47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
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import torch
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from torch.nn.parallel.distributed import DistributedDataParallel
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import lightning as L
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from lightning.app.components import PyTorchSpawnMultiNode
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class PyTorchDistributed(L.LightningWork):
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# Note: Only staticmethod are support for now with `PyTorchSpawnMultiNode`
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@staticmethod
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def run(
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world_size: int,
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node_rank: int,
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global_rank: str,
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local_rank: int,
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):
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# 1. Prepare distributed model
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model = torch.nn.Linear(32, 2)
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device = torch.device(f"cuda:{local_rank}") if torch.cuda.is_available() else torch.device("cpu")
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device_ids = device if torch.cuda.is_available() else None
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model = DistributedDataParallel(model, device_ids=device_ids).to(device)
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# 2. Prepare loss and optimizer
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criterion = torch.nn.MSELoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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# 3. Train the model for 50 steps.
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for step in range(50):
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model.zero_grad()
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x = torch.randn(64, 32).to(device)
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output = model(x)
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loss = criterion(output, torch.ones_like(output))
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print(f"global_rank: {global_rank} step: {step} loss: {loss}")
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loss.backward()
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optimizer.step()
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# Run over 2 nodes of 4 x V100
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app = L.LightningApp(
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PyTorchSpawnMultiNode(
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PyTorchDistributed,
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num_nodes=2,
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cloud_compute=L.CloudCompute("gpu-fast-multi"), # 4 x V100
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)
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)
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