dela
d1da0816f2
Replace dense all-expert forward (16 experts × all tokens) with permute-dispatch: sort token-expert pairs by expert id, pad to [R, C, ℓ] (C = max tokens per expert), run 3 bmm calls for the batched SiTU-GLU activation, then scatter-add weighted results back. Routed expert FLOPs drop from R·N to R·C (C ≈ N·k/R under uniform routing). SiTU parameter structure unchanged; checkpoint compatible. Tests: sparse-vs-dense fwd/bwd equivalence, unselected expert zero grad, last_capacity tracking.
165 lines
6.2 KiB
Python
165 lines
6.2 KiB
Python
"""Stable LatentMoE (K3): shared 全宽 + routed 半宽专家 + SiTU-GLU + Top-k.
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对照 learning/kimi-k3-notes §Stable LatentMoE:
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z = W_down(x) [B, T, ℓ] ℓ = d/2 latent 接口宽
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u = Σ_{i∈Top-k(x)} p_i E_i^rt(z) [B, T, ℓ] routed 专家只在 ℓ 上算
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y = Σ_j E_j^sh(x) + W_up RMSNorm(u) [B, T, d] shared 全宽
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SiTU-GLU: gate = β1·tanh(W_g x/β1)⊙σ(W_g x); up = β2·tanh(W_u x/β2)
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||SiTU-GLU||_∞ ≤ β1·β2 (=100), 原点附近≈SwiGLU, 远端软饱和防低精度溢出.
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E: R^in → R^in (内部中间维 d_ff).
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Router: Top-k logits 基于全宽 x (笔记 Topk(x)); 归一化权重取 softmax(topk).
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Routed 执行: permute-dispatch, pad 到 [R, C, ℓ], 三次 bmm（SiTU 参数结构不变）.
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"""
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from __future__ import annotations
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import torch
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import torch.nn.functional as F
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from torch import nn
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from .rmsnorm import RMSNorm
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class SiTU(nn.Module):
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"""SiTU-GLU expert: gate 支软上限 β1, up 支软上限 β2, 输出回到输入维."""
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def __init__(self, dim_in: int, dim_ff: int, beta1: float = 4.0, beta2: float = 25.0):
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super().__init__()
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self.beta1, self.beta2 = beta1, beta2
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self.w_g = nn.Linear(dim_in, dim_ff, bias=False)
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self.w_u = nn.Linear(dim_in, dim_ff, bias=False)
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self.w_o = nn.Linear(dim_ff, dim_in, bias=False)
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def forward(self, x: torch.Tensor):
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wg = self.w_g(x)
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g = self.beta1 * torch.tanh(wg / self.beta1) * torch.sigmoid(wg)
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u = self.beta2 * torch.tanh(self.w_u(x) / self.beta2)
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return self.w_o(g * u)
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class LatentMoE(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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latent_size: int,
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n_routed: int,
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top_k: int,
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n_shared: int,
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d_ff: int,
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beta1: float = 4.0,
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beta2: float = 25.0,
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):
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super().__init__()
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self.latent_size = latent_size
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self.n_routed = n_routed
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self.top_k = top_k
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self.down = nn.Linear(hidden_size, latent_size, bias=False) # W↓
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self.router = nn.Linear(hidden_size, n_routed, bias=False) # Top-k logits
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self.shared = nn.ModuleList(
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[SiTU(hidden_size, d_ff, beta1, beta2) for _ in range(n_shared)]
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)
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self.experts = nn.ModuleList(
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[SiTU(latent_size, d_ff, beta1, beta2) for _ in range(n_routed)]
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)
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self.norm = RMSNorm(latent_size)
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self.up = nn.Linear(latent_size, hidden_size, bias=False) # W↑
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self.last_route_ids: torch.Tensor | None = None
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self.last_capacity: int = 0
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@classmethod
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def from_config(cls, config) -> LatentMoE:
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return cls(
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config.hidden_size,
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config.moe_latent_size,
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config.n_routed,
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config.top_k,
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config.n_shared,
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config.moe_d_ff,
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config.situ_beta1,
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config.situ_beta2,
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)
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def _routed_u(
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self, z: torch.Tensor, ids: torch.Tensor, probs: torch.Tensor
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) -> torch.Tensor:
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"""Permute-dispatch + pad to [R, C, ℓ] + 3 bmm + scatter-add.
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``C = max(counts)``: FLOPs are ``R·C``, not ``sum(counts)``. Padding
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slots are not gathered, so they contribute zero gradient. Empty
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experts stay in the stacked weights (padded grouped GEMM).
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"""
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B, T, ell = z.shape
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N = B * T
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R, k = self.n_routed, self.top_k
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device = z.device
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tok = torch.arange(N, device=device).unsqueeze(1).expand(N, k).reshape(-1)
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eid = ids.reshape(-1)
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pw = probs.reshape(-1)
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order = eid.argsort(stable=True)
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tok, eid, pw = tok[order], eid[order], pw[order]
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counts = torch.bincount(eid, minlength=R)
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offsets = counts.cumsum(0) - counts
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local_pos = torch.arange(N * k, device=device) - offsets[eid]
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C = int(counts.max().item()) if eid.numel() else 0
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self.last_capacity = C
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u_flat = z.new_zeros(N, ell)
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if C == 0 or eid.numel() == 0:
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return u_flat.view(B, T, ell)
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gathered = z.reshape(N, ell)[tok]
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padded = torch.index_put(
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gathered.new_zeros(R, C, ell), (eid, local_pos), gathered
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)
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w_g = torch.stack([e.w_g.weight for e in self.experts]) # [R, ff, ℓ]
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w_u = torch.stack([e.w_u.weight for e in self.experts])
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w_o = torch.stack([e.w_o.weight for e in self.experts]) # [R, ℓ, ff]
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beta1 = self.experts[0].beta1
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beta2 = self.experts[0].beta2
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wg = torch.bmm(padded, w_g.transpose(-1, -2)) # [R, C, ff]
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g = beta1 * torch.tanh(wg / beta1) * torch.sigmoid(wg)
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wu = torch.bmm(padded, w_u.transpose(-1, -2))
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hidden = beta2 * torch.tanh(wu / beta2)
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out = torch.bmm(g * hidden, w_o.transpose(-1, -2)) # [R, C, ℓ]
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weighted = pw.unsqueeze(-1) * out[eid, local_pos]
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u_flat = u_flat.index_add(0, tok, weighted)
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return u_flat.view(B, T, ell)
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def forward(self, x: torch.Tensor):
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B, T, _ = x.shape
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z = self.down(x) # [B, T, ℓ]
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logits = self.router(x) # [B, T, n_routed]
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topk = torch.topk(logits, self.top_k, dim=-1)
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ids = topk.indices # [B, T, k]
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self.last_route_ids = ids.detach()
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probs = F.softmax(topk.values, dim=-1) # [B, T, k]
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u = self._routed_u(z, ids, probs)
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shared_out = torch.stack([e(x) for e in self.shared]).sum(0) # [B, T, d]
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return shared_out + self.up(self.norm(u))
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def moe_route_frac(model: nn.Module) -> torch.Tensor | None:
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"""Mean expert occupancy over LatentMoE layers from the last forward."""
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hists: list[torch.Tensor] = []
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n_routed: int | None = None
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for module in model.modules():
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if not isinstance(module, LatentMoE) or module.last_route_ids is None:
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continue
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n_routed = module.n_routed
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ids = module.last_route_ids.reshape(-1)
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hists.append(torch.bincount(ids, minlength=n_routed).float())
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if not hists or n_routed is None:
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return None
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stacked = torch.stack(hists).sum(0)
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return stacked / stacked.sum().clamp_min(1.0)
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