vllm - 💡(How to fix) Fix [Usage]: how does cpu offload work? [1 participants]

Fix Plan

To address the issue of the GPU exploding when loading a 7B model on 16GB VRAM, we need to optimize the model loading process by effectively utilizing CPU offload.

Steps to Fix

• Set the cpu_offload_threshold to a lower value to start offloading to CPU earlier, reducing GPU memory usage.
• Adjust the cpu_offload_size to control the amount of data offloaded to CPU.
• Consider using a larger batch_size to reduce the number of iterations and alleviate GPU memory pressure.

Example Code

import torch

# Set CPU offload threshold and size
cpu_offload_threshold = 10  # in GB
cpu_offload_size = 5  # in GB

# Load the model with CPU offload
model = torch.load('model.pth', map_location=torch.device('cpu'))

# Move the model to GPU with CPU offload
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device, non_blocking=True)

# Set the batch size
batch_size = 32

# Train the model
for batch in dataset:
    # Move the batch to GPU
    batch = batch.to(device, non_blocking=True)
    # Forward pass
    output = model(batch)
    # Backward pass
    loss = criterion(output, target)
    loss.backward()
    # Update the model
    optimizer.step()

Verification

Monitor the GPU memory usage using tools like nvidia-smi or gpu_memory_info to ensure that the GPU memory usage is reduced after implementing the fix.

Extra Tips

• Use the torch.cuda.empty_cache() function to release any unused GPU memory.
• Consider using a more efficient model architecture or pruning the model to reduce its size.
• Use a larger cpu_offload_size to offload more data to CPU, but be aware that this may increase the latency.