[App] Expose Run Work Executor

[tchaton]

What does this PR do?

This PR exposes RunWorkExecutor and provides some default one for MultiNode Component.

Example with PyTorch. Processes are automatically spawned for the users.

import torch
from torch.nn.parallel.distributed import DistributedDataParallel

import lightning as L
from lightning.app.components import PyTorchSpawnMultiNode


class PyTorchDistributed(L.LightningWork):

    # Note: Only staticmethod are support for now with `PyTorchSpawnMultiNode`
    @staticmethod
    def run(
        world_size: int,
        node_rank: int,
        global_rank: str,
        local_rank: int,
    ):
        # 1. 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)

        # 2. Prepare loss and optimizer
        criterion = torch.nn.MSELoss()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

        # 3. 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()


compute = L.CloudCompute("gpu-fast-multi")  # 4 x V100
app = L.LightningApp(
    PyTorchSpawnMultiNode(
        PyTorchDistributed,
        num_nodes=2,
        cloud_compute=compute,
    )
)

Example with LightningLite.

import torch

import lightning as L
from lightning.app.components import LiteMultiNode
from lightning.lite import LightningLite


class LitePyTorchDistributed(L.LightningWork):
    @staticmethod
    def run():
        # 1. Create LightningLite.
        lite = LightningLite(strategy="ddp", precision="bf16")

        # 2. 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()

        # 3. 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()


app = L.LightningApp(
    LiteMultiNode(
        LitePyTorchDistributed,
        cloud_compute=L.CloudCompute("gpu-fast-multi"),  # 4 x V100,
        num_nodes=2,
    )
)

import lightning as L
from lightning.app.components import PyTorchLightningMultiNode
from lightning.pytorch.demos.boring_classes import BoringModel


class PyTorchLightningDistributed(L.LightningWork):
    @staticmethod
    def run():
        model = BoringModel()
        trainer = L.Trainer(
            max_epochs=10,
            strategy="ddp",
        )
        trainer.fit(model)


compute = L.CloudCompute("gpu-fast-multi")  # 4 x V100
app = L.LightningApp(
    PyTorchLightningMultiNode(
        PyTorchLightningDistributed,
        num_nodes=2,
        cloud_compute=compute,
    )
)

Fixes #<issue_number>

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cc @Borda

[justusschock]

lgtm, a few minor comments