[App] Fixed Multi Node and add examples

examples/app_multi_node/README.md

@@ -0,0 +1,41 @@
+# Lightning & Multi Node Training
+
+Lightning supports makes multi-node training simple by providing a simple interface to orchestrate compute and data.
+
+## Multi Node with raw PyTorch
+
+You can run the multi-node raw PyTorch by running the following commands.
+
+```bash
+lightning run app app_torch_work.py
+```
+
+## Multi Node with raw PyTorch + Lite
+
+You can run the multi-node raw PyTorch and Lite by running the following commands.
+
+```bash
+lightning run app app_lite_work.py
+```
+
+## Multi Node with PyTorch Lightning
+
+Lightning supports running PyTorch Lightning from a script or within a Lightning Work.
+
+### Multi Node PyTorch Lightning Script
+
+```bash
+lightning run app app_pl_script.py
+```
+
+### Multi Node PyTorch Lightning Work
+
+```bash
+lightning run app app_pl_work.py
+```
+
+## Multi Node with any frameworks
+
+```bash
+lightning run app app_generic_work.py
+```

examples/app_multi_node/app_work.py → examples/app_multi_node/app_generic_work.py

@@ -1,4 +1,4 @@
-import lightning.app as L
+import lightning as L
 from lightning.app.components import MultiNode
 
 
@@ -7,16 +7,17 @@ def run(
         self,
         main_address: str,
         main_port: int,
+        num_nodes: int,
         node_rank: int,
     ):
-        print(f"ADD YOUR DISTRIBUTED CODE: {main_address} {main_port} {node_rank}")
+        print(f"ADD YOUR DISTRIBUTED CODE: {main_address} {main_port} {num_nodes} {node_rank}.")
 
 
 compute = L.CloudCompute("gpu")
 app = L.LightningApp(
     MultiNode(
         AnyDistributedComponent,
-        nodes=2,
+        num_nodes=2,
         cloud_compute=compute,
     )
 )

examples/app_multi_node/app_lite_work.py

@@ -0,0 +1,59 @@
+import os
+
+import torch
+
+import lightning as L
+from lightning.app.components import MultiNode
+from lightning.lite import LightningLite
+
+
+def distributed_train(lite: LightningLite):
+    # 1. Prepare distributed model and optimizer
+    model = torch.nn.Linear(32, 2)
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+    model, optimizer = lite.setup(model, optimizer)
+    criterion = torch.nn.MSELoss()
+
+    # 2. Train the model for 50 steps.
+    for step in range(50):
+        model.zero_grad()
+        x = torch.randn(64, 32).to(lite.device)
+        output = model(x)
+        loss = criterion(output, torch.ones_like(output))
+        print(f"global_rank: {lite.global_rank} step: {step} loss: {loss}")
+        lite.backward(loss)
+        optimizer.step()
+
+    # 3. Verify all processes have the same weights at the end of training.
+    weight = model.module.weight.clone()
+    torch.distributed.all_reduce(weight)
+    assert torch.equal(model.module.weight, weight / lite.world_size)
+
+    print("Multi Node Distributed Training Done!")
+
+
+class PyTorchDistributed(L.LightningWork):
+    def run(
+        self,
+        main_address: str,
+        main_port: int,
+        num_nodes: int,
+        node_rank: int,
+    ):
+
+        os.environ["MASTER_ADDR"] = main_address
+        os.environ["MASTER_PORT"] = str(main_port)
+        os.environ["NODE_RANK"] = str(node_rank)
+
+        lite = LightningLite(accelerator="auto", devices="auto", strategy="ddp_spawn", num_nodes=num_nodes)
+        lite.launch(function=distributed_train)
+
+
+compute = L.CloudCompute("gpu-fast-multi")  # 4xV100
+app = L.LightningApp(
+    MultiNode(
+        PyTorchDistributed,
+        num_nodes=2,
+        cloud_compute=compute,
+    )
+)

examples/app_multi_node/app_pl_work.py

@@ -0,0 +1,38 @@
+import os
+
+import lightning as L
+from lightning.app.components import MultiNode
+from lightning.pytorch.demos.boring_classes import BoringModel
+
+
+class PyTorchLightningDistributed(L.LightningWork):
+    def run(
+        self,
+        main_address: str,
+        main_port: int,
+        num_nodes: int,
+        node_rank: int,
+    ):
+        os.environ["MASTER_ADDR"] = main_address
+        os.environ["MASTER_PORT"] = str(main_port)
+        os.environ["NODE_RANK"] = str(node_rank)
+
+        model = BoringModel()
+        trainer = L.Trainer(
+            max_epochs=10,
+            devices="auto",
+            accelerator="auto",
+            num_nodes=num_nodes,
+            strategy="ddp_spawn",  # Only spawn based strategies are supported for now.
+        )
+        trainer.fit(model)
+
+
+compute = L.CloudCompute("gpu-fast-multi")  # 4xV100
+app = L.LightningApp(
+    MultiNode(
+        PyTorchLightningDistributed,
+        num_nodes=2,
+        cloud_compute=compute,
+    )
+)

examples/app_multi_node/app_torch_work.py

@@ -0,0 +1,70 @@
+import torch
+from torch.nn.parallel.distributed import DistributedDataParallel
+
+import lightning as L
+from lightning.app.components import MultiNode
+
+
+def distributed_train(local_rank: int, main_address: str, main_port: int, num_nodes: int, node_rank: int, nprocs: int):
+    # 1. Setting distributed environment
+    global_rank = local_rank + node_rank * nprocs
+    world_size = num_nodes * nprocs
+
+    if torch.distributed.is_available() and not torch.distributed.is_initialized():
+        torch.distributed.init_process_group(
+            "nccl" if torch.cuda.is_available() else "gloo",
+            rank=global_rank,
+            world_size=world_size,
+            init_method=f"tcp://{main_address}:{main_port}",
+        )
+
+    # 2. Prepare distributed model
+    model = torch.nn.Linear(32, 2)
+    device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu")
+    device_ids = device if torch.cuda.is_available() else None
+    model = DistributedDataParallel(model, device_ids=device_ids).to(device)
+
+    # 3. Prepare loss and optimizer
+    criterion = torch.nn.MSELoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+    # 4. Train the model for 50 steps.
+    for step in range(50):
+        model.zero_grad()
+        x = torch.randn(64, 32).to(device)
+        output = model(x)
+        loss = criterion(output, torch.ones_like(output))
+        print(f"global_rank: {global_rank} step: {step} loss: {loss}")
+        loss.backward()
+        optimizer.step()
+
+    # 5. Verify all processes have the same weights at the end of training.
+    weight = model.module.weight.clone()
+    torch.distributed.all_reduce(weight)
+    assert torch.equal(model.module.weight, weight / world_size)
+
+    print("Multi Node Distributed Training Done!")
+
+
+class PyTorchDistributed(L.LightningWork):
+    def run(
+        self,
+        main_address: str,
+        main_port: int,
+        num_nodes: int,
+        node_rank: int,
+    ):
+        nprocs = torch.cuda.device_count() if torch.cuda.is_available() else 1
+        torch.multiprocessing.spawn(
+            distributed_train, args=(main_address, main_port, num_nodes, node_rank, nprocs), nprocs=nprocs
+        )
+
+
+compute = L.CloudCompute("gpu-fast-multi")  # 4xV100
+app = L.LightningApp(
+    MultiNode(
+        PyTorchDistributed,
+        num_nodes=2,
+        cloud_compute=compute,
+    )
+)

examples/app_multi_node/train.py

@@ -1,7 +1,7 @@
-from pytorch_lightning import Trainer
-from pytorch_lightning.demos.boring_classes import BoringModel
+import lightning as L
+from lightning.pytorch.demos.boring_classes import BoringModel
 
 if __name__ == "__main__":
     model = BoringModel()
-    trainer = Trainer(max_epochs=1)
+    trainer = L.Trainer(max_epochs=1)
     trainer.fit(model)

src/lightning_app/CHANGELOG.md

@@ -51,7 +51,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed missing root flow among the flows of the app ([#15531](https://github.com/Lightning-AI/lightning/pull/15531))
 
--
+- Fixed bug with Multi Node Component and add some examples ([#15557](https://github.com/Lightning-AI/lightning/pull/15557))
 
 
 

src/lightning_app/components/multi_node.py

@@ -11,7 +11,7 @@ class MultiNode(LightningFlow):
     def __init__(
         self,
         work_cls: Type["LightningWork"],
-        nodes: int,
+        num_nodes: int,
         cloud_compute: "CloudCompute",
         *work_args: Any,
         **work_kwargs: Any,
@@ -39,22 +39,22 @@ def run(
             app = L.LightningApp(
                 MultiNode(
                     AnyDistributedComponent,
-                    nodes=8,
+                    num_nodes=8,
                     cloud_compute=compute,
                 )
             )
 
         Arguments:
             work_cls: The work to be executed
-            nodes: Number of nodes.
+            num_nodes: Number of nodes.
             cloud_compute: The cloud compute object used in the cloud.
             work_args: Arguments to be provided to the work on instantiation.
             work_kwargs: Keywords arguments to be provided to the work on instantiation.
         """
         super().__init__()
         self.ws = structures.List()
         self._work_cls = work_cls
-        self.nodes = nodes
+        self.num_nodes = num_nodes
         self._cloud_compute = cloud_compute
         self._work_args = work_args
         self._work_kwargs = work_kwargs
@@ -65,7 +65,7 @@ def run(self) -> None:
 
             # 1. Create & start the works
             if not self.ws:
-                for node_rank in range(self.nodes):
+                for node_rank in range(self.num_nodes):
                     self.ws.append(
                         self._work_cls(
                             *self._work_args,
@@ -84,12 +84,13 @@ def run(self) -> None:
             self.has_started = True
 
         # Loop over all node machines
-        for node_rank in range(self.nodes):
+        for node_rank in range(self.num_nodes):
 
             # 3. Run the user code in a distributed way !
             self.ws[node_rank].run(
                 main_address=self.ws[0].internal_ip,
                 main_port=self.ws[0].port,
+                num_nodes=self.num_nodes,
                 node_rank=node_rank,
             )
 

src/lightning_app/utilities/proxies.py

@@ -395,18 +395,18 @@ def run_once(self):
         # 6. Create the state observer thread.
         self.state_observer = WorkStateObserver(self.work, delta_queue=self.delta_queue)
 
+        # 7. Deepcopy the work state and send the first `RUNNING` status delta to the flow.
+        reference_state = deepcopy(self.work.state)
+
         # Set the internal IP address.
         # Set this here after the state observer is initialized, since it needs to record it as a change and send
         # it back to the flow
         self.work._internal_ip = os.environ.get("LIGHTNING_NODE_IP", "127.0.0.1")
 
-        # 7. Patch the setattr method of the work. This needs to be done after step 4, so we don't
+        # 8. Patch the setattr method of the work. This needs to be done after step 4, so we don't
         # send delta while calling `set_state`.
         self._proxy_setattr()
 
-        # 8. Deepcopy the work state and send the first `RUNNING` status delta to the flow.
-        reference_state = deepcopy(self.work.state)
-
         if self._is_starting(called, reference_state, call_hash):
             return