Refactor get_cache_execute_command() and get_interative_execute_command() #746
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WalkthroughThis change introduces two new functions in Changes
Sequence Diagram(s)sequenceDiagram
participant Scheduler as Scheduler Module
participant Command as standalone.command
participant Backend as Backend Script
Scheduler->>Command: get_cache_execute_command(file_name, cores)
Command->>Command: Check mpi4py availability & core count
Command-->>Scheduler: Return command list
Scheduler->>Backend: Execute command
sequenceDiagram
participant Scheduler as Scheduler Module
participant Command as standalone.command
participant Backend as Backend Script
Scheduler->>Command: get_interactive_execute_command(cores)
Command->>Command: Check mpi4py availability & core count
Command-->>Scheduler: Return command list
Scheduler->>Backend: Execute command
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jan-janssen
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Actionable comments posted: 2
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📒 Files selected for processing (4)
executorlib/standalone/command.py(2 hunks)executorlib/task_scheduler/file/shared.py(2 hunks)executorlib/task_scheduler/interactive/shared.py(2 hunks)tests/test_fluxclusterexecutor.py(2 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (3)
tests/test_fluxclusterexecutor.py (1)
executorlib/standalone/command.py (1)
get_cache_execute_command(19-43)
executorlib/task_scheduler/file/shared.py (1)
executorlib/standalone/command.py (1)
get_cache_execute_command(19-43)
executorlib/task_scheduler/interactive/shared.py (1)
executorlib/standalone/command.py (1)
get_interactive_execute_command(46-68)
🔇 Additional comments (7)
executorlib/standalone/command.py (1)
1-3: LGTM: Imports are appropriate for the new functionality.The addition of
importlibandsysimports is correct for the new command generation functions that check formpi4pyavailability and get the Python executable path.executorlib/task_scheduler/interactive/shared.py (2)
8-8: LGTM: Import correctly added for the new centralized function.The import of
get_interactive_execute_commandfromexecutorlib.standalone.commandis appropriate for the refactoring.
52-54: LGTM: Function call correctly updated to use centralized command generation.The replacement of the local
_get_backend_pathfunction withget_interactive_execute_commandis consistent with the refactoring objective.tests/test_fluxclusterexecutor.py (2)
9-9: LGTM: Test import correctly updated for the refactored function.The import change from the removed
_get_execute_commandtoget_cache_execute_commandmaintains test coverage for the centralized command generation logic.
66-69: LGTM: Test function call correctly updated.The test properly uses the new
get_cache_execute_commandfunction with appropriate parameters, ensuring the refactored command generation is tested.executorlib/task_scheduler/file/shared.py (2)
8-8: LGTM: Import correctly added for the refactored command generation.The import of
get_cache_execute_commandfromexecutorlib.standalone.commandproperly replaces the removed local helper function.
154-157: LGTM: Function call correctly migrated to use centralized logic.The replacement of the local
_get_execute_commandfunction withget_cache_execute_commandis consistent with the refactoring objective and maintains the same parameter interface.
| def get_interactive_execute_command( | ||
| cores: int, | ||
| ) -> list: | ||
| """ | ||
| Get command to call backend as a list of two strings | ||
| Args: | ||
| cores (int): Number of cores used to execute the task, if it is greater than one use interactive_parallel.py | ||
| else interactive_serial.py | ||
| Returns: | ||
| list[str]: List of strings containing the python executable path and the backend script to execute | ||
| """ | ||
| command_lst = [sys.executable] | ||
| if cores > 1 and importlib.util.find_spec("mpi4py") is not None: | ||
| command_lst += [get_command_path(executable="interactive_parallel.py")] | ||
| elif cores > 1: | ||
| raise ImportError( | ||
| "mpi4py is required for parallel calculations. Please install mpi4py." | ||
| ) | ||
| else: | ||
| command_lst += [get_command_path(executable="interactive_serial.py")] | ||
| return command_lst |
There was a problem hiding this comment.
Fix inconsistent MPI command construction.
The get_interactive_execute_command function doesn't prepend mpiexec for parallel execution, which is inconsistent with get_cache_execute_command. This could cause parallel execution to fail.
Apply this diff to fix the MPI command construction:
def get_interactive_execute_command(
cores: int,
) -> list:
"""
Get command to call backend as a list of two strings
Args:
cores (int): Number of cores used to execute the task, if it is greater than one use interactive_parallel.py
else interactive_serial.py
Returns:
list[str]: List of strings containing the python executable path and the backend script to execute
"""
command_lst = [sys.executable]
if cores > 1 and importlib.util.find_spec("mpi4py") is not None:
- command_lst += [get_command_path(executable="interactive_parallel.py")]
+ command_lst = (
+ ["mpiexec", "-n", str(cores)]
+ + command_lst
+ + [get_command_path(executable="interactive_parallel.py")]
+ )
elif cores > 1:
raise ImportError(
"mpi4py is required for parallel calculations. Please install mpi4py."
)
else:
command_lst += [get_command_path(executable="interactive_serial.py")]
return command_lst📝 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 get_interactive_execute_command( | |
| cores: int, | |
| ) -> list: | |
| """ | |
| Get command to call backend as a list of two strings | |
| Args: | |
| cores (int): Number of cores used to execute the task, if it is greater than one use interactive_parallel.py | |
| else interactive_serial.py | |
| Returns: | |
| list[str]: List of strings containing the python executable path and the backend script to execute | |
| """ | |
| command_lst = [sys.executable] | |
| if cores > 1 and importlib.util.find_spec("mpi4py") is not None: | |
| command_lst += [get_command_path(executable="interactive_parallel.py")] | |
| elif cores > 1: | |
| raise ImportError( | |
| "mpi4py is required for parallel calculations. Please install mpi4py." | |
| ) | |
| else: | |
| command_lst += [get_command_path(executable="interactive_serial.py")] | |
| return command_lst | |
| def get_interactive_execute_command( | |
| cores: int, | |
| ) -> list: | |
| """ | |
| Get command to call backend as a list of two strings | |
| Args: | |
| cores (int): Number of cores used to execute the task, if it is greater than one use interactive_parallel.py | |
| else interactive_serial.py | |
| Returns: | |
| list[str]: List of strings containing the python executable path and the backend script to execute | |
| """ | |
| command_lst = [sys.executable] | |
| if cores > 1 and importlib.util.find_spec("mpi4py") is not None: | |
| command_lst = ( | |
| ["mpiexec", "-n", str(cores)] | |
| + command_lst | |
| + [get_command_path(executable="interactive_parallel.py")] | |
| ) | |
| elif cores > 1: | |
| raise ImportError( | |
| "mpi4py is required for parallel calculations. Please install mpi4py." | |
| ) | |
| else: | |
| command_lst += [get_command_path(executable="interactive_serial.py")] | |
| return command_lst |
🤖 Prompt for AI Agents
In executorlib/standalone/command.py around lines 46 to 68, the function
get_interactive_execute_command constructs the command list for parallel
execution without prepending 'mpiexec', unlike get_cache_execute_command. To fix
this, modify the command list to start with 'mpiexec' followed by sys.executable
and the parallel script when cores > 1 and mpi4py is available. This ensures
consistent MPI command construction and proper parallel execution.
| def get_cache_execute_command(file_name: str, cores: int = 1) -> list: | ||
| """ | ||
| Get command to call backend as a list of two strings | ||
| Args: | ||
| file_name (str): The name of the file. | ||
| cores (int, optional): Number of cores used to execute the task. Defaults to 1. | ||
| Returns: | ||
| list[str]: List of strings containing the python executable path and the backend script to execute | ||
| """ | ||
| command_lst = [sys.executable] | ||
| if cores > 1 and importlib.util.find_spec("mpi4py") is not None: | ||
| command_lst = ( | ||
| ["mpiexec", "-n", str(cores)] | ||
| + command_lst | ||
| + [get_command_path(executable="cache_parallel.py"), file_name] | ||
| ) | ||
| elif cores > 1: | ||
| raise ImportError( | ||
| "mpi4py is required for parallel calculations. Please install mpi4py." | ||
| ) | ||
| else: | ||
| command_lst += [get_command_path(executable="cache_serial.py"), file_name] | ||
| return command_lst |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify MPI command construction and consider edge cases.
The function logic is mostly correct, but there are a few considerations:
- The MPI command construction for parallel execution looks correct
- The error handling for missing
mpi4pyis appropriate - However, there's an inconsistency between the two functions in MPI command construction
The get_cache_execute_command function adds mpiexec -n <cores> before the Python executable, while get_interactive_execute_command doesn't. This inconsistency could lead to different execution behaviors:
🏁 Script executed:
#!/bin/bash
# Verify the MPI command construction difference between cache and interactive modes
echo "=== Cache command (with mpiexec) ==="
ast-grep --pattern $'get_cache_execute_command($$$) {
$$$
command_lst = (
["mpiexec", "-n", str(cores)]
+ command_lst
+ [get_command_path($$$), file_name]
)
$$$
}'
echo "=== Interactive command (without mpiexec) ==="
ast-grep --pattern $'get_interactive_execute_command($$$) {
$$$
command_lst += [get_command_path($$$)]
$$$
}'Length of output: 485
🏁 Script executed:
#!/bin/bash
# Locate and display the definition of get_interactive_execute_command
rg -n "def get_interactive_execute_command" -nLength of output: 122
🏁 Script executed:
#!/bin/bash
# Display get_interactive_execute_command implementation for comparison
sed -n '46,90p' executorlib/standalone/command.pyLength of output: 918
Inconsistent MPI invocation in interactive mode
The get_interactive_execute_command function does not wrap the call in mpiexec -n <cores>, unlike get_cache_execute_command. This leads to different parallel execution behaviors:
• File: executorlib/standalone/command.py
– get_cache_execute_command (lines 19–43) prefixes the command with ["mpiexec", "-n", str(cores)].
– get_interactive_execute_command (lines 46–66) only appends [sys.executable, …] without mpiexec.
To unify behavior, update get_interactive_execute_command so that when cores > 1 && mpi4py is available, it constructs the command as:
- if cores > 1 and importlib.util.find_spec("mpi4py") is not None:
- command_lst += [get_command_path(executable="interactive_parallel.py")]
+ if cores > 1 and importlib.util.find_spec("mpi4py") is not None:
+ command_lst = (
+ ["mpiexec", "-n", str(cores)]
+ + command_lst
+ + [get_command_path(executable="interactive_parallel.py")]
+ )This ensures both “cache” and “interactive” modes use MPI correctly for parallel execution.
Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In executorlib/standalone/command.py around lines 46 to 66, update the
get_interactive_execute_command function to prepend the command list with
["mpiexec", "-n", str(cores)] when cores > 1 and mpi4py is available, similar to
get_cache_execute_command. This involves checking for mpi4py using
importlib.util.find_spec and raising an ImportError if mpi4py is missing when
cores > 1. Adjust the command construction to ensure consistent MPI invocation
for parallel execution in both cache and interactive modes.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #746 +/- ##
==========================================
- Coverage 97.42% 97.42% -0.01%
==========================================
Files 31 31
Lines 1398 1396 -2
==========================================
- Hits 1362 1360 -2
Misses 36 36 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Summary by CodeRabbit
Refactor
Tests
These updates enhance code maintainability while preserving existing user functionality.