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[fix] top-1 router gradient

master
jingyaogong 2026-09-18 16:34:37 +08:00
parent 740d467ece
commit fa36e4861d
1 changed files with 6 additions and 1 deletions
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7
model/model_minimind.py
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@ -158,7 +158,12 @@ class MOEFeedForward(nn.Module):
x_flat = x.view(-1, hidden_dim) x_flat = x.view(-1, hidden_dim)
scores = F.softmax(self.gate(x_flat), dim=-1) scores = F.softmax(self.gate(x_flat), dim=-1)
topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False) topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False)
if self.config.norm_topk_prob: topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20) if self.config.norm_topk_prob:
if self.config.num_experts_per_tok > 1:
topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20)
else:
top1 = torch.topk(F.softmax(self.gate(x_flat.detach()), dim=-1), k=1, dim=-1, sorted=False)[0]
topk_weight = top1 - top1.detach() + 1.0 # k=1: 1.0 forward on purpose, gradient via straight-through
y = torch.zeros_like(x_flat) y = torch.zeros_like(x_flat)
for i, expert in enumerate(self.experts): for i, expert in enumerate(self.experts):
mask = (topk_idx == i) mask = (topk_idx == i)