add runner script for slurm

pyproject.toml

@@ -27,7 +27,7 @@ dev = [
     "parameterized",
     "expecttest",
     "numpy",
-    "torchx",
+    "torchx-nightly",
     "lintrunner",
     "lintrunner-adapters",
 ]

torchft/examples/slurm/README.md

@@ -0,0 +1,34 @@
+## Launch lighthouse
+
+Run this command to launch the lighthouse somewhere and make sure other slurm nodes have access to this node
+
+
+```
+RUST_BACKTRACE=1 torchft_lighthouse --min_replicas 1 --quorum_tick_ms 100 --join_timeout_ms 10000
+```
+
+## Launch training
+
+First, go to your local torchtitan folder and run
+
+```bash
+$ pip install -r requirements.txt
+$ pip install .
+```
+
+Run the following command to launch torchft lighthouse and replicas using torchtitan on slurm
+
+```bash
+$ pip install torchx-nightly
+$ # Set the address of the lighthouse server e.g.
+$ export TORCHFT_LIGHTHOUSE=http://slurm-head-node-0:29510
+$ python runner.py --workspace-dir=/path/to/torchtitan/folder --nodes=1 --nproc-per-node=8 --replica-count=2
+```
+
+## Test fault tolerance
+
+To inject some failures, you can use the following command
+
+```bash
+$ python punisher.py kill_loop --num-failures=10 --mtbf-secs=300
+```

torchft/examples/slurm/punisher.py

@@ -0,0 +1,89 @@
+import argparse
+import logging
+import random
+import time
+
+from torchx import specs
+from torchx.runner import Runner, get_runner
+
+logging.basicConfig(level=logging.INFO)
+logger: logging.Logger = logging.getLogger(__name__)
+
+_SCHEDULER = "slurm"
+
+
+def kill_all(runner: Runner) -> None:
+    jobs = runner.list(_SCHEDULER)
+    jobs = [job for job in jobs if job.state == specs.AppState.RUNNING]
+    for job in jobs:
+        if "ft_" not in job.name:
+            continue
+        print(f"killing {job.app_handle}")
+        runner.cancel(job.app_handle)
+
+
+def kill_one(runner: Runner) -> None:
+    jobs = runner.list(_SCHEDULER)
+    jobs = [job for job in jobs if job.state == specs.AppState.RUNNING]
+    candidates = []
+    for job in jobs:
+        if "ft_" not in job.name:
+            continue
+        if "ft_0" in job.name:
+            continue
+        candidates.append(job.app_handle)
+    choice = random.choice(candidates)
+    print(f"killing {choice=} {candidates=}")
+    runner.cancel(choice)
+
+
+def kill_loop(runner: Runner, args: argparse.Namespace) -> None:
+    for _ in range(args.num_failures):
+        kill_one(runner)
+        dur = random.random() * (2 * args.mtbf_secs)
+        print(f"sleeping for {dur=} {args.mtbf_secs=}")
+        time.sleep(args.mtbf_secs)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="CLI tool to inject failures on slurm")
+    subparsers = parser.add_subparsers(dest="command", help="Available commands")
+
+    # kill_loop subcommand
+    kill_loop_parser = subparsers.add_parser("kill_loop", help="Kill jobs in a loop")
+    kill_loop_parser.add_argument(
+        "--mtbf-secs",
+        type=float,
+        default=5,
+        help="Mean time between failures",
+    )
+    kill_loop_parser.add_argument(
+        "--num-failures",
+        type=int,
+        default=1,
+        help="Number of failures to inject",
+    )
+
+    # kill_one subcommand
+    subparsers.add_parser("kill_one", help="Kill a single job")
+
+    # kill_all subcommand
+    subparsers.add_parser("kill_all", help="Kill all jobs")
+
+    args = parser.parse_args()
+
+    if args.command is None:
+        parser.print_help()
+        return
+
+    with get_runner() as runner:
+        if args.command == "kill_loop":
+            kill_loop(runner, args)
+        elif args.command == "kill_one":
+            kill_one(runner)
+        elif args.command == "kill_all":
+            kill_all(runner)
+
+
+if __name__ == "__main__":
+    main()

torchft/examples/slurm/runner.py

@@ -0,0 +1,215 @@
+import argparse
+import logging
+import os
+import time
+
+from torchx import specs
+from torchx.components.dist import ddp
+from torchx.runner import Runner, get_runner
+
+logging.basicConfig(level=logging.INFO)
+logger: logging.Logger = logging.getLogger(__name__)
+
+_SCHEDULER = "slurm"
+
+
+def _make_app(replica_id: int, cli_args: argparse.Namespace) -> specs.AppDef:
+    args = [
+        "--comm.trace_buf_size=0",
+        "--comm.train_timeout_seconds=60",
+        "--metrics.log_freq=1",
+        "--profiling.enable_profiling",
+        "--experimental.custom_args_module=torchtitan.components.ft.config",
+        "--job.config_file=./torchtitan/models/llama3/train_configs/llama3_8b.toml",
+        "--model.name=llama3_ft",
+        "--training.dataset=c4",
+        "--training.steps=10000",
+        "--training.local_batch_size=2",
+        f"--parallelism.data_parallel_shard_degree={cli_args.nodes * cli_args.nproc_per_node}",
+        "--fault_tolerance.enable",
+        f"--fault_tolerance.replica_id={replica_id}",
+        f"--fault_tolerance.group_size={cli_args.replica_count}",
+        f"--fault_tolerance.process_group={cli_args.process_group}",
+        f"--fault_tolerance.process_group_timeout_ms={600 * 1000}",
+    ]
+
+    if cli_args.enable_semi_sync:
+        args += [
+            f"--fault_tolerance.semi_sync_method={cli_args.semi_sync_method}",
+        ]
+
+    if cli_args.semi_sync_method == "diloco":
+        args += [
+            "--fault_tolerance.sync_steps=20",
+            "--fault_tolerance.fragment_sync_delay=1",
+            f"--fault_tolerance.num_fragments={cli_args.num_fragments}",
+        ]
+
+    if replica_id == 0:
+        args += [
+            "--metrics.enable-wandb",
+            "--checkpoint.interval=100",
+        ]
+
+    env = {}
+
+    # use agent store in torchelastic to avoid TCPStore init race condition
+    env["TORCH_SHARE_RDZV_TCP_STORE"] = "1"
+    env["TORCH_CPP_LOG_LEVEL"] = "INFO"
+
+    env["TORCH_CUDA_SANITIZER=1"] = "1"
+
+    # NCCL envs for debugging
+    env["NCCL_DEBUG"] = "INFO"
+    env["NCCL_DEBUG_SUBSYS"] = "ALL"
+    env["NCCL_PROTO"] = "Simple"
+
+    # gloo
+    if os.environ.get("GLOO_SOCKET_IFNAME") is not None:
+        env["GLOO_SOCKET_IFNAME"] = os.environ.get("GLOO_SOCKET_IFNAME")
+
+    # application log levels
+    env["LOGLEVEL"] = "INFO"
+    env["RUST_LOGS"] = "INFO"
+    env["TORCH_CPP_LOG_LEVEL"] = "INFO"
+
+    # application timeouts
+    env["TORCHFT_QUORUM_TIMEOUT_SEC"] = "900"
+    env["TORCHFT_TIMEOUT_SEC"] = "600"
+    env["TORCHFT_QUORUM_RETRIES"] = "0"
+
+    env["TORCHFT_LIGHTHOUSE"] = os.environ.get(
+        "TORCHFT_LIGHTHOUSE", "http://slurm-head-node-0:29510"
+    )
+
+    env["WANDB_PROJECT"] = "torchft"
+
+    app = ddp(
+        *args,
+        name=f"ft_{replica_id}",
+        env=env,
+        script="./torchtitan/train.py",
+        gpu=cli_args.nproc_per_node,
+        j=f"{cli_args.nodes}x{cli_args.nproc_per_node}",
+    )
+    app.roles[0].name = app.name
+    return app
+
+
+def start_replica(
+    runner: Runner, replica_id: int, args: argparse.Namespace
+) -> specs.AppHandle:
+    app = _make_app(replica_id, args)
+
+    app_handle = runner.run(
+        app,
+        scheduler=_SCHEDULER,
+    )
+
+    return app_handle
+
+
+def monitor(runner: Runner, args: argparse.Namespace) -> None:
+    jobs = runner.list(_SCHEDULER)
+    jobs = [job for job in jobs if job.state == specs.AppState.RUNNING]
+
+    active_replicas = {}
+
+    for job in jobs:
+        if "ft_" not in job.name:
+            continue
+        name, _, _ = job.name.partition("-")
+        _, _, replica_id_str = name.partition("_")
+        replica_id = int(replica_id_str)
+        active_replicas[replica_id] = job
+
+    to_launch = set()
+    for replica_id in range(args.replica_count):
+        alive = replica_id in active_replicas
+
+        if alive:
+            job = active_replicas[replica_id]
+            print(f" - {replica_id=:2d}: ALIVE {job.app_handle}")
+        else:
+            print(f" - {replica_id=:2d}: DEAD")
+            to_launch.add(replica_id)
+
+    for replica_id in to_launch:
+        app_handle = start_replica(
+            runner,
+            replica_id,
+            args,
+        )
+        print(f"launched {replica_id=}: {app_handle=}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="CLI tool lauch data parallel replicas on slurm"
+    )
+
+    parser.add_argument(
+        "--workspace-dir", type=str, help="Location of torchtitan folder"
+    )
+
+    parser.add_argument(
+        "--nodes",
+        type=int,
+        default=10,
+        help="Number of nodes per replica",
+    )
+
+    parser.add_argument(
+        "--nproc-per-node",
+        type=int,
+        default=10,
+        help="Number of ranks per node",
+    )
+
+    parser.add_argument(
+        "--replica-count",
+        type=int,
+        default=10,
+        help="Number of data parallel replicas",
+    )
+
+    parser.add_argument(
+        "--process-group",
+        type=str,
+        default="gloo",
+        help="The process group to use for data parallel",
+    )
+
+    parser.add_argument(
+        "--enable-semi-sync",
+        type=bool,
+        default=True,
+        help="Whether to enable semi-sync method for data parallel",
+    )
+
+    parser.add_argument(
+        "--semi-sync-method",
+        type=str,
+        default="diloco",
+        help="The semi-sync method to use for data parallel. Options: diloco, local_sgd",
+    )
+
+    parser.add_argument(
+        "--num-fragments",
+        type=int,
+        default=2,
+        help="The number of fragments to use for data parallel. Only used for diloco semi-sync method",
+    )
+
+    args = parser.parse_args()
+
+    os.chdir(args.workspace_dir)
+
+    with get_runner() as runner:
+        while True:
+            monitor(runner, args)
+            time.sleep(10)
+
+
+if __name__ == "__main__":
+    main()