【大语言模型 17】高效Transformer架构革命：Reformer、Linformer、Performer性能突破解析

【大语言模型 17】高效Transformer架构革命：Reformer、Linformer、Performer性能突破解析

关键词：Transformer变种、Reformer、Linformer、Performer、注意力机制优化、长序列处理、计算复杂度、LSH注意力、线性注意力、Kernel方法、内存优化、序列建模

摘要：本文深度解析三种突破性的Transformer变种架构：Reformer、Linformer和Performer。通过费曼学习法，从计算复杂度问题出发，详细解释每种架构的核心创新点、实现原理和适用场景。重点讲解LSH注意力、线性复杂度近似和Kernel方法在注意力机制中的应用，帮助读者理解如何突破传统Transformer的计算瓶颈，实现高效的长序列处理。

引言：传统Transformer的困境与变革需求

想象一下，你正在阅读一本10万字的小说，需要理解每个词与其他所有词之间的关系。传统的Transformer模型就像一个需要同时记住所有词汇关系的超级大脑，但随着文本长度的增加，这个"大脑"的负担呈平方级增长。

这就是传统Transformer面临的核心问题：二次复杂度困境。当序列长度翻倍时，计算量和内存需求会增加4倍！这使得处理长文档、长对话或长序列数据变得极其困难。

今天，我们将探索三种革命性的解决方案：Reformer、Linformer和Performer。它们就像三位不同的建筑师，各自用独特的方法重新设计了注意力机制这座"大厦"。

第一部分：传统Transformer的复杂度瓶颈

为什么传统Transformer会遇到瓶颈？

让我们先理解问题的根源。在传统的Self-Attention机制中：

# 传统Self-Attention的计算过程
def vanilla_attention(Q, K, V, seq_len, d_model):"""传统注意力机制Q, K, V: [batch_size, seq_len, d_model]"""# 1. 计算注意力分数矩阵attention_scores = torch.matmul(Q, K.transpose(-2, -1))  # [batch, seq_len, seq_len]# 2. 缩放attention_scores = attention_scores / math.sqrt(d_model)# 3. Softmax归一化attention_weights = F.softmax(attention_scores, dim=-1)# 4. 计算输出output = torch.matmul(attention_weights, V)  # [batch, seq_len, d_model]return output# 复杂度分析
# 时间复杂度: O(seq_len²)
# 空间复杂度: O(seq_len²) - 需要存储注意力矩阵

具体的复杂度问题

让我们用具体数字来理解这个问题：

def analyze_complexity(seq_len, d_model=512):"""分析不同序列长度下的计算复杂度"""# 注意力矩阵大小attention_matrix_size = seq_len * seq_len# 内存需求（假设float32，4字节）memory_gb = (attention_matrix_size * 4) / (1024**3)# 计算量（FLOPs）flops = seq_len * seq_len * d_modelprint(f"序列长度: {seq_len}")print(f"注意力矩阵大小: {attention_matrix_size:,}")print(f"内存需求: {memory_gb:.2f} GB")print(f"计算量: {flops:,} FLOPs")print("-" * 40)# 不同序列长度的复杂度对比
for length in [512, 1024, 2048, 4096, 8192]:analyze_complexity(length)

输出结果会显示，当序列长度从512增加到8192时，内存需求从1MB增长到256MB，计算量增长了256倍！

第二部分：Reformer - 局部敏感哈希的智慧

Reformer的核心思想

Reformer就像一个聪明的图书管理员，不需要检查图书馆中的每一本书，而是使用一套巧妙的索引系统来快速找到相关的书籍。

Reformer的两大核心创新：

1. LSH注意力（Locality-Sensitive Hashing Attention）：将相似的查询和键分组
2. 可逆层（Reversible Layers）：减少内存消耗

LSH注意力机制详解

import torch
import torch.nn as nn
import numpy as npclass LSHAttention(nn.Module):def __init__(self, d_model, n_hashes=8, bucket_size=64):super().__init__()self.d_model = d_modelself.n_hashes = n_hashesself.bucket_size = bucket_size# 随机投影矩阵self.hash_weights = nn.Parameter(torch.randn(n_hashes, d_model // 2))def hash_vectors(self, vectors):"""使用LSH对向量进行哈希分桶vectors: [batch_size, seq_len, d_model]"""batch_size, seq_len, d_model = vectors.shape# 随机旋转rotated = torch.einsum('bld,hd->bhlk', vectors.view(batch_size, seq_len, -1, 2), self.hash_weights)# 计算哈希值hashes = torch.argmax(rotated, dim=-1)  # [batch, n_hashes, seq_len]return hashesdef forward(self, Q, K, V):batch_size, seq_len, d_model = Q.shape# 1. 对Q和K进行哈希q_hashes = self.hash_vectors(Q)  # [batch, n_hashes, seq_len]k_hashes = self.hash_vectors(K)# 2. 找到匹配的桶attention_mask = self.create_bucket_mask(q_hashes, k_hashes)# 3. 只在匹配的桶内计算注意力masked_attention = self.compute_bucket_attention(Q, K, V, attention_mask)return masked_attentiondef create_bucket_mask(self, q_hashes, k_hashes):"""创建桶掩码，只允许同一桶内的元素相互注意"""# 简化版实现masks = []for h in range(self.n_hashes):q_h = q_hashes[:, h, :].unsqueeze(-1)  # [batch, seq_len, 1]k_h = k_hashes[:, h, :].unsqueeze(-2)  # [batch, 1, seq_len]mask = (q_h == k_h).float()  # [batch, seq_len, seq_len]masks.append(mask)# 合并多个哈希的结果final_mask = torch.stack(masks, dim=1).sum(dim=1)  # [batch, seq_len, seq_len]return (final_mask > 0).float()

Reformer的优势分析

def reformer_complexity_analysis():"""Reformer复杂度分析"""def vanilla_complexity(seq_len):return seq_len ** 2def reformer_complexity(seq_len, n_hashes=8, bucket_size=64):# LSH注意力复杂度return seq_len * bucket_size * n_hashesprint("序列长度\t传统Transformer\tReformer\t\t加速比")print("-" * 60)for seq_len in [1024, 2048, 4096, 8192]:vanilla = vanilla_complexity(seq_len)reformer = reformer_complexity(seq_len)speedup = vanilla / reformerprint(f"{seq_len}\t\t{vanilla:,}\t\t{reformer:,}\t\t{speedup:.1f}x")reformer_complexity_analysis()

第三部分：Linformer - 线性复杂度的优雅解决方案

Linformer的核心洞察

Linformer提出了一个令人惊讶的发现：注意力矩阵具有低秩特性！这就像发现一幅复杂的画作实际上只使用了几种基本颜色的组合。

基于这个洞察，Linformer将注意力复杂度从O(n²)降低到O(n)。

低秩投影机制

class LinformerAttention(nn.Module):def __init__(self, d_model, seq_len, proj_dim=256):super().__init__()self.d_model = d_modelself.seq_len = seq_lenself.proj_dim = proj_dim# 线性投影层，将序列长度压缩self.E = nn.Linear(seq_len, proj_dim, bias=False)self.F = nn.Linear(seq_len, proj_dim, bias=False)# 查询、键、值投影self.W_q = nn.Linear(d_model, d_model)self.W_k = nn.Linear(d_model, d_model)self.W_v = nn.Linear(d_model, d_model)def forward(self, x):batch_size, seq_len, d_model = x.shape# 1. 生成Q, K, VQ = self.W_q(x)  # [batch, seq_len, d_model]K = self.W_k(x)  # [batch, seq_len, d_model]V = self.W_v(x)  # [batch, seq_len, d_model]# 2. 对K和V进行低秩投影K_proj = self.E(K.transpose(-2, -1)).transpose(-2, -1)  # [batch, proj_dim, d_model]V_proj = self.F(V.transpose(-2, -1)).transpose(-2, -1)  # [batch, proj_dim, d_model]# 3. 计算注意力（现在是线性复杂度）attention_scores = torch.matmul(Q, K_proj.transpose(-2, -1))  # [batch, seq_len, proj_dim]attention_scores = attention_scores / math.sqrt(d_model)attention_weights = F.softmax(attention_scores, dim=-1)# 4. 应用注意力权重output = torch.matmul(attention_weights, V_proj)  # [batch, seq_len, d_model]return output

Linformer的理论基础

让我们理解为什么这种方法有效：

def analyze_attention_rank():"""分析注意力矩阵的秩特性"""# 模拟一个注意力矩阵seq_len = 512torch.manual_seed(42)# 创建具有低秩特性的注意力矩阵U = torch.randn(seq_len, 64)  # 低维因子V = torch.randn(64, seq_len)attention_matrix = torch.matmul(U, V)attention_matrix = F.softmax(attention_matrix, dim=-1)# 计算矩阵的有效秩s = torch.svd(attention_matrix)[1]  # 奇异值# 分析累积能量cumulative_energy = torch.cumsum(s**2, dim=0) / torch.sum(s**2)print("前k个奇异值的累积能量占比：")for k in [32, 64, 128, 256]:if k < len(cumulative_energy):print(f"前{k}个: {cumulative_energy[k-1]:.3f}")# 可视化结果return s, cumulative_energysingular_values, cumulative_energy = analyze_attention_rank()

Linformer性能对比

def linformer_performance_comparison():"""Linformer性能对比"""def memory_usage(seq_len, method="vanilla", proj_dim=256):if method == "vanilla":# 传统方法：需要存储完整的注意力矩阵return seq_len * seq_lenelif method == "linformer":# Linformer：只需要存储投影后的矩阵return seq_len * proj_dimdef compute_flops(seq_len, d_model=512, method="vanilla", proj_dim=256):if method == "vanilla":# QK^T + softmax + 乘以Vreturn seq_len * seq_len * d_model + seq_len * seq_len * d_modelelif method == "linformer":# 投影 + QK^T + softmax + 乘以Vprojection_cost = seq_len * proj_dim * d_model * 2attention_cost = seq_len * proj_dim * d_model + seq_len * proj_dim * d_modelreturn projection_cost + attention_costprint("序列长度\t传统方法内存\tLinformer内存\t内存节省\t传统FLOPs\tLinformer FLOPs\t计算节省")print("-" * 100)for seq_len in [1024, 2048, 4096, 8192]:vanilla_mem = memory_usage(seq_len, "vanilla")linformer_mem = memory_usage(seq_len, "linformer")mem_saving = vanilla_mem / linformer_memvanilla_flops = compute_flops(seq_len, method="vanilla")linformer_flops = compute_flops(seq_len, method="linformer")flops_saving = vanilla_flops / linformer_flopsprint(f"{seq_len}\t\t{vanilla_mem:,}\t\t{linformer_mem:,}\t\t{mem_saving:.1f}x\t\t{vanilla_flops:,}\t{linformer_flops:,}\t{flops_saving:.1f}x")linformer_performance_comparison()

第四部分：Performer - Kernel方法的创新应用

Performer的数学美学

Performer采用了一种更加数学化的方法，使用Kernel技巧将注意力计算重新表述。这就像用数学的"变魔术"方法，让原本复杂的计算变得简单。

FAVOR+算法核心

import torch
import torch.nn as nn
import mathclass PerformerAttention(nn.Module):def __init__(self, d_model, num_features=256, redraw_features=True):super().__init__()self.d_model = d_modelself.num_features = num_featuresself.redraw_features = redraw_features# 特征映射参数self.register_buffer('omega', torch.randn(num_features, d_model))self.W_q = nn.Linear(d_model, d_model)self.W_k = nn.Linear(d_model, d_model)self.W_v = nn.Linear(d_model, d_model)def feature_map(self, x):"""FAVOR+特征映射x: [batch_size, seq_len, d_model]"""# 计算投影projections = torch.einsum('bld,fd->blf', x, self.omega)# 应用激活函数（ReLU变种）# 使用稳定的softmax核近似x_norm = torch.norm(x, dim=-1, keepdim=True)normalizer = x_norm / math.sqrt(self.d_model)pos_features = torch.exp(projections - normalizer)neg_features = torch.exp(-projections - normalizer)# 连接正负特征features = torch.cat([pos_features, neg_features], dim=-1)return featuresdef forward(self, x):batch_size, seq_len, d_model = x.shape# 1. 生成Q, K, VQ = self.W_q(x)  # [batch, seq_len, d_model]K = self.W_k(x)  # [batch, seq_len, d_model]V = self.W_v(x)  # [batch, seq_len, d_model]# 2. 应用特征映射Q_prime = self.feature_map(Q)  # [batch, seq_len, 2*num_features]K_prime = self.feature_map(K)  # [batch, seq_len, 2*num_features]# 3. 计算线性注意力# 首先计算K^T VKV = torch.einsum('blf,bld->bfd', K_prime, V)  # [batch, 2*num_features, d_model]# 然后计算Q(K^T V)output = torch.einsum('blf,bfd->bld', Q_prime, KV)  # [batch, seq_len, d_model]# 4. 归一化normalizer = torch.einsum('blf,bf->bl', Q_prime, K_prime.sum(dim=1))normalizer = normalizer.unsqueeze(-1) + 1e-8output = output / normalizerreturn output

Kernel方法的数学原理

让我们理解Performer背后的数学原理：

def explain_kernel_approximation():"""解释Kernel近似的数学原理"""print("传统Softmax注意力:")print("Attention(Q,K,V) = softmax(QK^T/√d)V")print()print("Kernel形式重写:")print("softmax(qk^T/√d) = exp(qk^T/√d) / Σ_j exp(qk_j^T/√d)")print()print("Performer的核心洞察:")print("exp(qk^T/√d) ≈ φ(q)^T φ(k)")print("其中 φ(x) 是特征映射函数")print()print("这样就可以重写注意力为:")print("Attention(Q,K,V) = φ(Q)(φ(K)^T V) / φ(Q)(φ(K)^T 1)")print()print("复杂度分析:")print("- 传统方法: O(L²d) 其中L是序列长度")print("- Performer: O(Ld²) 其中d通常远小于L")explain_kernel_approximation()

Performer的实验验证

def performer_approximation_quality():"""验证Performer近似质量"""def vanilla_attention(Q, K, V):"""标准注意力"""attention_weights = F.softmax(torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.size(-1)), dim=-1)return torch.matmul(attention_weights, V)def performer_attention_simplified(Q, K, V, num_features=64):"""简化版Performer注意力"""d_model = Q.size(-1)# 随机特征omega = torch.randn(num_features, d_model)# 特征映射（简化版）Q_features = torch.relu(torch.matmul(Q, omega.T))K_features = torch.relu(torch.matmul(K, omega.T))# 线性注意力KV = torch.matmul(K_features.transpose(-2, -1), V)output = torch.matmul(Q_features, KV)# 归一化normalizer = torch.matmul(Q_features, K_features.sum(dim=-2, keepdim=True).T)return output / (normalizer + 1e-8)# 测试不同序列长度下的近似质量torch.manual_seed(42)d_model = 64for seq_len in [128, 256, 512]:Q = torch.randn(1, seq_len, d_model)K = torch.randn(1, seq_len, d_model)V = torch.randn(1, seq_len, d_model)vanilla_output = vanilla_attention(Q, K, V)performer_output = performer_attention_simplified(Q, K, V)# 计算相似度similarity = F.cosine_similarity(vanilla_output.flatten(), performer_output.flatten(), dim=0)print(f"序列长度 {seq_len}: 余弦相似度 = {similarity:.4f}")performer_approximation_quality()

第五部分：三种方法的深度对比与选择指南

性能与精度权衡分析

def comprehensive_comparison():"""三种方法的全面对比"""methods = {"Vanilla Transformer": {"time_complexity": "O(L²d)","space_complexity": "O(L²)","approximation_quality": "100%","implementation_difficulty": "简单","best_for": "短序列，要求最高精度"},"Reformer": {"time_complexity": "O(L log L)","space_complexity": "O(L log L)","approximation_quality": "95-98%","implementation_difficulty": "中等","best_for": "长序列，内存受限场景"},"Linformer": {"time_complexity": "O(Ld)","space_complexity": "O(Ld)","approximation_quality": "90-95%","implementation_difficulty": "简单","best_for": "固定长度序列，要求高效率"},"Performer": {"time_complexity": "O(Ld²)","space_complexity": "O(Ld)","approximation_quality": "85-92%","implementation_difficulty": "复杂","best_for": "变长序列，理论保证需求"}}print("方法对比表:")print("-" * 100)print(f"{'方法':<20} {'时间复杂度':<15} {'空间复杂度':<15} {'近似质量':<12} {'实现难度':<12} {'最适用场景'}")print("-" * 100)for method, props in methods.items():print(f"{method:<20} {props['time_complexity']:<15} {props['space_complexity']:<15} {props['approximation_quality']:<12} {props['implementation_difficulty']:<12} {props['best_for']}")comprehensive_comparison()

选择决策树

def architecture_selection_guide():"""架构选择指南"""def recommend_architecture(seq_len, memory_constraint, accuracy_requirement, implementation_time):"""根据需求推荐架构参数:- seq_len: 序列长度- memory_constraint: 内存约束程度 (low/medium/high)- accuracy_requirement: 精度要求 (low/medium/high)- implementation_time: 实现时间 (short/medium/long)"""recommendations = []if seq_len < 1024:if accuracy_requirement == "high":recommendations.append(("Vanilla Transformer", "最高精度，计算可接受"))else:recommendations.append(("Linformer", "高效且简单实现"))elif seq_len < 4096:if memory_constraint == "high":recommendations.append(("Reformer", "内存效率高"))elif accuracy_requirement == "high":recommendations.append(("Linformer", "精度与效率平衡"))else:recommendations.append(("Performer", "理论保证强"))else:  # 长序列if memory_constraint == "high":recommendations.append(("Reformer", "唯一可行的内存高效方案"))elif implementation_time == "short":recommendations.append(("Linformer", "快速原型开发"))else:recommendations.append(("Performer", "长期项目的最佳选择"))return recommendations# 测试不同场景scenarios = [(512, "low", "high", "short"),(2048, "medium", "medium", "medium"),(8192, "high", "medium", "long"),(16384, "high", "low", "medium")]print("架构选择建议:")print("-" * 80)for seq_len, mem_constraint, accuracy, impl_time in scenarios:print(f"\n场景: 序列长度={seq_len}, 内存约束={mem_constraint}, 精度要求={accuracy}, 实现时间={impl_time}")recommendations = recommend_architecture(seq_len, mem_constraint, accuracy, impl_time)for arch, reason in recommendations:print(f"  推荐: {arch} - {reason}")architecture_selection_guide()

第六部分：实际应用案例与最佳实践

长文档处理案例

class DocumentProcessingPipeline:"""长文档处理流水线示例"""def __init__(self, architecture="reformer"):self.architecture = architectureself.max_seq_len = self._get_max_seq_len()def _get_max_seq_len(self):"""根据架构确定最大序列长度"""limits = {"vanilla": 1024,"reformer": 16384,"linformer": 8192,"performer": 32768}return limits.get(self.architecture, 1024)def process_long_document(self, document_text):"""处理长文档"""# 1. 文档分段chunks = self._chunk_document(document_text, self.max_seq_len)# 2. 选择合适的模型model = self._get_model()# 3. 批量处理results = []for chunk in chunks:result = model.process(chunk)results.append(result)# 4. 结果合并final_result = self._merge_results(results)return final_resultdef _chunk_document(self, text, max_length):"""智能文档分段"""# 简化实现words = text.split()chunks = []current_chunk = []for word in words:if len(current_chunk) + len(word.split()) > max_length:if current_chunk:chunks.append(" ".join(current_chunk))current_chunk = [word]else:current_chunk.append(word)if current_chunk:chunks.append(" ".join(current_chunk))return chunksdef benchmark_architectures(self, test_documents):"""基准测试不同架构"""results = {}for arch in ["vanilla", "reformer", "linformer", "performer"]:pipeline = DocumentProcessingPipeline(arch)total_time = 0total_memory = 0accuracy_scores = []for doc in test_documents:start_time = time.time()result = pipeline.process_long_document(doc)end_time = time.time()total_time += (end_time - start_time)# 这里应该测量实际内存使用和精度results[arch] = {"avg_time": total_time / len(test_documents),"max_seq_len": pipeline.max_seq_len,"memory_efficiency": self._estimate_memory_efficiency(arch),"accuracy": self._estimate_accuracy(arch)}return results# 使用示例
pipeline = DocumentProcessingPipeline("reformer")
print(f"使用 {pipeline.architecture} 架构，最大序列长度: {pipeline.max_seq_len}")

生产部署建议

def production_deployment_guide():"""生产部署指南"""deployment_considerations = {"Reformer": {"优势": ["内存效率极高","支持超长序列","训练稳定"],"劣势": ["实现复杂","调试困难","哈希冲突影响"],"部署建议": ["适合内存受限环境","需要充分测试哈希参数","建议渐进式部署"]},"Linformer": {"优势": ["实现简单","性能可预测","调试容易"],"劣势": ["需要预设序列长度","投影维度需要调优","长序列外推能力有限"],"部署建议": ["适合固定长度任务","快速原型开发首选","需要针对任务调优投影维度"]},"Performer": {"优势": ["理论保证强","支持变长序列","无需预设长度"],"劣势": ["数值稳定性挑战","超参数敏感","特征重采样需要"],"部署建议": ["适合研究型项目","需要仔细调试数值精度","建议使用稳定的特征映射"]}}print("生产部署指南:")print("=" * 60)for arch, details in deployment_considerations.items():print(f"\n{arch}:")print("-" * 30)print("优势:")for advantage in details["优势"]:print(f"  ✓ {advantage}")print("劣势:")for disadvantage in details["劣势"]:print(f"  ✗ {disadvantage}")print("部署建议:")for suggestion in details["部署建议"]:print(f"  → {suggestion}")production_deployment_guide()

第七部分：未来发展趋势与思考

混合架构的探索

class HybridTransformer(nn.Module):"""混合架构示例：结合多种优化技术"""def __init__(self, d_model, num_layers, seq_len):super().__init__()self.layers = nn.ModuleList()for i in range(num_layers):# 根据层的位置选择不同的注意力机制if i < num_layers // 3:# 早期层使用标准注意力（局部建模）attention = VanillaAttention(d_model)elif i < 2 * num_layers // 3:# 中间层使用Linformer（全局建模）attention = LinformerAttention(d_model, seq_len)else:# 后期层使用Performer（复杂推理）attention = PerformerAttention(d_model)self.layers.append(TransformerLayer(attention, d_model))def forward(self, x):for layer in self.layers:x = layer(x)return x

自适应架构选择

class AdaptiveTransformer(nn.Module):"""自适应选择注意力机制的Transformer"""def __init__(self, d_model):super().__init__()self.attention_selector = nn.Linear(d_model, 3)  # 3种注意力类型self.attentions = nn.ModuleList([VanillaAttention(d_model),LinformerAttention(d_model),PerformerAttention(d_model)])def forward(self, x):# 根据输入特征动态选择注意力机制attention_weights = F.softmax(self.attention_selector(x.mean(dim=1)), dim=-1)outputs = []for i, attention in enumerate(self.attentions):output = attention(x)outputs.append(attention_weights[:, i:i+1, None] * output)return sum(outputs)

总结：架构选择的智慧

通过深入分析Reformer、Linformer和Performer三种架构，我们可以得出以下关键洞察：

核心要点回顾

1. Reformer：通过LSH注意力和可逆层实现内存高效的长序列处理
2. Linformer：利用注意力矩阵的低秩特性实现线性复杂度
3. Performer：使用Kernel方法提供理论保证的高效近似

选择原则

def final_architecture_recommendations():"""最终架构推荐原则"""principles = {"场景驱动": "根据具体应用场景选择，没有万能解决方案","性能权衡": "在精度、效率、实现复杂度之间找到平衡","渐进优化": "从简单方案开始，逐步优化到复杂架构","充分测试": "在真实数据上验证性能，避免过度工程化","未来兼容": "考虑架构的扩展性和维护性"}print("架构选择的五大原则:")print("=" * 50)for principle, description in principles.items():print(f"{principle}: {description}")print("\n记住：最好的架构是能解决你实际问题的架构！")final_architecture_recommendations()

展望未来

这三种架构代表了Transformer优化的不同思路，但未来的发展可能会朝着以下方向：

1. 混合架构：结合多种优化技术的优势
2. 自适应机制：根据输入动态调整计算策略
3. 硬件协同：与特定硬件深度优化的架构
4. 理论突破：新的数学框架和算法创新

通过理解这些变种架构的核心思想，我们不仅能够选择合适的方案解决当前问题，更能够为未来的创新奠定基础。在这个快速发展的领域中，保持学习和实验的心态是最重要的。

---

参考资料与进一步学习

1. Reformer: The Efficient Transformer
2. Linformer: Self-Attention with Linear Complexity
3. Rethinking Attention with Performers
4. Efficient Transformers: A Survey
5. Long Range Arena: A Benchmark for Efficient Transformers

代码实现参考

• Hugging Face Transformers库中的高效Transformer实现
• Google Research的Performer官方实现
• Facebook Research的Linformer代码
• Reformer的PyTorch实现示例