6：LongCat-Flash-Lite 无思考MoE模型：68.5B总参数的高效大语言模型解决方案

# MoE架构核心代码示例
class MoELayer(nn.Module):
    def __init__(self, input_dim, output_dim, num_experts, top_k):
        super().__init__()
        self.num_experts = num_experts
        self.top_k = top_k
        # 专家网络
        self.experts = nn.ModuleList([
            nn.Linear(input_dim, output_dim) for _ in range(num_experts)
        ])
        # 门控网络
        self.gate = nn.Linear(input_dim, num_experts)
    
    def forward(self, x):
        """MoE层前向传播"""
        batch_size, seq_len, dim = x.shape
        # 计算专家权重
        gate_logits = self.gate(x)
        # 选择top-k专家
        weights, indices = torch.topk(gate_logits, self.top_k, dim=-1)
        weights = F.softmax(weights, dim=-1)
        
        # 准备专家输入
        flat_x = x.view(-1, dim)
        flat_indices = indices.view(-1, self.top_k)
        flat_weights = weights.view(-1, self.top_k)
        
        # 专家推理
        expert_outputs = []
        for i in range(self.num_experts):
            # 收集需要该专家处理的输入
            mask = (flat_indices == i).any(dim=-1)
            if mask.sum() > 0:
                expert_input = flat_x[mask]
                expert_output = self.experts[i](expert_input)
                expert_outputs.append((i, mask, expert_output))
        
        # 组合专家输出
        output = torch.zeros_like(flat_x)
        for expert_idx, mask, expert_output in expert_outputs:
            # 找到该专家对应的权重
            weight_mask = (flat_indices == expert_idx)
            expert_weights = flat_weights[weight_mask].unsqueeze(-1)
            # 加权组合
            output[mask] += (expert_output * expert_weights).sum(dim=1)
        
        return output.view(batch_size, seq_len, dim)

# N-gram嵌入表核心代码示例
class NGramEmbedding(nn.Module):
    def __init__(self, vocab_size, embed_dim, max_ngram=3):
        super().__init__()
        self.max_ngram = max_ngram
        # 不同粒度的嵌入表
        self.embeddings = nn.ModuleList([
            nn.Embedding(vocab_size, embed_dim)
            for _ in range(max_ngram)
        ])
        # 融合权重
        self.fusion_weights = nn.Parameter(torch.ones(max_ngram))
    
    def forward(self, tokens, ngram_mask=None):
        """N-gram嵌入前向传播"""
        batch_size, seq_len = tokens.shape
        embeddings = []
        
        # 计算不同粒度的嵌入
        for i in range(self.max_ngram):
            if i == 0:
                # 1-gram嵌入
                emb = self.embeddings[i](tokens)
            else:
                # n-gram嵌入
                ngram_tokens = self.extract_ngrams(tokens, i+1)
                emb = self.embeddings[i](ngram_tokens)
            embeddings.append(emb)
        
        # 融合不同粒度的嵌入
        weights = F.softmax(self.fusion_weights, dim=0)
        fused_emb = sum(w * emb for w, emb in zip(weights, embeddings))
        
        return fused_emb
    
    def extract_ngrams(self, tokens, n):
        """提取n-gram tokens"""
        # 实现n-gram提取逻辑
        # ...
        return ngram_tokens

# YaRN长上下文核心代码示例
class YaRNPositionEncoding(nn.Module):
    def __init__(self, embed_dim, max_seq_len=256000, base=10000):
        super().__init__()
        self.embed_dim = embed_dim
        self.max_seq_len = max_seq_len
        self.base = base
        # YaRN参数
        self.alpha = nn.Parameter(torch.tensor(1.0))
        self.beta = nn.Parameter(torch.tensor(1.0))
    
    def forward(self, seq_len, device):
        """生成YaRN位置编码"""
        # 生成传统RoPE位置编码
        position = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, self.embed_dim, 2, device=device) * 
                            (-math.log(self.base) / self.embed_dim))
        pe = torch.zeros(seq_len, self.embed_dim, device=device)
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        
        # 应用YaRN扩展
        if seq_len > self.max_seq_len:
            # 对超出原始窗口的位置应用YaRN变换
            extended_positions = position[position >= self.max_seq_len]
            scaled_positions = self.alpha * extended_positions + self.beta
            scaled_div_term = div_term * self.alpha
            pe[position >= self.max_seq_len, 0::2] = torch.sin(scaled_positions * scaled_div_term)
            pe[position >= self.max_seq_len, 1::2] = torch.cos(scaled_positions * scaled_div_term)
        
        return pe

# 推理优化核心代码示例
class OptimizedInferenceEngine:
    def __init__(self, model, quantization='fp16'):
        self.model = model
        self.quantization = quantization
        self.optimize_model()
        self.kv_cache = {}
    
    def optimize_model(self):
        """优化模型以提高推理速度"""
        # 应用量化
        if self.quantization == 'int8':
            self.model = self.quantize_to_int8(self.model)
        elif self.quantization == 'fp16':
            self.model = self.model.half()
        
        # 移动到合适的设备
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()
    
    def infer(self, input_ids, attention_mask=None, use_cache=True):
        """高效推理"""
        with torch.no_grad():
            # 准备输入
            input_ids = input_ids.to(self.device)
            if attention_mask is not None:
                attention_mask = attention_mask.to(self.device)
            
            # 使用KV缓存
            if use_cache and 'past_key_values' in self.kv_cache:
                past_key_values = self.kv_cache['past_key_values']
            else:
                past_key_values = None
            
            # 推理
            output = self.model(
                input_ids=input_ids,
                attention_mask=attention_mask,
                past_key_values=past_key_values,
                use_cache=use_cache
            )
            
            # 更新KV缓存
            if use_cache:
                self.kv_cache['past_key_values'] = output.past_key_values
            
            return output
    
    def quantize_to_int8(self, model):
        """将模型量化为INT8"""
        # 实现INT8量化
        # ...
        return quantized_model

import gradio as gr
import torch
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

# 加载模型
longcat_pipeline = pipeline(
    Tasks.text_generation,
    model='meituan-longcat/LongCat-Flash-Lite'
)

# 处理函数
def generate_text(prompt, max_length=512, temperature=0.7, top_p=0.95):
    """文本生成"""
    # 执行文本生成
    result = longcat_pipeline({
        'prompt': prompt,
        'max_length': max_length,
        'temperature': temperature,
        'top_p': top_p,
        'use_cache': True
    })
    
    # 格式化输出
    output = f"生成结果:\n{result['text']}"
    
    # 生成统计信息
    stats = f"生成长度: {len(result['text'].split())} tokens\n"
    stats += f"处理时间: {result.get('processing_time', 'N/A')}秒\n"
    stats += f"温度参数: {temperature}\n"
    stats += f"Top-p参数: {top_p}\n"
    
    return output, stats

# 创建Gradio界面
with gr.Blocks(title="LongCat-Flash-Lite 文本生成") as demo:
    gr.Markdown("# LongCat-Flash-Lite 文本生成演示")
    gr.Markdown("输入提示词，生成文本内容")
    
    with gr.Row():
        with gr.Column(scale=1):
            prompt_input = gr.Textbox(
                label="提示词", 
                value="写一篇关于人工智能未来发展的文章",
                placeholder="输入提示词"
            )
            max_length = gr.Slider(
                min=100, max=2048, value=512, step=100,
                label="最大生成长度"
            )
            temperature = gr.Slider(
                min=0.1, max=1.0, value=0.7, step=0.1,
                label="温度参数"
            )
            top_p = gr.Slider(
                min=0.5, max=1.0, value=0.95, step=0.05,
                label="Top-p参数"
            )
            generate_btn = gr.Button("生成")
        
        with gr.Column(scale=2):
            output_text = gr.Textbox(label="生成结果", lines=20)
            stats_output = gr.Textbox(label="生成统计", lines=5)
    
    # 绑定事件
    generate_btn.click(
        fn=generate_text,
        inputs=[prompt_input, max_length, temperature, top_p],
        outputs=[output_text, stats_output]
    )

if __name__ == "__main__":
    demo.launch(share=True)

pytorch==2.0.1
modelscope==1.9.1
gradio==4.14.0
numpy==1.24.4
transformers==4.35.0
accelerate==0.24.0

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime

WORKDIR /app

COPY . /app

RUN pip install -r requirements.txt

EXPOSE 7860

CMD ["python", "app.py"]