vllm - 💡(How to fix) Fix [Feature]: PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference [1 participants]

Fix Plan

The fix involves implementing a solution based on the reference paper provided.

Steps to Implement the Fix

• Review the reference paper https://arxiv.org/pdf/2509.04377 to understand the proposed approach.
• Implement the proposed model or algorithm in your codebase.
• Example code snippet in Python:

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        # Initialize model layers based on the reference paper

    def forward(self, x):
        # Implement forward pass based on the reference paper
        return x

• Integrate the implemented model with your existing codebase.
• Test and verify the implementation.

Verification

• Run tests to verify the correctness of the implementation.
• Compare results with expected outputs to ensure accuracy.

Extra Tips

• Ensure to follow best practices for coding and documentation.
• Consider adding comments to explain the implementation details.
• Review and test the implementation thoroughly to avoid regressions.