torchelastic: change monitor_interval default to 0.1 (#124692)

This reduces the default monitor_interval for torchelastic to 0.1s as testing shows negligble load for common use cases. Even at the extremes, 100k processes is only 45.4% cpu util of a single core.

Torchelastic monitor_interval only monitors the processes on a single worker so under typical loads even for huge jobs we expect ~8 subprocesses per machine with one per GPU.

As an external datapoint, Python's wait polls every 50usec-50ms (https://github.com/python/cpython/blob/main/Lib/subprocess.py#L2035).

## Motivation

This setting is used to control how frequently we poll for failed processes in elastic.

* For some jobs of note we run elastic 3 times per try so with the default timeout of 5 seconds we should save ~15 seconds per retry.
* @kiukchung's use case: Apparently this is annoying in notebooks etc since it adds delay to shutdown when testing things

## Results

This is measured in cores (100% is a single core under full load).

| monitor_interval (s) | nproc-per-node | CPU util (highest observed) |
| -------------------- | -------------- | --------------------------- |
| 1.0                  | 10             | 0.2%                        |
| 0.1                  | 1              | 0.4%                        |
| 0.1                  | 10             | 0.4%                        |
| 0.01                 | 10             | 0.9%                        |
| 0.001                | 10             | 4.0%                        |
| 0.1                  | 100            | 0.5%                        |
| 0.1                  | 1000           | 2.2%                        |
| 0.1                  | 10000          | 15.7%                       |
| 0.1                  | 100000         | 45.4%                       |

## Methodology

```sh
# run command
$ LOGLEVEL=INFO torchrun --nnodes 1 --nproc-per-node 10 --monitor-interval 0.1 ~/wait.py

# wait a few seconds for all processes to start and reach steady state and then run, wait ~30s or 3 prints and take the highest
$ top -b -d 10 -c | rg 'torchrun.*wait
```

wait.py

```py
import time

time.sleep(10*60)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124692
Approved by: https://github.com/kiukchung, https://github.com/kurman

test/distributed/elastic/agent/server/test/api_test.py

@@ -54,7 +54,7 @@ class WorkerGroupTest(unittest.TestCase):
             args=(),
             rdzv_handler=None,
             max_restarts=50,
-            monitor_interval=1,
+            monitor_interval=0.1,
         )
         worker_group = WorkerGroup(spec)
 
@@ -157,7 +157,7 @@ class SimpleElasticAgentTest(unittest.TestCase):
     def _get_worker_spec(
         self,
         max_restarts=1,
-        monitor_interval=1.0,
+        monitor_interval=0.1,
         role="test_trainer",
         local_world_size=8,
         local_addr=None,

test/distributed/launcher/api_test.py

@@ -70,7 +70,7 @@ def get_test_launch_config(
         nproc_per_node=nproc_per_node,
         run_id=run_id,
         rdzv_endpoint=rdzv_endpoint,
-        monitor_interval=1,
+        monitor_interval=0.1,
         rdzv_backend=rdzv_backend,
         start_method="spawn",
         max_restarts=0,

torch/distributed/elastic/agent/server/api.py

@@ -85,7 +85,7 @@ class WorkerSpec:
     entrypoint: Union[Callable, str, None] = None
     args: Tuple = ()
     max_restarts: int = 3
-    monitor_interval: float = 30.0
+    monitor_interval: float = 0.1
     master_port: Optional[int] = None
     master_addr: Optional[str] = None
     local_addr: Optional[str] = None

torch/distributed/launcher/api.py

@@ -75,7 +75,7 @@ class LaunchConfig:
     rdzv_configs: Dict[str, Any] = field(default_factory=dict)
     rdzv_timeout: int = -1
     max_restarts: int = 3
-    monitor_interval: float = 30
+    monitor_interval: float = 0.1
     start_method: str = "spawn"
     log_line_prefix_template: Optional[str] = None
     metrics_cfg: Dict[str, str] = field(default_factory=dict)

torch/distributed/run.py

@@ -499,7 +499,7 @@ def get_args_parser() -> ArgumentParser:
         "--monitor_interval",
         action=env,
         type=float,
-        default=5,
+        default=0.1,
         help="Interval, in seconds, to monitor the state of workers.",
     )
     parser.add_argument(