LCLM 深度解析：16 倍上下文压缩如何颠覆 LLM 推理经济学

# LCLM 多尺度注意力架构简化实现
# 展示核心原理，非完整生产代码

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

class MultiScaleAttention(nn.Module):
    """LCLM 多尺度注意力：粗粒度扫描 + 细粒度聚焦"""

    def __init__(self, d_model: int, n_heads: int,
                 compression_ratio: int = 16):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.compression_ratio = compression_ratio

        # 粗粒度投影：用于全局扫描
        self.coarse_proj = nn.Linear(d_model, d_model // compression_ratio)

        # 细粒度投影：用于局部聚焦
        self.fine_proj = nn.Linear(d_model, d_model)

        # 注意力门控：决定每个位置使用粗/细粒度
        self.gate = nn.Sequential(
            nn.Linear(d_model * 2, 1),
            nn.Sigmoid()
        )

    def forward(self, query: torch.Tensor,
                latent_context: torch.Tensor,
                original_context: torch.Tensor = None):
        """
        query: [batch, seq_len, d_model] - 当前生成的 token
        latent_context: [batch, compressed_len, d_model] - 压缩后的上下文
        original_context: [batch, full_len, d_model] - 原始上下文（可选）
        """
        # 第一步：粗粒度扫描全部潜在上下文
        coarse_keys = self.coarse_proj(latent_context)
        coarse_attn = F.scaled_dot_product_attention(
            query, coarse_keys, latent_context,
            dropout_p=0.0
        )

        # 第二步：基于粗粒度结果，选择需要细读的区域
        # 计算每个 latent vector 的相关性分数
        relevance_scores = torch.einsum('bqd,bkd->bqk',
                                        query, latent_context)
        top_k_indices = relevance_scores.topk(
            k=relevance_scores.shape[-1] // 4, dim=-1
        ).indices

        # 第三步：对高相关区域进行细粒度处理
        if original_context is not None:
            # 解压 top-k 区域，恢复原始 token 级信息
            fine_regions = self._extract_fine_regions(
                original_context, top_k_indices
            )
            fine_keys = self.fine_proj(fine_regions)
            fine_attn = F.scaled_dot_product_attention(
                query, fine_keys, fine_regions,
                dropout_p=0.0
            )

            # 门控融合：动态决定每个位置用粗/细粒度
            gate_input = torch.cat([coarse_attn, fine_attn], dim=-1)
            gate_weight = self.gate(gate_input)
            output = gate_weight * coarse_attn + (1 - gate_weight) * fine_attn
        else:
            output = coarse_attn

        return output

    def _extract_fine_regions(self, context, indices):
        """从原始上下文中提取高相关区域"""
        batch_size = context.shape[0]
        regions = []
        for b in range(batch_size):
            region = context[b][indices[b].flatten()]
            regions.append(region)
        return torch.stack(regions)


class LatentContextCompressor(nn.Module):
    """LCLM 上下文压缩器"""

    def __init__(self, d_model: int, compression_ratio: int = 16):
        super().__init__()
        self.compression_ratio = compression_ratio

        # 压缩编码器：轻量级 Transformer
        self.encoder = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=d_model,
                nhead=8,
                dim_feedforward=d_model * 2,
                batch_first=True
            ),
            num_layers=2  # 只需 2 层，参数量约为基线模型的 5%
        )

        # 池化层：将每 compression_ratio 个 token 压缩为 1 个 latent
        self.pool = nn.AvgPool1d(compression_ratio)

    def compress(self, token_embeddings: torch.Tensor) -> torch.Tensor:
        """
        token_embeddings: [batch, seq_len, d_model]
        returns: [batch, seq_len // ratio, d_model]
        """
        # 通过编码器
        encoded = self.encoder(token_embeddings)

        # 池化压缩
        # [batch, seq_len, d_model] -> [batch, d_model, seq_len]
        transposed = encoded.transpose(1, 2)
        pooled = self.pool(transposed)
        # [batch, d_model, compressed_len] -> [batch, compressed_len, d_model]
        compressed = pooled.transpose(1, 2)

        return compressed

# 零检索 RAG：用 LCLM 分析法律文档
# pip install transformers latent-context

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from latent_context import LatentContextCompressor
import time

class ZeroRetrievalRAG:
    """基于 LCLM 的零检索 RAG 系统"""

    def __init__(self, model_name: str = "meta-llama/Llama-3.1-8B"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.compressor = LatentContextCompressor(
            compression_ratio=16,
            model_path="latent-context/lclm-llama3-8b"
        )
        self._compressed_cache = {}

    def ingest_document(self, doc_id: str, text: str) -> dict:
        """
        摄入文档：压缩并缓存
        传统 RAG 需要：分块 + Embedding + 存入向量库
        LCLM 只需要：全文压缩 + 缓存
        """
        start = time.time()

        # Tokenize 全文
        tokens = self.tokenizer(
            text, return_tensors="pt",
            truncation=True, max_length=1_000_000
        ).to(self.model.device)

        token_count = tokens['input_ids'].shape[1]

        # 压缩为潜在表示
        with torch.no_grad():
            # 先获取 token embeddings
            with self.model._get_embedding_context():
                embeddings = self.model.get_input_embeddings()(
                    tokens['input_ids']
                )

            compressed = self.compressor.compress(embeddings)

        # 缓存压缩结果
        self._compressed_cache[doc_id] = {
            'compressed': compressed,
            'token_count': token_count,
            'compressed_len': compressed.shape[1]
        }

        elapsed = time.time() - start
        return {
            'doc_id': doc_id,
            'tokens': token_count,
            'compressed_vectors': compressed.shape[1],
            'compression_ratio': token_count / compressed.shape[1],
            'ingest_time_seconds': round(elapsed, 2)
        }

    def query(self, doc_id: str, question: str) -> str:
        """
        查询：直接在压缩空间推理，无需检索
        """
        if doc_id not in self._compressed_cache:
            raise ValueError(f"Document {doc_id} not ingested")

        cached = self._compressed_cache[doc_id]
        compressed = cached['compressed']

        # 构建查询
        prompt = f"""你是一个法律文档分析专家。基于提供的合同文档，回答以下问题。
请给出详细分析，包括具体条款引用和风险等级评估。

问题：{question}

分析："""

        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)

        start = time.time()
        with torch.no_grad():
            output = self.model.generate(
                **inputs,
                latent_context=compressed,  # 关键：传入压缩上下文
                max_new_tokens=1024,
                temperature=0.1,  # 法律场景用低温度
                use_cache=True
            )
        elapsed = time.time() - start

        answer = self.tokenizer.decode(output[0][inputs['input_ids'].shape[1]:],
                                       skip_special_tokens=True)
        return {
            'answer': answer,
            'query_time_seconds': round(elapsed, 2),
            'tokens_generated': output.shape[1] - inputs['input_ids'].shape[1]
        }


# === 使用示例 ===
rag = ZeroRetrievalRAG()

# 摄入 120 页合同（约 80K tokens）
result = rag.ingest_document(
    "contract-2026-q2",
    open("legal/contract-q2.txt").read()
)
print(f"摄入结果: {result}")
# {'doc_id': 'contract-2026-q2', 'tokens': 80000,
#  'compressed_vectors': 5000, 'compression_ratio': 16.0,
#  'ingest_time_seconds': 3.2}

# 查询
answer = rag.query(
    "contract-2026-q2",
    "找出所有与知识产权相关的条款，分析风险等级"
)
print(f"查询耗时: {answer['query_time_seconds']}s")
print(f"回答: {answer['answer'][:500]}...")