# vLLM中的算子融合实现
from vllm.model_executor.layers.linear import LinearMethod
from vllm.model_executor.layers.activation import ActivationFunction

class VLLMOperatorFusion:
    def __init__(self):
        self.fused_operators = {
            'fused_linear_relu': self._fused_linear_relu,
            'fused_linear_gelu': self._fused_linear_gelu,
            'fused_attention_softmax': self._fused_attention_softmax,
            'fused_layer_norm': self._fused_layer_norm
        }
    
    def _fused_linear_relu(self, x, weight, bias):
        """融合的线性层+ReLU算子"""
        # 使用vLLM的融合CUDA kernel
        return torch.ops.vllm.fused_linear_relu(x, weight, bias)
    
    def _fused_linear_gelu(self, x, weight, bias):
        """融合的线性层+GELU算子"""
        return torch.ops.vllm.fused_linear_gelu(x, weight, bias)
    
    def _fused_attention_softmax(self, q, k, v):
        """融合的注意力+Softmax算子"""
        return torch.ops.vllm.fused_attention_softmax(q, k, v)
    
    def _fused_layer_norm(self, x, weight, bias, eps=1e-6):
        """融合的LayerNorm算子"""
        return torch.ops.vllm.fused_layer_norm(x, weight, bias, eps)