vllm - 💡(How to fix) Fix [Feature]: add ParoQuant quantization [3 comments, 3 participants]

Fix Plan

To implement ParoQuant, follow these steps:

• Clone the ParoQuant repository: git clone https://github.com/z-lab/paroquant.git
• Install required dependencies: pip install -r requirements.txt
• Implement Pairwise Rotation Quantization in your model:

import torch
import torch.nn as nn
from paroquant import ParoQuant

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc = nn.Linear(128, 128)
        self.paroquant = ParoQuant()

    def forward(self, x):
        x = self.paroquant(x)
        x = self.fc(x)
        return x

• Integrate ParoQuant into your existing pipeline:

model = MyModel()
input_tensor = torch.randn(1, 128)
output = model(input_tensor)

• Fine-tune hyperparameters for optimal performance.

Verification

Verify the implementation by checking the model's accuracy and inference speed:

import time

start_time = time.time()
output = model(input_tensor)
end_time = time.time()
print(f"Inference time: {end_time - start_time} seconds")
print(f"Accuracy: {calculate_accuracy(output)}")

Extra Tips

• Refer to the ParoQuant documentation and paper for more information on hyperparameter tuning and optimization.
• Experiment with different quantization methods to find the best approach for your specific use case.