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Actionable comments posted: 6
🧹 Outside diff range and nitpick comments (4)
executorlib/backend/cache_serial.py (1)
Line range hint
5-7: Consider adding error handling for command line arguments.The script assumes that a command line argument will always be provided. Consider adding error handling to provide a better user experience when arguments are missing.
Here's a suggested improvement:
if __name__ == "__main__": + if len(sys.argv) != 2: + print("Error: Please provide a file name as argument") + print("Usage: python cache_serial.py <file_name>") + sys.exit(1) backend_execute_task_in_file(file_name=sys.argv[1])tests/test_cache_shared.py (1)
Line range hint
31-33: Improve cache directory path handling.Consider using
os.path.joinfor the cache directory path and moving it to a class-level constant or setUp method. This would make the path management more maintainable and follow Python's path handling best practices.Here's a suggested improvement:
class TestSharedFunctions(unittest.TestCase): + def setUp(self): + self.cache_directory = os.path.join(os.path.abspath(os.path.dirname(__file__)), "cache") + os.makedirs(self.cache_directory, exist_ok=True) + def test_execute_function_mixed(self): - cache_directory = os.path.abspath("cache") - os.makedirs(cache_directory, exist_ok=True)Also applies to: 89-91
executorlib/shared/cache.py (2)
Line range hint
56-59: Avoid CPU-intensive busy-waiting inexecute_in_subprocess.The
whileloop inexecute_in_subprocessperforms a busy-wait, which can lead to high CPU usage:while len(task_dependent_lst) > 0: task_dependent_lst = [ task for task in task_dependent_lst if task.poll() is None ]Consider adding a short sleep interval to prevent unnecessary CPU consumption.
Apply this diff to mitigate the issue:
+import time def execute_in_subprocess( command: list, task_dependent_lst: list = [] ) -> subprocess.Popen: ... while len(task_dependent_lst) > 0: task_dependent_lst = [ task for task in task_dependent_lst if task.poll() is None ] + time.sleep(0.1) return subprocess.Popen(command, universal_newlines=True)
Line range hint
100-117: Reduce CPU usage when the task queue is empty inexecute_tasks_h5.In the
execute_tasks_h5function, if thefuture_queueis empty, the loop continues without delay, causing high CPU usage:while True: task_dict = None try: task_dict = future_queue.get_nowait() except queue.Empty: pass if task_dict is not None: ... else: ...Consider adding a short sleep interval when the queue is empty to reduce CPU consumption.
Apply this diff:
+import time while True: task_dict = None try: task_dict = future_queue.get_nowait() except queue.Empty: pass + time.sleep(0.1) if task_dict is not None: ...
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (5)
- executorlib/backend/cache_parallel.py (1 hunks)
- executorlib/backend/cache_serial.py (1 hunks)
- executorlib/cache/backend.py (1 hunks)
- executorlib/shared/cache.py (1 hunks)
- tests/test_cache_shared.py (1 hunks)
🔇 Additional comments (5)
executorlib/backend/cache_serial.py (2)
3-3: LGTM! Import path updated correctly.The import statement has been properly updated to reflect the new location of the backend-specific functionality, which aligns with the PR objective of splitting the shared cache.
6-6: LGTM! Function call updated correctly.The function call has been properly updated to use the new backend-specific function name while maintaining the same argument passing pattern.
executorlib/cache/backend.py (1)
1-6: LGTM: Clean and well-organized importsThe imports are minimal, well-organized, and properly separated between standard library and custom modules.
executorlib/shared/cache.py (2)
7-7: LGTM!The import of
Tuplefrom thetypingmodule is necessary for type annotations in the code.
10-10: Updated imports align with the refactored code.The import statement now includes
dumpandget_output, which are used in this file. The removal ofloadreflects that it's no longer needed after refactoring.
| @@ -3,7 +3,7 @@ | |||
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| import cloudpickle | |||
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| from executorlib.shared.cache import backend_load_file, backend_write_file | |||
| from executorlib.cache.backend import backend_load_file, backend_write_file | |||
There was a problem hiding this comment.
💡 Codebase verification
Migration to executorlib.cache.backend is incomplete
The architectural split is not fully implemented:
backend.pystill imports fromexecutorlib.shared.cacheexecutor.pystill uses old imports fromexecutorlib.shared.cache- Only the backend execution modules (
cache_parallel.pyandcache_serial.py) have adopted the new import path
This suggests the migration is only partially done and needs to be completed for architectural consistency.
🔗 Analysis chain
LGTM! Verify architectural consistency.
The import path change aligns with the PR objective of splitting shared cache between backend and frontend.
Let's verify the architectural consistency:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that other backend modules follow the same import pattern
# and that frontend modules don't import from backend cache
# Check if other backend modules use the new import path
echo "Checking backend modules for consistent import patterns:"
rg -l "from executorlib.cache.backend import" "executorlib/backend/"
# Verify frontend doesn't import from backend cache
echo "Checking frontend modules don't import from backend cache:"
rg -l "from executorlib.cache.backend import" "executorlib/frontend/"
# Check for any remaining references to the old path
echo "Checking for remaining references to old path:"
rg -l "from executorlib.shared.cache import" "executorlib/"
Length of output: 749
Script:
#!/bin/bash
# Let's check the content of these files to understand the import structure better
# Check the implementation in backend.py to see if it's properly split
echo "Content of executorlib/cache/backend.py:"
cat executorlib/cache/backend.py
# Check executor.py to understand why it still uses the old import
echo -e "\nContent of executorlib/cache/executor.py:"
cat executorlib/cache/executor.py
# Check cache_serial.py to verify consistent usage pattern
echo -e "\nContent of executorlib/backend/cache_serial.py:"
cat executorlib/backend/cache_serial.py
Length of output: 3643
| def backend_load_file(file_name: str) -> dict: | ||
| """ | ||
| Load the data from an HDF5 file and convert FutureItem objects to their results. | ||
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||
| Args: | ||
| file_name (str): The name of the HDF5 file. | ||
|
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| Returns: | ||
| dict: The loaded data from the file. | ||
|
|
||
| """ | ||
| apply_dict = load(file_name=file_name) | ||
| apply_dict["args"] = [ | ||
| arg if not isinstance(arg, FutureItem) else arg.result() | ||
| for arg in apply_dict["args"] | ||
| ] | ||
| apply_dict["kwargs"] = { | ||
| key: arg if not isinstance(arg, FutureItem) else arg.result() | ||
| for key, arg in apply_dict["kwargs"].items() | ||
| } | ||
| return apply_dict |
There was a problem hiding this comment.
Add error handling for file operations and Future resolution
While the implementation is correct, it lacks error handling for potential failure scenarios:
- File I/O operations might fail
- FutureItem.result() could raise exceptions
Consider adding try-except blocks:
def backend_load_file(file_name: str) -> dict:
- apply_dict = load(file_name=file_name)
- apply_dict["args"] = [
- arg if not isinstance(arg, FutureItem) else arg.result()
- for arg in apply_dict["args"]
- ]
- apply_dict["kwargs"] = {
- key: arg if not isinstance(arg, FutureItem) else arg.result()
- for key, arg in apply_dict["kwargs"].items()
- }
- return apply_dict
+ try:
+ apply_dict = load(file_name=file_name)
+ try:
+ apply_dict["args"] = [
+ arg if not isinstance(arg, FutureItem) else arg.result()
+ for arg in apply_dict["args"]
+ ]
+ apply_dict["kwargs"] = {
+ key: arg if not isinstance(arg, FutureItem) else arg.result()
+ for key, arg in apply_dict["kwargs"].items()
+ }
+ return apply_dict
+ except Exception as e:
+ raise RuntimeError(f"Failed to resolve FutureItems: {str(e)}") from e
+ except Exception as e:
+ raise RuntimeError(f"Failed to load file {file_name}: {str(e)}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def backend_load_file(file_name: str) -> dict: | |
| """ | |
| Load the data from an HDF5 file and convert FutureItem objects to their results. | |
| Args: | |
| file_name (str): The name of the HDF5 file. | |
| Returns: | |
| dict: The loaded data from the file. | |
| """ | |
| apply_dict = load(file_name=file_name) | |
| apply_dict["args"] = [ | |
| arg if not isinstance(arg, FutureItem) else arg.result() | |
| for arg in apply_dict["args"] | |
| ] | |
| apply_dict["kwargs"] = { | |
| key: arg if not isinstance(arg, FutureItem) else arg.result() | |
| for key, arg in apply_dict["kwargs"].items() | |
| } | |
| return apply_dict | |
| def backend_load_file(file_name: str) -> dict: | |
| """ | |
| Load the data from an HDF5 file and convert FutureItem objects to their results. | |
| Args: | |
| file_name (str): The name of the HDF5 file. | |
| Returns: | |
| dict: The loaded data from the file. | |
| """ | |
| try: | |
| apply_dict = load(file_name=file_name) | |
| try: | |
| apply_dict["args"] = [ | |
| arg if not isinstance(arg, FutureItem) else arg.result() | |
| for arg in apply_dict["args"] | |
| ] | |
| apply_dict["kwargs"] = { | |
| key: arg if not isinstance(arg, FutureItem) else arg.result() | |
| for key, arg in apply_dict["kwargs"].items() | |
| } | |
| return apply_dict | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to resolve FutureItems: {str(e)}") from e | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to load file {file_name}: {str(e)}") from e |
| def backend_execute_task_in_file(file_name: str) -> None: | ||
| """ | ||
| Execute the task stored in a given HDF5 file. | ||
|
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||
| Args: | ||
| file_name (str): The file name of the HDF5 file as an absolute path. | ||
|
|
||
| Returns: | ||
| None | ||
| """ | ||
| apply_dict = backend_load_file(file_name=file_name) | ||
| result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"]) | ||
| backend_write_file( | ||
| file_name=file_name, | ||
| output=result, | ||
| ) |
There was a problem hiding this comment.
Improve function validation and documentation
The implementation needs better validation and error handling:
- No validation that 'fn' exists and is callable
- No error handling for the function execution
- Docstring should document the expected file format
Consider these improvements:
def backend_execute_task_in_file(file_name: str) -> None:
"""
Execute the task stored in a given HDF5 file.
Args:
file_name (str): The file name of the HDF5 file as an absolute path.
+ Expected file format:
+ The HDF5 file should contain a dictionary with:
+ - 'fn': A callable object
+ - 'args': List of positional arguments
+ - 'kwargs': Dictionary of keyword arguments
+
Returns:
None
+
+ Raises:
+ RuntimeError: If the file format is invalid or execution fails
"""
apply_dict = backend_load_file(file_name=file_name)
- result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"])
- backend_write_file(
- file_name=file_name,
- output=result,
- )
+
+ # Validate function
+ if "fn" not in apply_dict:
+ raise RuntimeError("Missing 'fn' in task file")
+ if not callable(apply_dict["fn"]):
+ raise RuntimeError("The 'fn' object is not callable")
+
+ try:
+ result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"])
+ backend_write_file(
+ file_name=file_name,
+ output=result,
+ )
+ except Exception as e:
+ raise RuntimeError(f"Task execution failed: {str(e)}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def backend_execute_task_in_file(file_name: str) -> None: | |
| """ | |
| Execute the task stored in a given HDF5 file. | |
| Args: | |
| file_name (str): The file name of the HDF5 file as an absolute path. | |
| Returns: | |
| None | |
| """ | |
| apply_dict = backend_load_file(file_name=file_name) | |
| result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"]) | |
| backend_write_file( | |
| file_name=file_name, | |
| output=result, | |
| ) | |
| def backend_execute_task_in_file(file_name: str) -> None: | |
| """ | |
| Execute the task stored in a given HDF5 file. | |
| Args: | |
| file_name (str): The file name of the HDF5 file as an absolute path. | |
| Expected file format: | |
| The HDF5 file should contain a dictionary with: | |
| - 'fn': A callable object | |
| - 'args': List of positional arguments | |
| - 'kwargs': Dictionary of keyword arguments | |
| Returns: | |
| None | |
| Raises: | |
| RuntimeError: If the file format is invalid or execution fails | |
| """ | |
| apply_dict = backend_load_file(file_name=file_name) | |
| # Validate function | |
| if "fn" not in apply_dict: | |
| raise RuntimeError("Missing 'fn' in task file") | |
| if not callable(apply_dict["fn"]): | |
| raise RuntimeError("The 'fn' object is not callable") | |
| try: | |
| result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"]) | |
| backend_write_file( | |
| file_name=file_name, | |
| output=result, | |
| ) | |
| except Exception as e: | |
| raise RuntimeError(f"Task execution failed: {str(e)}") from e |
| def backend_write_file(file_name: str, output: Any) -> None: | ||
| """ | ||
| Write the output to an HDF5 file. | ||
|
|
||
| Args: | ||
| file_name (str): The name of the HDF5 file. | ||
| output (Any): The output to be written. | ||
|
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||
| Returns: | ||
| None | ||
|
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||
| """ | ||
| file_name_out = os.path.splitext(file_name)[0] | ||
| os.rename(file_name, file_name_out + ".h5ready") | ||
| dump(file_name=file_name_out + ".h5ready", data_dict={"output": output}) | ||
| os.rename(file_name_out + ".h5ready", file_name_out + ".h5out") |
There was a problem hiding this comment.
Improve robustness of file operations
The current implementation could be more robust in handling edge cases and failures:
- Temporary files might be left behind if the operation fails
- Existing .h5ready files could cause conflicts
- Write permissions aren't verified upfront
Consider this improved implementation:
def backend_write_file(file_name: str, output: Any) -> None:
- file_name_out = os.path.splitext(file_name)[0]
- os.rename(file_name, file_name_out + ".h5ready")
- dump(file_name=file_name_out + ".h5ready", data_dict={"output": output})
- os.rename(file_name_out + ".h5ready", file_name_out + ".h5out")
+ file_name_out = os.path.splitext(file_name)[0]
+ temp_file = file_name_out + ".h5ready"
+ output_file = file_name_out + ".h5out"
+
+ # Check permissions early
+ if not os.access(os.path.dirname(file_name_out), os.W_OK):
+ raise PermissionError(f"No write permission in directory: {os.path.dirname(file_name_out)}")
+
+ # Clean up any existing temporary files
+ if os.path.exists(temp_file):
+ os.remove(temp_file)
+
+ try:
+ os.rename(file_name, temp_file)
+ try:
+ dump(file_name=temp_file, data_dict={"output": output})
+ os.rename(temp_file, output_file)
+ except Exception as e:
+ # Restore original file if dump fails
+ os.rename(temp_file, file_name)
+ raise RuntimeError(f"Failed to write output: {str(e)}") from e
+ except Exception as e:
+ raise RuntimeError(f"Failed to process file {file_name}: {str(e)}") from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def backend_write_file(file_name: str, output: Any) -> None: | |
| """ | |
| Write the output to an HDF5 file. | |
| Args: | |
| file_name (str): The name of the HDF5 file. | |
| output (Any): The output to be written. | |
| Returns: | |
| None | |
| """ | |
| file_name_out = os.path.splitext(file_name)[0] | |
| os.rename(file_name, file_name_out + ".h5ready") | |
| dump(file_name=file_name_out + ".h5ready", data_dict={"output": output}) | |
| os.rename(file_name_out + ".h5ready", file_name_out + ".h5out") | |
| def backend_write_file(file_name: str, output: Any) -> None: | |
| """ | |
| Write the output to an HDF5 file. | |
| Args: | |
| file_name (str): The name of the HDF5 file. | |
| output (Any): The output to be written. | |
| Returns: | |
| None | |
| """ | |
| file_name_out = os.path.splitext(file_name)[0] | |
| temp_file = file_name_out + ".h5ready" | |
| output_file = file_name_out + ".h5out" | |
| # Check permissions early | |
| if not os.access(os.path.dirname(file_name_out), os.W_OK): | |
| raise PermissionError(f"No write permission in directory: {os.path.dirname(file_name_out)}") | |
| # Clean up any existing temporary files | |
| if os.path.exists(temp_file): | |
| os.remove(temp_file) | |
| try: | |
| os.rename(file_name, temp_file) | |
| try: | |
| dump(file_name=temp_file, data_dict={"output": output}) | |
| os.rename(temp_file, output_file) | |
| except Exception as e: | |
| # Restore original file if dump fails | |
| os.rename(temp_file, file_name) | |
| raise RuntimeError(f"Failed to write output: {str(e)}") from e | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to process file {file_name}: {str(e)}") from e |
| import os | ||
| from typing import Any | ||
|
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| from executorlib.shared.cache import FutureItem | ||
| from executorlib.shared.hdf import dump, load | ||
|
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|
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| def backend_load_file(file_name: str) -> dict: | ||
| """ | ||
| Load the data from an HDF5 file and convert FutureItem objects to their results. | ||
|
|
||
| Args: | ||
| file_name (str): The name of the HDF5 file. | ||
|
|
||
| Returns: | ||
| dict: The loaded data from the file. | ||
|
|
||
| """ | ||
| apply_dict = load(file_name=file_name) | ||
| apply_dict["args"] = [ | ||
| arg if not isinstance(arg, FutureItem) else arg.result() | ||
| for arg in apply_dict["args"] | ||
| ] | ||
| apply_dict["kwargs"] = { | ||
| key: arg if not isinstance(arg, FutureItem) else arg.result() | ||
| for key, arg in apply_dict["kwargs"].items() | ||
| } | ||
| return apply_dict | ||
|
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||
|
|
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| def backend_write_file(file_name: str, output: Any) -> None: | ||
| """ | ||
| Write the output to an HDF5 file. | ||
|
|
||
| Args: | ||
| file_name (str): The name of the HDF5 file. | ||
| output (Any): The output to be written. | ||
|
|
||
| Returns: | ||
| None | ||
|
|
||
| """ | ||
| file_name_out = os.path.splitext(file_name)[0] | ||
| os.rename(file_name, file_name_out + ".h5ready") | ||
| dump(file_name=file_name_out + ".h5ready", data_dict={"output": output}) | ||
| os.rename(file_name_out + ".h5ready", file_name_out + ".h5out") | ||
|
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|
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| def backend_execute_task_in_file(file_name: str) -> None: | ||
| """ | ||
| Execute the task stored in a given HDF5 file. | ||
|
|
||
| Args: | ||
| file_name (str): The file name of the HDF5 file as an absolute path. | ||
|
|
||
| Returns: | ||
| None | ||
| """ | ||
| apply_dict = backend_load_file(file_name=file_name) | ||
| result = apply_dict["fn"].__call__(*apply_dict["args"], **apply_dict["kwargs"]) | ||
| backend_write_file( | ||
| file_name=file_name, | ||
| output=result, | ||
| ) |
There was a problem hiding this comment.
💡 Codebase verification
Security Advisory: Critical - Unsafe Deserialization of Arbitrary Functions
The verification confirms and elevates the security concern. The codebase uses cloudpickle to serialize and deserialize arbitrary Python functions from HDF5 files without any validation:
- In
executorlib/shared/hdf.py,load()deserializes arbitrary functions usingcloudpickle.loads()without any security checks - Multiple components execute these functions directly:
executorlib/cache/backend.pyexecutorlib/interactive/backend.pyexecutorlib/backend/cache_parallel.py
Critical security risks:
- Remote Code Execution (RCE) through malicious pickled data
- No function whitelist or validation
- No sandboxing of execution
- Used in parallel execution contexts (MPI)
Immediate recommendations:
- Implement function validation before deserialization
- Use a secure serialization format or implement signing
- Run untrusted code in a restricted environment
- Add input validation for HDF5 files
🔗 Analysis chain
Security Advisory: Validate input sources
The code executes arbitrary functions loaded from HDF5 files, which could be a security risk if the files come from untrusted sources. Consider:
- Implementing a whitelist of allowed functions
- Adding validation of input file sources
- Running in a sandboxed environment
Let's check for other instances where HDF5 files are loaded and executed:
Let me search for HDF file operations and function execution patterns using different approaches since the previous command had issues with the file type recognition.
Let me examine the test files and implementation details to understand if there are any existing security measures or validation patterns.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for HDF5 file operations and function execution
rg -l "load.*\.h5|dump.*\.h5|__call__.*args" --type python
Length of output: 91
Script:
#!/bin/bash
# Search for HDF5 file operations and function execution with ripgrep
rg -l "\.h5|__call__.*args|dump\(|load\("
# Search for imports of HDF functions
rg "from.*hdf.*import"
# Look for function execution patterns
ast-grep --pattern 'fn.__call__($$$)'
Length of output: 733
Script:
#!/bin/bash
# Check how the HDF files are used in the test files to understand validation
rg -A 5 "\.h5" tests/
# Look at the implementation of dump and load functions
cat executorlib/shared/hdf.py
# Check how functions are validated before execution in other files
rg -A 5 "__call__" executorlib/
Length of output: 7293
| try: | ||
| from executorlib.shared.cache import ( | ||
| FutureItem, | ||
| execute_task_in_file, | ||
| _check_task_output, | ||
| ) | ||
| from executorlib.cache.backend import FutureItem | ||
| from executorlib.shared.hdf import dump |
There was a problem hiding this comment.
💡 Codebase verification
Update imports to use backend functions
The verification reveals that:
execute_task_in_filehas been moved to backend asbackend_execute_task_in_file_check_task_outputis still inshared.cacheand is used internally by the shared cache implementation- The test file is the only place still using the old
execute_task_in_fileimport
You should:
- Update the test to import
backend_execute_task_in_filefromexecutorlib.cache.backendinstead ofexecute_task_in_filefrom shared - Keep the
_check_task_outputimport as is since it's correctly located in shared.cache
🔗 Analysis chain
Consider migrating remaining shared.cache imports.
The import change for FutureItem aligns with the cache splitting objective. However, other functions like execute_task_in_file and _check_task_output are still imported from shared.cache. Consider if these should also be moved to the backend module for consistency.
Let's verify the current location of these functions:
Let me gather more information about the function locations and their implementations:
Let me check one more thing to understand the complete picture:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Check if execute_task_in_file and _check_task_output are used elsewhere
# and if they should be moved to backend
# Search for usage of these functions
echo "Searching for execute_task_in_file usage:"
rg "execute_task_in_file"
echo -e "\nSearching for _check_task_output usage:"
rg "_check_task_output"
# Check if these functions are defined in both shared and backend
echo -e "\nChecking function definitions:"
ast-grep --pattern 'def execute_task_in_file'
ast-grep --pattern 'def _check_task_output'
Length of output: 1319
Script:
#!/bin/bash
# Check the implementation of execute_task_in_file in shared.cache
echo "Checking execute_task_in_file in shared.cache:"
rg -A 10 "def execute_task_in_file" executorlib/shared/cache.py
# Check the implementation in backend
echo -e "\nChecking backend_execute_task_in_file in backend:"
rg -A 10 "def backend_execute_task_in_file" executorlib/cache/backend.py
# Check if there are any other references to these functions in imports
echo -e "\nChecking imports:"
rg "from executorlib.shared.cache import.*execute_task_in_file"
rg "from executorlib.cache.backend import.*execute_task_in_file"
Length of output: 940
Script:
#!/bin/bash
# Check the content of shared.cache to understand the relationship between functions
echo "Checking shared.cache content:"
cat executorlib/shared/cache.py
# Also check if there are any direct references to these functions
echo -e "\nChecking direct references to execute_task_in_file:"
rg "\bexecute_task_in_file\b" --type py
Length of output: 8330
Summary by CodeRabbit