核心结论FRSM V6 Dense MoE 训练速度慢于 Transformer(同结构下 3.6x),但推理 O(1)、长序列显存恒定、总成本在推理部署场景下更优。它不是 Transformer 的替代品,而是特定场景下的更好选择。一、FRSM 架构概述FRSM(Fast Recurrent State Machine)是一个多尺度内容门控状态机——RNN 的现代化变体:每个专家有 num_scales 个并行的时间尺度,各自维护一个状态向量内容门控网络动态决定每个尺度的写入强度Dense MoE 版本:16 个路由专家 1 个共享专家,全部通过堆叠 einsum 计算路由器产生软权重,专家输出按权重混合共享专家始终激活,捕获通用知识关键改进:去掉了 Sparse MoE 的 token-to-expert gather 参数拷贝(原占总步时间 77%),改为 Dense/Soft MoE 的全专家堆叠 einsum chunk 并行。二、性能数据(实测于 RTX 4090 D, 24GB, T512)2.1 公平对比:两边都是 Dense MoE 结构公平对比:Transformer 也用上同样的 16 专家 Dense MoE,保证 FLOPs/参数量可比。模型参数Btok/s显存相对速度Transformer Dense MoE67M32219,2339.4GB1.0xFRSM Dense MoE C12845M2852,96820.3GB慢 4.1xFRSM Dense MoE C512(全并行)45M2460,51917.8GB慢 3.6x结论:同样 MoE 结构下,FRSM 比 Transformer 慢 3.6 倍。这是 RNN 串行 vs Transformer 并行的架构性差距。2.2 FRSM 在不同 chunk 下的训练速度C步数BB*Ctok/svs Trfm1(无chunk)51288881,924慢 114x16322844837,224慢 5.9x32162889643,566慢 5.0x648281,79249,386慢 4.4x1284283,58452,968慢 4.1x512(全并行)12412,28860,519慢 3.6xchunk 将差距从 114x 缩到 3.6x。C512 时 FRSM 和 Transformer 一样一次性处理全部 token,但 FRSM 的 16 专家 × 4 尺度 × 3 门控 192 个独立 matmul 无法融合成一个大 matmul,GPU 利用率先天不足。2.3 推理速度(生成)场景FRSMTransformer单步推理(1 token)O(1) ~2msO(N) 随长度增长生成 256 token~440ms~320ms生成 2048 token~3.5s~10s生成 8192 token~14sOOM 或极慢FRSM 的generate_step永远常数时间,Transformer 的注意力成本随序列增长。在生成长度 1000 时 FRSM 推理反超。2.4 序列长度与显存TFRSM(显存/速度)Transformer(显存/速度)51220GB / 60K tok/s9GB / 219K tok/s102420GB / 21K tok/s17GB / 232K tok/s204820GB / 9K tok/s~30GB(OOM)4096OOM(logits显存)OOM(注意力)FRSM 的显存与 T 弱相关(仅受 B×T logits 影响),Transformer 受 O(T²) 注意力矩阵拖累。在 T2K 时 Transformer 先 OOM。三、总成本分析以训练一个 45M 模型 长期推理部署(1B token 生成)为例:成本项TransformerFRSM Dense MoE训练 GPU 时1x~3.6x推理 GPU 时(1B token)~8,200h~500h(16x 节省)总成本(训练推理)~8,500h~2,300h(73% 节省)对于推理部署为主的场景,FRSM 的总成本比 Transformer 低 73%。训练端的 3.6x 差距被推理端的 16x 优势轻松覆盖。四、技术总结维度FRSM Dense MoETransformer训练速度慢 3.6x(架构差距)快推理速度(短)略慢略快推理速度(长)O(1) 永远快O(N) 越长越慢长序列显存与 T 弱相关O(T²) 爆显存总成本(推理重)低 73%高架构复杂度低(RNN 循环)高(注意力KVCache)可控性完全可控标准架构五、最终结论FRSM V6 Dense MoE 训练速度追不上 Transformer——3.6x 是 RNN 串行架构的先天上限。但它的价值不在训练速度,在:推理永远 O(1)长序列显存不爆总成本在推理部署场景下胜出架构完全可控如果你的场景以推理部署为主(对话、生成、Agent),FRSM 的长期总成本远低于 Transformer。如果追求极致训练速度,Transformer 是正确选择。附录: FRSM V6 Dense MoE 完整代码文件:frsm_v6_moe/frsm_v6a_dense_moe.py FRSM V6a Dense MoE — 全部专家用堆叠 einsum无 gather/chunk/检查点 importmath,torch,torch.nnasnn,torch.nn.functionalasFclassFRSM_V6_DenseMoE(nn.Module):def__init__(self,vocab_size,d_model256,num_scales4,n_experts16,n_shared1,router_noise1.0,aux_loss_weight0.01,chunk_size0):super().__init__()self.d_modeld_model;self.num_scalesnum_scales self.n_expertsn_experts;self.n_sharedn_shared;self.router_noiserouter_noise self.aux_loss_weightaux_loss_weight;self.chunk_sizechunk_size self.aux_losstorch.tensor(0.0)E,S,Dn_experts,num_scales,d_model;dhD//4self.embednn.Embedding(vocab_size,D);self.input_projnn.Linear(D,D)fornin[W_forget,W_input,W_cand]:setattr(self,n,nn.Parameter(torch.empty(E,S,D,2*D)))setattr(self,b_n[2:],nn.Parameter(torch.empty(E,S,D)))self.gate_W1nn.Parameter(torch.empty(E,S,dh,2*D))self.gate_b1nn.Parameter(torch.empty(E,S,dh))self.gate_W2nn.Parameter(torch.empty(E,S,1,dh))self.gate_b2nn.Parameter(torch.empty(E,S,1))self.fusion_Wnn.Parameter(torch.empty(E,S*D,D))self.fusion_bnn.Parameter(torch.empty(E,D))ifn_shared0:fornin[W_forget,W_input,W_cand]:setattr(self,n_sh,nn.Parameter(torch.empty(n_shared,S,D,2*D)))setattr(self,b_n.split(_)[1]_sh,nn.Parameter(torch.empty(n_shared,S,D)))self.gate_W1_shnn.Parameter(torch.empty(n_shared,S,dh,2*D))self.gate_b1_shnn.Parameter(torch.empty(n_shared,S,dh))self.gate_W2_shnn.Parameter(torch.empty(n_shared,S,1,dh))self.gate_b2_shnn.Parameter(torch.empty(n_shared,S,1))self.fusion_W_shnn.Parameter(torch.empty(n_shared,S*D,D))self.fusion_b_shnn.Parameter(torch.empty(n_shared,D))self.routernn.Linear(D,E)self.output_normnn.LayerNorm(D);self.output_projnn.Linear(D,vocab_size)self._init_w()def_init_w(self):def_k(p):foreinrange(p.size(0)):forsinrange(self.num_scales):nn.init.kaiming_uniform_(p[e,s],amath.sqrt(5))forpnin[W_forget,W_input,W_cand,gate_W1,gate_W2]:_k(getattr(self,pn))foreinrange(self.n_experts):nn.init.kaiming_uniform_(self.fusion_W[e],amath.sqrt(5))ifself.n_shared0:forpnin[W_forget,W_input,W_cand,gate_W1,gate_W2]:_k(getattr(self,pn_sh))foreinrange(self.n_shared):nn.init.kaiming_uniform_(getattr(self,fusion_W_sh)[e],amath.sqrt(5))forn,pinself.named_parameters():ifbiasinn:nn.init.zeros_(p)nn.init.zeros_(self.b_cand);nn.init.zeros_(self.gate_b1);nn.init.zeros_(self.gate_b2);nn.init.zeros_(self.fusion_b)nn.init.constant_(self.b_forget,1.0);nn.init.constant_(self.b_input,-2.0)ifself.n_shared0:nn.init.zeros_(self.b_cand_sh);nn.init.zeros_(self.gate_b1_sh);nn.init.zeros_(self.gate_b2_sh);nn.init.zeros_(self.fusion_b_sh)nn.init.constant_(self.b_forget_sh,1.0);nn.init.constant_(self.b_input_sh,-2.0)nn.init.normal_(self.router.weight,0,0.02);nn.init.normal_(self.embed.weight,0,0.02)nn.init.kaiming_uniform_(self.input_proj.weight,amath.sqrt(5))nn.init.kaiming_uniform_(self.output_proj.weight,amath.sqrt(5))def_estep(self,H,inp,Wf,Wi,Wc,bf,bi,bc,gW1,gb1,gW2,gb2,fW,fb):E,BH.shape[:2];S,Dself.num_scales,self.d_model inpinp.reshape(-1,D)# (B_actual, D)ieinp.unsqueeze(0).unsqueeze(2).expand(E,B,S,D)gtorch.cat([H,ie],dim-1)ftorch.sigmoid(torch.einsum(ebsj,esij-ebsi,g,Wf)bf.unsqueeze(1))itorch.sigmoid(torch.einsum(ebsj,esij-ebsi,g,Wi)bi.unsqueeze(1))ctorch.tanh(torch.einsum(ebsj,esij-ebsi,g,Wc)bc.unsqueeze(1))candf*Hi*c h1F.gelu(torch.einsum(ebsj,esij-ebsi,g,gW1)gb1.unsqueeze(1))sttorch.sigmoid(torch.einsum(ebsi,esoi-ebso,h1,gW2)gb2.unsqueeze(1))Hnst*cand(1-st)*H fusedtorch.einsum(ebk,eki-ebi,Hn.reshape(E,B,S*D),fW)fb.unsqueeze(1)returnHn,fuseddef_step(self,H,Hs,inp):Hn,fusedself._estep(H,inp,self.W_forget,self.W_input,self.W_cand,self.b_forget,self.b_input,self.b_cand,self.gate_W1,self.gate_b1,self.gate_W2,self.gate_b2,self.fusion_W,self.fusion_b)ifself.n_shared0:Hsn,sfself._estep(Hs,inp,self.W_forget_sh,self.W_input_sh,self.W_cand_sh,self.b_forget_sh,self.b_input_sh,self.b_cand_sh,self.gate_W1_sh,self.gate_b1_sh,self.gate_W2_sh,self.gate_b2_sh,self.fusion_W_sh,self.fusion_b_sh)sfsf.sum(dim0)# (NS,B,D) - (B,D)else:Hsn,sfNone,0probsself._route(inp)combined((probs.t().unsqueeze(-1)*fused).sum(dim0))sfreturnHn,Hsn,combined,probsdef_route(self,inp):lself.router(inp)ifself.trainingandself.router_noise0:lltorch.randn_like(l)*self.router_noisereturnF.softmax(l,dim-1)defforward(self,x,h_prevNone,return_stateFalse):B,Tx.shape;E,S,Dself.n_experts,self.num_scales,self.d_model xeself.embed(x);iseqself.input_proj(xe)ifh_previsNone:Htorch.zeros(E,B,S,D,devicex.device,dtypeiseq.dtype)Hstorch.zeros(self.n_shared,B,S,D,devicex.device,dtypeiseq.dtype)ifself.n_shared0elseNoneelse:H,Hsh_prev logitstorch.zeros(B,T,self.output_proj.out_features,devicex.device,dtypeiseq.dtype)auxtorch.zeros((),devicex.device,dtypetorch.float32)Cself.chunk_sizeifself.chunk_size0elsemax(1,int(math.sqrt(T)))fortsinrange(0,T,C):temin(tsC,T);chte-ts iciseq[:,ts:te,:]bchB*ch infic.reshape(bch,D)HfH.unsqueeze(2).expand(E,B,ch,S,D).reshape(E,bch,S,D)HsfHs.unsqueeze(2).expand(self.n_shared,B,ch,S,D).reshape(self.n_shared,bch,S,D)ifHsisnotNoneelseNoneHnf,fused_fself._estep(Hf,inf,self.W_forget,self.W_input,self.W_cand,self.b_forget,self.b_input,self.b_cand,self.gate_W1,self.gate_b1,self.gate_W2,self.gate_b2,self.fusion_W,self.fusion_b)ifself.n_shared0:Hsnf,sfself._estep(Hsf,inf,self.W_forget_sh,self.W_input_sh,self.W_cand_sh,self.b_forget_sh,self.b_input_sh,self.b_cand_sh,self.gate_W1_sh,self.gate_b1_sh,self.gate_W2_sh,self.gate_b2_sh,self.fusion_W_sh,self.fusion_b_sh)else:Hsnf,sfNone,0ifsfisnotNone:sfsf.sum(dim0)# (NS,bch,D)-(bch,D)probsself._route(ic[:,0,:])pbfprobs.unsqueeze(1).expand(B,ch,E).reshape(bch,E)comb_f((pbf.t().unsqueeze(-1)*fused_f).sum(dim0))sf combcomb_f.reshape(B,ch,D)logits[:,ts:te,:]self.output_proj(self.output_norm(comb))litorch.arange(B,devicex.device)*ch(ch-1)HHnf[:,li,:,:]HsHsnf[:,li,:,:]ifHsnfisnotNoneelseNonetpeprobs.mean(0);auxauxE*torch.sum(tpe*probs.mean(0))self.aux_lossaux/max(1,(TC-1)//C)ifreturn_state:returnlogits,(H,Hs)returnlogitstorch.no_grad()defgenerate_step(self,token,h_prev):H,Hsh_prev;Btoken.size(0)xeself.embed(token).squeeze(1);inpself.input_proj(xe)Hn,fuself._estep(H,inp,self.W_forget,self.W_input,self.W_cand,self.b_forget,self.b_input,self.b_cand,self.gate_W1,self.gate_b1,self.gate_W2,self.gate_b2,self.fusion_W,self.fusion_b)ifself.n_shared0:Hsn,sfself._estep(Hs,inp,self.W_forget_sh,self.W_input_sh,self.W_cand_sh,self.b_forget_sh,self.b_input_sh,self.b_cand_sh,self.gate_W1_sh,self.gate_b1_sh,self.gate_W2_sh,self.gate_b2_sh,self.fusion_W_sh,self.fusion_b_sh)sfsf.sum(dim0)else:Hsn,sfNone,0probsself._route(inp)comb((probs.t().unsqueeze(-1)*fu).sum(dim0))sfreturnself.output_proj(self.output_norm(comb)),(Hn,Hsn)