# vLLM中的CUDA Kernel优化
from vllm._C import ops
import torch

class OptimizedCUDAKernels:
    def __init__(self):
        self.attention_ops = ops.attention_ops
        self.activation_ops = ops.activation_ops
        self.linear_ops = ops.linear_ops
    
    def optimized_attention(self, q, k, v, attn_metadata):
        """优化的注意力计算"""
        # 使用vLLM优化的注意力kernel
        return self.attention_ops.flash_attn_v2(
            q, k, v,
            attn_metadata.block_tables,
            attn_metadata.seq_lens,
            attn_metadata.max_seq_len
        )
    
    def optimized_linear(self, input_tensor, weight, bias=None):
        """优化的线性层计算"""
        # 使用优化的矩阵乘法kernel
        return self.linear_ops.linear(
            input_tensor, weight, bias
        )
    
    def optimized_activation(self, x, activation_type="gelu"):
        """优化的激活函数"""
        # 使用融合的激活函数kernel
        return self.activation_ops.fused_activation(
            x, activation_type
        )