[DeepSeek]代码智能的新纪元：深度解析DeepSeek Coder的能力边界

import torch
import torch.nn as nn
import torch.nn.functional as F

class CodeAwareAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.d_model = d_model
        self.num_heads = num_heads
        self.head_dim = d_model // num_heads
        
        self.q_proj = nn.Linear(d_model, d_model)
        self.k_proj = nn.Linear(d_model, d_model)
        self.v_proj = nn.Linear(d_model, d_model)
        self.out_proj = nn.Linear(d_model, d_model)
        
    def forward(self, x, code_structure_mask=None):
        batch_size, seq_len, _ = x.size()
        
        # 投影查询、键和值
        q = self.q_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        k = self.k_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        v = self.v_proj(x).view(batch_size, seq_len, self.num_heads, self.head_dim)
        
        # 转置用于批量矩阵乘法
        q = q.transpose(1, 2)  # (batch_size, num_heads, seq_len, head_dim)
        k = k.transpose(1, 2)
        v = v.transpose(1, 2)
        
        # 计算注意力分数
        scores = torch.matmul(q, k.transpose(2, 3)) / (self.head_dim ** 0.5)
        
        # 应用代码结构感知掩码
        if code_structure_mask is not None:
            scores = scores + code_structure_mask
        
        # 应用softmax并获取注意力权重
        attn_weights = F.softmax(scores, dim=-1)
        
        # 计算加权和
        context = torch.matmul(attn_weights, v)
        context = context.transpose(1, 2).reshape(batch_size, seq_len, self.d_model)
        
        # 最终投影
        output = self.out_proj(context)
        return output

def extract_ast_features(code_snippet, language):
    """
    提取代码的抽象语法树特征
    
    Args:
        code_snippet: 代码片段
        language: 编程语言
        
    Returns:
        AST特征的张量表示
    """
    parser = get_language_parser(language)
    ast = parser.parse(code_snippet)
    
    # 递归遍历AST并提取特征
    features = []
    for node in ast.traverse():
        node_type = node.type
        node_depth = node.depth
        parent_type = node.parent.type if node.parent else "None"
        
        # 将节点信息编码为特征向量
        node_features = encode_node_features(node_type, node_depth, parent_type)
        features.append(node_features)
    
    return torch.tensor(features)

# 多阶段训练的简化实现
def train_deepseek_coder():
    # 初始化模型
    model = DeepSeekCoderModel()
    
    # 阶段1: 代码预训练
    for epoch in range(pre_training_epochs):
        for batch in code_corpus_loader:
            # 掩码语言模型训练
            masked_input, targets = create_masked_samples(batch)
            loss = model.train_step(masked_input, targets)
            optimizer.step(loss)
    
    # 阶段2: 指令微调
    for epoch in range(instruction_tuning_epochs):
        for batch in instruction_data_loader:
            # 指令-代码对训练
            instructions, code_solutions = batch
            loss = model.train_instruction_following(instructions, code_solutions)
            optimizer.step(loss)
    
    # 阶段3: RLHF训练
    reward_model = train_reward_model(human_preference_data)
    for epoch in range(rlhf_epochs):
        for batch in rlhf_data_loader:
            # PPO训练循环
            instructions = batch
            code_samples = model.generate_samples(instructions)
            rewards = reward_model(instructions, code_samples)
            ppo_loss = compute_ppo_loss(rewards, code_samples)
            optimizer.step(ppo_loss)
    
    return model

# 用户请求: "编写一个函数计算两个日期之间的工作日数量，不包括周末和节假日"

def count_workdays(start_date, end_date, holidays=None):
    """
    计算两个日期之间的工作日数量（不包括周末和节假日）
    
    Args:
        start_date (datetime): 开始日期
        end_date (datetime): 结束日期
        holidays (list, optional): 节假日列表，包含datetime对象
    
    Returns:
        int: 工作日数量
    """
    if holidays is None:
        holidays = []
    
    # 确保开始日期小于等于结束日期
    if start_date > end_date:
        start_date, end_date = end_date, start_date
    
    # 初始化工作日计数器
    workdays = 0
    current_date = start_date
    
    # 遍历每一天
    while current_date <= end_date:
        # 检查当前日期是否为工作日（不是周末且不是节假日）
        if (current_date.weekday() < 5) and (current_date not in holidays):
            workdays += 1
        
        # 移至下一天
        current_date += datetime.timedelta(days=1)
    
    return workdays

# 用户请求: "将以下JavaScript代码转换为Python"

# 原始JavaScript代码
"""
function deepClone(obj) {
    if (obj === null || typeof obj !== 'object') {
        return obj;
    }
    
    let clone = Array.isArray(obj) ? [] : {};
    
    for (let key in obj) {
        if (Object.prototype.hasOwnProperty.call(obj, key)) {
            clone[key] = deepClone(obj[key]);
        }
    }
    
    return clone;
}
"""

# DeepSeek Coder生成的Python等价代码
def deep_clone(obj):
    """
    深度克隆一个对象
    
    Args:
        obj: 要克隆的对象
        
    Returns:
        克隆后的对象
    """
    if obj is None or not isinstance(obj, (dict, list)):
        return obj
    
    clone = [] if isinstance(obj, list) else {}
    
    if isinstance(obj, list):
        for item in obj:
            clone.append(deep_clone(item))
    else:  # dict
        for key, value in obj.items():
            clone[key] = deep_clone(value)
    
    return clone

# 用户请求: "优化以下求斐波那契数列的函数"

# 原始低效实现
def fibonacci_original(n):
    if n <= 1:
        return n
    return fibonacci_original(n-1) + fibonacci_original(n-2)

# DeepSeek Coder提供的优化版本
def fibonacci_optimized(n):
    """
    计算斐波那契数列的第n个数
    优化版本：使用动态规划避免重复计算
    
    Args:
        n: 要计算的位置
        
    Returns:
        第n个斐波那契数
    """
    if n <= 1:
        return n
    
    fib = [0] * (n + 1)
    fib[1] = 1
    
    for i in range(2, n + 1):
        fib[i] = fib[i-1] + fib[i-2]
    
    return fib[n]

# 进一步优化：空间复杂度O(1)的版本
def fibonacci_optimized_space(n):
    """
    计算斐波那契数列的第n个数
    空间优化版本：仅使用常数额外空间
    
    Args:
        n: 要计算的位置
        
    Returns:
        第n个斐波那契数
    """
    if n <= 1:
        return n
    
    a, b = 0, 1
    for _ in range(2, n + 1):
        a, b = b, a + b
    
    return b

# 未来多模态代码理解示例
def analyze_project(code_files, documentation, execution_traces):
    """
    多模态项目分析
    
    Args:
        code_files: 项目代码文件
        documentation: 项目文档
        execution_traces: 代码执行跟踪
        
    Returns:
        项目分析报告
    """
    # 代码结构分析
    code_structure = extract_code_structure(code_files)
    
    # 文档理解
    doc_insights = extract_documentation_insights(documentation)
    
    # 执行行为分析
    runtime_behavior = analyze_execution_traces(execution_traces)
    
    # 多模态融合分析
    integrated_analysis = multimodal_fusion(
        code_structure, doc_insights, runtime_behavior
    )
    
    return generate_analysis_report(integrated_analysis)

# DeepSeek Coder辅助API学习示例
def tensorflow_to_pytorch_conversion(tensorflow_code):
    """
    将TensorFlow代码转换为PyTorch等价实现
    
    Args:
        tensorflow_code: TensorFlow实现的代码
        
    Returns:
        PyTorch等价实现
    """
    prompt = f"""
    请将以下TensorFlow代码转换为PyTorch等价实现，
    并解释关键区别与转换思路：
    
    ```tensorflow
    {tensorflow_code}
    ```
    """
    
    response = deepseek_coder.generate(prompt)
    
    converted_code = extract_code_blocks(response)
    explanation = extract_explanation(response)
    
    return {
        "pytorch_code": converted_code,
        "conversion_explanation": explanation
    }