manager_integ_tests: added recovery test (#28)

src/lib.rs

@@ -44,9 +44,9 @@ impl Manager {
         bind: String,
         store_addr: String,
         world_size: u64,
-    ) -> Self {
+    ) -> PyResult<Self> {
         py.allow_threads(move || {
-            let runtime = Runtime::new().unwrap();
+            let runtime = Runtime::new()?;
             let manager = runtime
                 .block_on(manager::Manager::new(
                     replica_id,
@@ -56,13 +56,13 @@ impl Manager {
                     store_addr,
                     world_size,
                 ))
-                .unwrap();
+                .map_err(|e| PyRuntimeError::new_err(e.to_string()))?;
             let handle = runtime.spawn(manager.clone().run());
-            Self {
+            Ok(Self {
                 handle: handle,
                 manager: manager,
                 _runtime: runtime,
-            }
+            })
         })
     }
 
@@ -89,7 +89,7 @@ impl ManagerClient {
     #[new]
     fn new(py: Python<'_>, addr: String, timeout: Duration) -> PyResult<Self> {
         py.allow_threads(move || {
-            let runtime = Runtime::new().unwrap();
+            let runtime = Runtime::new()?;
             let client = runtime
                 .block_on(manager::manager_client_new(addr, timeout))
                 .map_err(|e| PyRuntimeError::new_err(e.to_string()))?;
@@ -193,14 +193,16 @@ fn reset_python_signals(py: Python<'_>) -> PyResult<()> {
 }
 
 #[pyfunction]
-fn lighthouse_main(py: Python<'_>) {
-    reset_python_signals(py).unwrap();
+fn lighthouse_main(py: Python<'_>) -> PyResult<()> {
+    reset_python_signals(py)?;
 
     let mut args = env::args();
     args.next(); // discard binary arg
     let opt = lighthouse::LighthouseOpt::from_iter(args);
-    let rt = Runtime::new().unwrap();
-    rt.block_on(lighthouse_main_async(opt)).unwrap();
+    let rt = Runtime::new()?;
+    rt.block_on(lighthouse_main_async(opt))
+        .map_err(|e| PyRuntimeError::new_err(e.to_string()))?;
+    Ok(())
 }
 
 async fn lighthouse_main_async(opt: lighthouse::LighthouseOpt) -> Result<()> {
@@ -223,7 +225,7 @@ impl Lighthouse {
     #[new]
     fn new(py: Python<'_>, bind: String, min_replicas: u64) -> PyResult<Self> {
         py.allow_threads(move || {
-            let rt = Runtime::new().unwrap();
+            let rt = Runtime::new()?;
 
             let lighthouse = rt
                 .block_on(lighthouse::Lighthouse::new(lighthouse::LighthouseOpt {
@@ -232,7 +234,7 @@ impl Lighthouse {
                     join_timeout_ms: 100,
                     quorum_tick_ms: 100,
                 }))
-                .unwrap();
+                .map_err(|e| PyRuntimeError::new_err(e.to_string()))?;
 
             Ok(Self {
                 handle: rt.spawn(lighthouse.clone().run()),
@@ -261,7 +263,7 @@ fn torchft(m: &Bound<'_, PyModule>) -> PyResult<()> {
         .show_module_names(true)
         .timestamp(stderrlog::Timestamp::Millisecond)
         .init()
-        .unwrap();
+        .map_err(|e| PyRuntimeError::new_err(e.to_string()))?;
 
     m.add_class::<Manager>()?;
     m.add_class::<ManagerClient>()?;

torchft/manager.py

@@ -140,6 +140,7 @@ def __init__(
             wait_for_workers=False,
         )
         self._pg = pg
+        self._manager = None
 
         if rank == 0:
             hostname = socket.gethostname()
@@ -148,7 +149,8 @@ def __init__(
             lighthouse_addr = lighthouse_addr or os.environ["TORCHFT_LIGHTHOUSE"]
 
             if replica_id is None:
-                replica_id = str(uuid.uuid4())
+                replica_id = ""
+            replica_id = replica_id + str(uuid.uuid4())
             self._manager = _Manager(
                 replica_id=replica_id,
                 lighthouse_addr=lighthouse_addr,
@@ -180,6 +182,8 @@ def shutdown(self) -> None:
         Shutdown the manager and checkpoint server.
         """
         self._ckpt_server.shutdown()
+        if self._manager is not None:
+            self._manager.shutdown()
 
     def allreduce_grad(self, grad: torch.Tensor) -> torch.futures.Future[torch.Tensor]:
         """
@@ -364,7 +368,7 @@ def _async_quorum(self) -> None:
                 self._participating_rank = None
 
         if quorum_id != self._quorum_id:
-            logger.info(f"reconfiguring for quorum_id {quorum_id}")
+            logger.info(f"{replica_rank=} reconfiguring for quorum_id {quorum_id}")
             store_prefixed_addr = f"{store_address}/torchft/{quorum_id}/{self._rank}"
             # We use the replica rank and world as we want all replicas in the PG.
             self._pg.configure(store_prefixed_addr, replica_rank, replica_world_size)
@@ -373,7 +377,7 @@ def _async_quorum(self) -> None:
         # See manager.rs for healing conditions
         if heal:
             self._healing = True
-            logger.info("healing required")
+            logger.info(f"{replica_rank}= healing required")
 
             logger.info(f"fetching checkpoint server address from {address}")
             primary_client = ManagerClient(address, timeout=self._timeout)

torchft/manager_integ_test.py

@@ -1,4 +1,6 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
+from contextlib import ExitStack
+from typing import Set, Tuple
 from unittest import TestCase
 
 import torch
@@ -24,63 +26,108 @@ def forward(self, x):
         return self.model(x)
 
 
-def train_loop(replica_id: int, lighthouse_address: str) -> None:
-    store = dist.TCPStore(
-        host_name="localhost",
-        port=0,
-        is_master=True,
-        wait_for_workers=False,
-    )
-
-    def load_state_dict(state_dict):
-        m.load_state_dict(state_dict["model"])
-        optimizer.load_state_dict(state_dict["optim"])
-
-    def state_dict():
-        return {
-            "model": m.state_dict(),
-            "optim": optimizer.state_dict(),
-        }
-
-    pg = ProcessGroupGloo()
-    manager = Manager(
-        pg=pg,
-        min_replica_size=2,
-        load_state_dict=load_state_dict,
-        state_dict=state_dict,
-        replica_id=str(replica_id),
-        store_addr="localhost",
-        store_port=store.port,
-        rank=0,
-        world_size=1,
-        lighthouse_addr=lighthouse_address,
-        port=19530 + replica_id,
-    )
-    m = DistributedDataParallel(manager, MyModel())
-    optimizer = OptimizerWrapper(manager, optim.Adam(m.parameters()))
-    criterion = nn.CrossEntropyLoss()
-
-    while True:
-        inputs = torch.rand(2, 3)
-        labels = torch.randint(4, (2,))
-
-        optimizer.zero_grad()
-        out = m(inputs)
-        loss = criterion(out, labels)
-
-        loss.backward()
-        optimizer.step()
-
-        # TODO: assert weights are equal across replicas
-
-        if manager.current_step() >= 5:
-            break
-
-    manager.shutdown()
+class InjectedFailure(Exception):
+    pass
+
+
+class FailureInjector:
+    def __init__(self) -> None:
+        self._failures: Set[int] = set()
+        self.count = 0
+
+    def fail_at(self, step: int) -> "FailureInjector":
+        self._failures.add(step)
+        return self
+
+    def check(self, step: int) -> None:
+        if step in self._failures:
+            self.count += 1
+            self._failures.remove(step)
+            print(f"injecting failure {step=}")
+            raise InjectedFailure(f"injected failure {step=}")
+
+
+def worker_manager(
+    replica_id: int,
+    lighthouse_address: str,
+    failure_injector: FailureInjector,
+    attempts: int = 3,
+) -> None:
+    for i in range(attempts):
+        try:
+            print(f"starting worker {replica_id} attempt {i}")
+            return train_loop(
+                replica_id, lighthouse_address, failure_injector=failure_injector
+            )
+        except InjectedFailure as e:
+            print("got injected failure", i, e)
+            if i == attempts - 1:
+                raise
+            continue
+
+
+def train_loop(
+    replica_id: int, lighthouse_address: str, failure_injector: FailureInjector
+) -> None:
+    with ExitStack() as stack:
+        store = dist.TCPStore(
+            host_name="localhost",
+            port=0,
+            is_master=True,
+            wait_for_workers=False,
+        )
+
+        def load_state_dict(state_dict):
+            m.load_state_dict(state_dict["model"])
+            optimizer.load_state_dict(state_dict["optim"])
+
+        def state_dict():
+            return {
+                "model": m.state_dict(),
+                "optim": optimizer.state_dict(),
+            }
+
+        pg = ProcessGroupGloo()
+        manager = Manager(
+            pg=pg,
+            min_replica_size=2,
+            load_state_dict=load_state_dict,
+            state_dict=state_dict,
+            replica_id=str(replica_id),
+            store_addr="localhost",
+            store_port=store.port,
+            rank=0,
+            world_size=1,
+            lighthouse_addr=lighthouse_address,
+            port=19530 + replica_id,
+        )
+        stack.callback(manager.shutdown)
+
+        m = DistributedDataParallel(manager, MyModel())
+        optimizer = OptimizerWrapper(manager, optim.Adam(m.parameters()))
+        criterion = nn.CrossEntropyLoss()
+
+        while True:
+            print(f"worker {replica_id} starting step {manager.current_step()}")
+            inputs = torch.rand(2, 3)
+            labels = torch.randint(4, (2,))
+
+            optimizer.zero_grad()
+            out = m(inputs)
+            loss = criterion(out, labels)
+
+            loss.backward()
+            optimizer.step()
+
+            if manager.current_step() >= 5:
+                # return state_dict so we can check consistency
+                return state_dict()
+
+            failure_injector.check(manager.current_step())
 
 
 class ManagerIntegTest(TestCase):
-    def test_ddp(self):
+    def test_ddp_healthy(self):
         lighthouse = Lighthouse(
             bind="[::]:0",
             min_replicas=2,
@@ -90,11 +137,60 @@ def test_ddp(self):
 
         with ThreadPoolExecutor(max_workers=num_replicas) as executor:
             for replica_id in range(num_replicas):
+                failure_injector = FailureInjector()
+                futures.append(
+                    executor.submit(
+                        worker_manager,
+                        replica_id,
+                        lighthouse.address(),
+                        failure_injector=failure_injector,
+                    )
+                )
+
+        state_dicts = []
+
+        for fut in as_completed(futures):
+            state_dicts.append(fut.result())
+
+        lighthouse.shutdown()
+
+        for state_dict in state_dicts:
+            torch.testing.assert_close(state_dict, state_dicts[0])
+
+    def test_ddp_recovery(self):
+        lighthouse = Lighthouse(
+            bind="[::]:0",
+            min_replicas=2,
+        )
+        num_replicas = 2
+        futures = []
+
+        failure_injectors = [
+            FailureInjector(),
+            FailureInjector().fail_at(2),
+        ]
+
+        with ThreadPoolExecutor(max_workers=num_replicas) as executor:
+            for replica_id, failure_injector in zip(
+                range(num_replicas), failure_injectors
+            ):
                 futures.append(
-                    executor.submit(train_loop, replica_id, lighthouse.address())
+                    executor.submit(
+                        worker_manager,
+                        replica_id,
+                        lighthouse.address(),
+                        failure_injector=failure_injector,
+                    )
                 )
 
+        state_dicts = []
+
         for fut in as_completed(futures):
-            fut.result()
+            state_dicts.append(fut.result())
 
         lighthouse.shutdown()
+
+        for state_dict in state_dicts:
+            torch.testing.assert_close(state_dict, state_dicts[0])
+
+        self.assertEqual(failure_injectors[1].count, 1)