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DDP-script.py
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DDP-script.py
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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
# Appendix A: Introduction to PyTorch (Part 3)
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
# NEW imports:
import os
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed import init_process_group, destroy_process_group
# NEW: function to initialize a distributed process group (1 process / GPU)
# this allows communication among processes
def ddp_setup(rank, world_size):
"""
Arguments:
rank: a unique process ID
world_size: total number of processes in the group
"""
# rank of machine running rank:0 process
# here, we assume all GPUs are on the same machine
os.environ["MASTER_ADDR"] = "localhost"
# any free port on the machine
os.environ["MASTER_PORT"] = "12345"
# initialize process group
# Windows users may have to use "gloo" instead of "nccl" as backend
# nccl: NVIDIA Collective Communication Library
init_process_group(backend="nccl", rank=rank, world_size=world_size)
torch.cuda.set_device(rank)
class ToyDataset(Dataset):
def __init__(self, X, y):
self.features = X
self.labels = y
def __getitem__(self, index):
one_x = self.features[index]
one_y = self.labels[index]
return one_x, one_y
def __len__(self):
return self.labels.shape[0]
class NeuralNetwork(torch.nn.Module):
def __init__(self, num_inputs, num_outputs):
super().__init__()
self.layers = torch.nn.Sequential(
# 1st hidden layer
torch.nn.Linear(num_inputs, 30),
torch.nn.ReLU(),
# 2nd hidden layer
torch.nn.Linear(30, 20),
torch.nn.ReLU(),
# output layer
torch.nn.Linear(20, num_outputs),
)
def forward(self, x):
logits = self.layers(x)
return logits
def prepare_dataset():
X_train = torch.tensor([
[-1.2, 3.1],
[-0.9, 2.9],
[-0.5, 2.6],
[2.3, -1.1],
[2.7, -1.5]
])
y_train = torch.tensor([0, 0, 0, 1, 1])
X_test = torch.tensor([
[-0.8, 2.8],
[2.6, -1.6],
])
y_test = torch.tensor([0, 1])
train_ds = ToyDataset(X_train, y_train)
test_ds = ToyDataset(X_test, y_test)
train_loader = DataLoader(
dataset=train_ds,
batch_size=2,
shuffle=False, # NEW: False because of DistributedSampler below
pin_memory=True,
drop_last=True,
# NEW: chunk batches across GPUs without overlapping samples:
sampler=DistributedSampler(train_ds) # NEW
)
test_loader = DataLoader(
dataset=test_ds,
batch_size=2,
shuffle=False,
)
return train_loader, test_loader
# NEW: wrapper
def main(rank, world_size, num_epochs):
ddp_setup(rank, world_size) # NEW: initialize process groups
train_loader, test_loader = prepare_dataset()
model = NeuralNetwork(num_inputs=2, num_outputs=2)
model.to(rank)
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
model = DDP(model, device_ids=[rank]) # NEW: wrap model with DDP
# the core model is now accessible as model.module
for epoch in range(num_epochs):
model.train()
for features, labels in train_loader:
features, labels = features.to(rank), labels.to(rank) # New: use rank
logits = model(features)
loss = F.cross_entropy(logits, labels) # Loss function
optimizer.zero_grad()
loss.backward()
optimizer.step()
# LOGGING
print(f"[GPU{rank}] Epoch: {epoch+1:03d}/{num_epochs:03d}"
f" | Batchsize {labels.shape[0]:03d}"
f" | Train/Val Loss: {loss:.2f}")
model.eval()
train_acc = compute_accuracy(model, train_loader, device=rank)
print(f"[GPU{rank}] Training accuracy", train_acc)
test_acc = compute_accuracy(model, test_loader, device=rank)
print(f"[GPU{rank}] Test accuracy", test_acc)
destroy_process_group() # NEW: cleanly exit distributed mode
def compute_accuracy(model, dataloader, device):
model = model.eval()
correct = 0.0
total_examples = 0
for idx, (features, labels) in enumerate(dataloader):
features, labels = features.to(device), labels.to(device)
with torch.no_grad():
logits = model(features)
predictions = torch.argmax(logits, dim=1)
compare = labels == predictions
correct += torch.sum(compare)
total_examples += len(compare)
return (correct / total_examples).item()
if __name__ == "__main__":
print("PyTorch version:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
print("Number of GPUs available:", torch.cuda.device_count())
torch.manual_seed(123)
# NEW: spawn new processes
# note that spawn will automatically pass the rank
num_epochs = 3
world_size = torch.cuda.device_count()
mp.spawn(main, args=(world_size, num_epochs), nprocs=world_size)
# nprocs=world_size spawns one process per GPU