import torch  
 import torch.nn as nn  
 import torch.nn.functional as F  
 import math  
   
 class TurboAttention(nn.Module):  
     def __init__(self, embed_dim, num_heads, sparse_ratio=0.1):  
         super(TurboAttention, self).__init__()  
         self.embed_dim = embed_dim  
         self.num_heads = num_heads  
         self.sparse_ratio = sparse_ratio  
         self.head_dim = embed_dim // num_heads  
   
         assert (  
             self.head_dim * num_heads == embed_dim  
        ), "嵌入維度必須能被注意力頭數(shù)整除"  
           
         # 定義線性投影層  
         self.q_proj = nn.Linear(embed_dim, embed_dim)  
         self.k_proj = nn.Linear(embed_dim, embed_dim)  
         self.v_proj = nn.Linear(embed_dim, embed_dim)  
           
         # 定義輸出投影層  
         self.out_proj = nn.Linear(embed_dim, embed_dim)  
           
         # 定義e^-x近似的多項式系數(shù) (SAS)  
         # P(x) = a3*x^3 + a2*x^2 + a1*x + a0  
         self.poly_a3 = -0.1025  
         self.poly_a2 = 0.4626  
         self.poly_a1 = -0.9922  
         self.poly_a0 = 0.9996  
   
     def forward(self, x):  
         batch_size, seq_length, embed_dim = x.size()  
           
         # 第1步：執(zhí)行線性投影并可選進行量化  
         Q_fp = self.q_proj(x)  
         K_fp = self.k_proj(x)  
         V_fp = self.v_proj(x)  
           
         # 注：此處省略漸進式量化實現(xiàn)代碼  
         # 實際應(yīng)用中需要將Q、K、V量化為低位格式  
         # 并在需要時進行部分反量化以支持矩陣乘法  
           
         # 重排張量以支持多頭注意力計算  
         Q = Q_fp.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)  
         K = K_fp.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)  
         V = V_fp.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)  
           
         # 第2步：計算縮放點積注意力  
         # 使用多項式近似替代標(biāo)準(zhǔn)指數(shù)函數(shù)  
         scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(self.head_dim)  
           
         # 將注意力分?jǐn)?shù)限制在[0, 1]范圍內(nèi)以適應(yīng)多項式計算  
         scores_clamped = torch.clamp(scores, 0, 1)  
           
         # 使用多項式近似計算e^-x  
         # softmax中根據(jù)分?jǐn)?shù)符號使用e^score或e^-score  
         # 此處展示e^-x的近似計算  
         exponent_approx = (  
             self.poly_a3 * scores_clamped ** 3 +  
             self.poly_a2 * scores_clamped ** 2 +  
             self.poly_a1 * scores_clamped +  
             self.poly_a0  
        )  
           
         # 第3步：實現(xiàn)top-k稀疏化  
         top_k = max(1, int(seq_length * self.sparse_ratio))  
         top_scores, _ = torch.topk(scores, top_k, dim=-1)  
         threshold = top_scores[:, :, :, -1].unsqueeze(-1)  
         mask = (scores >= threshold)  
           
         # 將多項式近似結(jié)果轉(zhuǎn)換為帶掩碼的注意力分布  
         exponent_approx = exponent_approx.masked_fill(~mask, float('-inf'))  
           
         # 第4步：執(zhí)行softmax歸一化  
         attn = F.softmax(exponent_approx, dim=-1)  
           
         # 第5步：應(yīng)用dropout進行正則化  
         attn = F.dropout(attn, p=0.1, training=self.training)  
           
         # 第6步：計算注意力加權(quán)和  
         context = torch.matmul(attn, V)  
           
         # 恢復(fù)原始張量形狀  
         context = context.transpose(1, 2).contiguous().view(batch_size, seq_length, embed_dim)  
         out = self.out_proj(context)  
           
         return out

class TransformerBlock(nn.Module):  
     def __init__(self, embed_dim, num_heads):  
         super(TransformerBlock, self).__init__()  
         self.attention = TurboAttention(embed_dim, num_heads)  
         self.layer_norm1 = nn.LayerNorm(embed_dim)  
         self.feed_forward = nn.Sequential(  
             nn.Linear(embed_dim, embed_dim * 4),  
             nn.ReLU(),  
             nn.Linear(embed_dim * 4, embed_dim)  
        )  
         self.layer_norm2 = nn.LayerNorm(embed_dim)  
   
     def forward(self, x):  
         # 注意力層計算  
         attn_out = self.attention(x)  
         x = self.layer_norm1(x + attn_out)  
   
         # 前饋網(wǎng)絡(luò)計算  
         ff_out = self.feed_forward(x)  
         x = self.layer_norm2(x + ff_out)  
   
         return x

# 環(huán)境變量配置  
 ENV PYTHONDONTWRITEBYTECODE=1  
 ENV PYTHONUNBUFFERED=1  
   
 # 工作目錄設(shè)置  
 WORKDIR /app  
   
 # 依賴項安裝  
 COPY requirements.txt .  
 RUN pip install --upgrade pip  
 RUN pip install -r requirements.txt  
   
 # 項目文件復(fù)制  
 COPY . .  
   
 # 服務(wù)啟動命令  
 CMD ["python", "deploy_model.py"]

torch==1.12.1  
    torchvisinotallow==0.13.1  
    flask==2.0.3  
    gunicorn==20.1.0

apiVersion: apps/v1  
     kind: Deployment  
     metadata:  
       name: turboattention-deployment  
     spec:  
       replicas: 3  
       selector:  
         matchLabels:  
           app: turboattention  
       template:  
         metadata:  
           labels:  
             app: turboattention  
         spec:  
           containers:  
           - name: turboattention-container  
             image: your-docker-repo/turboattention:latest  
             ports:  
             - containerPort: 8000  
             resources:  
               limits:  
                 memory: "2Gi"  
                 cpu: "1"  
               requests:  
                 memory: "1Gi"  
                 cpu: "0.5"  
     ---  
     apiVersion: v1  
     kind: Service  
     metadata:  
       name: turboattention-service  
     spec:  
       selector:  
         app: turboattention  
       ports:  
         - protocol: TCP  
           port: 80  
           targetPort: 8000  
       type: LoadBalancer

from airflow import DAG  
 from airflow.operators.bash import BashOperator  
 from datetime import datetime  
 
 default_args = {  
     'owner': 'airflow',  
     'start_date': datetime(2023, 1, 1),  
 }  
   
 with DAG('deploy_turboattention', default_args=default_args, schedule_interval='@daily') as dag:  
     build_docker = BashOperator(  
         task_id='build_docker_image',  
         bash_command='docker build -t your-docker-repo/turboattention:latest .'  
    )  
     push_docker = BashOperator(  
         task_id='push_docker_image',  
         bash_command='docker push your-docker-repo/turboattention:latest'  
    )  
     update_kubernetes = BashOperator(  
         task_id='update_kubernetes_deployment',  
         bash_command='kubectl apply -f k8s-deployment.yaml'  
    )  
   
     # 定義任務(wù)執(zhí)行順序  
     build_docker >> push_docker >> update_kubernetes# **性能評估方法**

import time  
  import torch  
 def benchmark_attention(attention_layer, x):  
     start_time = time.time()  
     for _ in range(100):  
         output = attention_layer(x)  
     end_time = time.time()  
     avg_time = (end_time - start_time) / 100  
     return avg_time  
   
 # 構(gòu)造測試數(shù)據(jù)  
 batch_size = 32  
 seq_length = 512  
 embed_dim = 1024  
 x = torch.randn(batch_size, seq_length, embed_dim).cuda()  
   
 # 標(biāo)準(zhǔn)注意力機制測試  
 standard_attention = nn.MultiheadAttention(embed_dim, num_heads=8).cuda()  
 standard_time = benchmark_attention(standard_attention, x)  
 print(f"標(biāo)準(zhǔn)注意力機制平均執(zhí)行時間：{standard_time:.6f}秒")  
   
 # TurboAttention測試  
 turbo_attention = TurboAttention(embed_dim, num_heads=8, sparse_ratio=0.1).cuda()  
 turbo_time = benchmark_attention(turbo_attention, x)  
 print(f"TurboAttention平均執(zhí)行時間：{turbo_time:.6f}秒")