Lint fixes for parallel_bench- adding docstrings and avoid the empty list default argument.

Reformat the flags help text for consistency, remove trailing whitespace.

PiperOrigin-RevId: 669022062
Change-Id: Id2eeaaea411f3acb4b02478e5be9ff6fb5dad8b6

Author: rjogrady

fleetbench/parallel/parallel_bench.py

@@ -52,28 +52,26 @@
     "benchmark_filter",
     [],
     """Specifies subset of benchmarks to run.
-
     Filtering options:
     - Empty list: Selects all default benchmarks.
-    - Keyword list: Selects benchmarks from the default list matching any \n
-        provided keyword, one keyword per filter \n
-        (e.g., "--benchmark_filter=Cold --benchmark_filter=Hot").""",
+    - Keyword list: Selects benchmarks from the default list matching any
+      provided keyword, one keyword per filter
+      (e.g., "--benchmark_filter=Cold --benchmark_filter=Hot")
+    """,
 )
 
 
 _WORKLOAD_FILTER = flags.DEFINE_multi_string(
     "workload_filter",
     [],
-    """Selects benchmarks associated with specified workloads. This will \n
+    """Selects benchmarks associated with specified workloads. This will
        overwrite the `--benchmark_filter` flag.
-
     Filtering options:
-    - Workload name + keyword(s): Selects benchmarks associated with the \n
-        specified workload, further filtered by keywords 
-        (e.g., "--workload_filter=libc,Memcpy,Memcmp").
-    - Workload name + "all": Selects all benchmarks associated with the \n
-        specified workload 
-        (e.g., "--workload_filter=proto,all")
+    - Workload name + keyword(s): Selects benchmarks associated with the
+      specified workload, further filtered by keywords
+      (e.g., "--workload_filter=libc,Memcpy,Memcmp").
+    - Workload name + "all": Selects all benchmarks associated with the
+      specified workload (e.g., "--workload_filter=proto,all")
     """,
 )
 
@@ -109,9 +107,9 @@
 _NUM_CPUS = flags.DEFINE_integer(
     "num_cpus",
     len(cpu.Available()),
-    "Number of CPUs to use. Note that 1 CPU will be reserved for scheduling."
-    " The lowest available core will be used for the control thread, and the"
-    " remaining up to num_cpus - 1 will be used for the workers.",
+    """Number of CPUs to use. Note that 1 CPU will be reserved for scheduling.
+    The lowest available core will be used for the control thread, and the
+     remaining up to num_cpus - 1 will be used for the workers.""",
     lower_bound=2,
     upper_bound=len(cpu.Available()),
 )

fleetbench/parallel/parallel_bench_lib.py

@@ -62,6 +62,13 @@ def _GetDefaultBenchmarks(
   - Empty list: Returns all default benchmarks.
   - Keyword list: Returns benchmarks from the default list matching the provided
                   keyword (e.g., "Cold Hot").
+
+  Args:
+    benchmark_target: Path to the benchmark binary.
+    benchmark_filters: List of filters to apply to the benchmarks to run.
+
+  Returns:
+    A map of benchmark names to Benchmark objects.
   """
   benchmarks = {}
   sub_benchmarks = bm.GetSubBenchmarks(benchmark_target)
@@ -89,6 +96,12 @@ def _GetWorkloadBenchmarks(
         "libc,Memcpy,Memcmp").
     - Workload name + "all": Returns all benchmarks associated with the
         specified workload (e.g., "proto,all").
+  Args:
+    benchmark_target: Path to the benchmark binary.
+    workload_filters: List of filters to apply to the benchmarks to run.
+
+  Returns:
+    A map of benchmark names to Benchmark objects.
   """
   benchmarks = {}
 
@@ -177,7 +190,8 @@ def __init__(
       raise ValueError("Must specify at least 2 CPUs.")
 
     # Reserve a CPU for the controller loop.
-    self.controller_cpu, *self.cpus = cpus
+    self.controller_cpu = cpus[0]
+    self.cpus = cpus[1:]
     self.cpu_affinity = cpu_affinity
     self.benchmarks: dict[str, bm.Benchmark] = {}
     self.target_utilization = utilization * 100
@@ -191,8 +205,8 @@ def __init__(
   def _PreRun(
       self,
       benchmark_target: str,
-      benchmark_filters: list[str],
-      workload_filters: list[str],
+      benchmark_filters: list[str] | None,
+      workload_filters: list[str] | None,
       benchmark_perf_counters: str,
       benchmark_repetitions: int,
       benchmark_min_time: str,
@@ -332,6 +346,7 @@ def _RunSchedulingLoop(self) -> None:
           self.results.append(r)
 
   def GenerateBenchmarkReport(self) -> None:
+    """Print some aggregated results of the benchmark runs."""
     utilization = pd.DataFrame(
         self.utilization_samples, columns=["timestamp", "utilization"]
     )
@@ -399,8 +414,8 @@ def GeneratePerfCounterReport(self, counters: list[str]) -> None:
   def Run(
       self,
       benchmark_target: str,
-      benchmark_filter: list[str] = [],
-      workload_filter: list[str] = [],
+      benchmark_filter: list[str] | None = None,
+      workload_filter: list[str] | None = None,
       benchmark_perf_counters: str = "",
       benchmark_repetitions: int = 0,
       benchmark_min_time: str = "",