refactor diloco test (#232)

Summary:
- move the training loop to a separate file
- convert it into a class so that methods can be overridden without having to duplicate code

Author: tushar00jain

torchft/local_sgd_integ_test.py

@@ -33,38 +33,11 @@
     ProcessGroupBabyNCCL,
     ProcessGroupGloo,
 )
+from torchft.test.diloco_trainer import DiLoCoTrainer, MultiMyModel
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 
-class MultiMyModel(torch.nn.Module):
-    def __init__(self, in_dim: int = 3, out_dim: int = 4, n_layers: int = 1) -> None:
-        super().__init__()
-        self.in_dim = in_dim
-
-        self.layers = torch.nn.ModuleList()
-        for i in range(n_layers):
-            self.layers.append(MyModel(in_dim, out_dim))
-            in_dim, out_dim = out_dim, in_dim
-
-        self.out_dim = in_dim
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        for layer in self.layers:
-            x = layer(x)
-        return x
-
-    def get_rand_inputs(
-        self, batch_size: int, device: torch.device = torch.device("cpu")
-    ) -> torch.Tensor:
-        return torch.rand(batch_size, self.in_dim, device=device)
-
-    def get_rand_labels(
-        self, batch_size: int, device: torch.device = torch.device("cpu")
-    ) -> torch.Tensor:
-        return torch.randint(self.out_dim, (batch_size,), device=device)
-
-
 def local_sgd_train_loop(
     rank: int,
     store_port: int,
@@ -148,158 +121,11 @@ def diloco_train_loop(
     diloco_args = train_loop_args.get("diloco_args", {})
 
     with ExitStack() as stack:
-        # Declare the model and optimizers
-        m = MultiMyModel(2, 3, n_fragments)
-        m.load_state_dict(model_state_dict)
-        m.to(device)
-
-        # Setup optimizers
-        inner_optimizer: optim.Optimizer = torch.optim.AdamW(
-            m.parameters(), lr=4e-4, weight_decay=0.1, betas=(0.9, 0.95)
+        trainer = DiLoCoTrainer(
+            rank, store_port, device, runner, model_state_dict, n_fragments, diloco_args
         )
-
-        # Create one outer optimizer per fragment
-        outer_optimizers = []
-        for _, layer in enumerate(m.layers):
-            outer_optimizers.append(
-                torch.optim.SGD(layer.parameters(), lr=0.7, momentum=0.9, nesterov=True)
-            )
-
-        # pyre-ignore[53]
-        def load_state_dict(state_dict: Dict[str, Dict[str, object]]) -> None:
-            m.load_state_dict(state_dict["model"])
-            m.to(device)
-
-            # Load original parameters for each fragment
-            for i, fragment in enumerate(diloco._fragments):
-                fragment.original_parameters = cast(
-                    Dict[str, torch.Tensor], state_dict["original_params"][f"{i}"]
-                )
-
-            for fragment in diloco._fragments:
-                for name in fragment.original_parameters.keys():
-                    fragment.original_parameters[name] = fragment.original_parameters[
-                        name
-                    ].to(device)
-
-            inner_optimizer.load_state_dict(state_dict["inner_optim"])
-            for i, optimizer in enumerate(outer_optimizers):
-                optimizer.load_state_dict(state_dict[f"outer_optim"][f"{i}"])
-
-        def state_dict() -> Dict[str, Dict[str, object]]:  # pyre-ignore[53]
-            return {
-                "model": m.state_dict(),
-                "original_params": {
-                    f"{i}": fragment.original_parameters
-                    for i, fragment in enumerate(diloco._fragments)
-                },
-                "inner_optim": inner_optimizer.state_dict(),
-                "outer_optim": {
-                    f"{i}": optimizer.state_dict()
-                    for i, optimizer in enumerate(outer_optimizers)
-                },
-            }
-
-        print(f"worker {runner.replica_id=} {rank=} {runner.world_size=} starting")
-
-        if device.type == "cuda":
-            pg = FakeProcessGroupWrapper(ProcessGroupBabyNCCL())
-        else:
-            pg = FakeProcessGroupWrapper(
-                ProcessGroupGloo(timeout=timedelta(seconds=10))
-            )
-        manager = Manager(
-            pg=pg,
-            min_replica_size=2,
-            use_async_quorum=False,
-            load_state_dict=load_state_dict,
-            state_dict=state_dict,
-            replica_id=str(runner.replica_id),
-            store_addr="localhost",
-            store_port=store_port,
-            rank=rank,
-            world_size=runner.world_size,
-            lighthouse_addr=runner.lighthouse_address,
-            port=19530 + runner.replica_id,
-            connect_timeout=timedelta(seconds=10),
-            quorum_timeout=timedelta(seconds=10),
-            timeout=timedelta(seconds=10),
-            # pyre-fixme[6]: Incompatible parameter type
-            **runner.manager_args,
-        )
-        runner.event_injector.set_pg(pg)
-        stack.callback(manager.shutdown)
-        # initialize default group for device mesh to work
-        if not torch.distributed.is_initialized():
-            # TODO: remove this try-except once pytorch is updated to 2.8.0 and can use localhost:0
-            try:
-                torch.distributed.init_process_group(
-                    init_method="tcp://localhost:0",
-                    rank=rank,
-                    world_size=runner.world_size,
-                )
-            except ValueError:
-                os.environ["MASTER_ADDR"] = "localhost"
-                os.environ["MASTER_PORT"] = "0"
-                os.environ["WORLD_SIZE"] = str(runner.world_size)
-                os.environ["RANK"] = str(rank)
-
-        device_type = device.type
-        ft_device_mesh = ft_init_device_mesh(
-            device_type=device_type,
-            mesh_shape=(runner.world_size, 1),
-            mesh_dim_names=("replicate", "none"),
-            replicate_dim=0,
-            manager=manager,
-        )
-        for layer in m.layers:
-            if isinstance(layer, nn.Linear):
-                for param in layer.parameters():
-                    param = DTensor.from_local(
-                        param,
-                        device_mesh=ft_device_mesh,
-                    )
-
-        criterion = nn.CrossEntropyLoss()
-        all_state_dicts = {}
-
-        if "sync_every" not in diloco_args:
-            diloco_args["sync_every"] = 2
-
-        with DiLoCo(
-            manager,
-            [layer for layer in m.layers],
-            inner_optimizer,
-            outer_optimizers,
-            backup_device=device,
-            **diloco_args,
-        ) as diloco:
-            while True:
-                runner.event_injector.check(rank, manager.current_step())
-
-                manager_curr_step = manager.current_step()
-                if manager_curr_step not in all_state_dicts:
-                    all_state_dicts[manager_curr_step] = copy.deepcopy(
-                        manager._manager_state_dict()
-                    )
-
-                batch_size = 1
-                inputs = m.get_rand_inputs(batch_size, device=device)
-                labels = m.get_rand_labels(batch_size, device=device)
-
-                out = m(inputs)
-                loss = criterion(out, labels)
-
-                inner_optimizer.zero_grad()
-                loss.backward()
-                inner_optimizer.step()
-
-                # after 4 model updates then break
-                if manager.current_step() >= 4:
-                    break
-
-        # return state_dict so we can check consistency
-        return all_state_dicts
+        stack.callback(trainer.manager.shutdown)
+        return trainer.train_loop()
     return {}