61 transformer blocks › 58 MoE blocks › Sparse latent attention › Query heads
Split rotary heads
×no parametersq.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
Partition the rotary features into 128 heads.
- in
- [B batch, T positions, 8192 heads × rotary]
- out
- [B batch, 128 heads, T positions, 64 rotary]
Codemodel.py 560–564
qr = self.q_norm(self.wq_a(x))
q = self.wq_b(qr)
q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
q_pe = apply_rotary_emb(q_pe, freqs_cis)Sources1
Direct evidence
Queries are produced through a low-rank latent, split into a non-rotated and a rotated part, and rotated.
mla-queries · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 560–564
Read code excerpt
qr = self.q_norm(self.wq_a(x))
q = self.wq_b(qr)
q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
q_pe = apply_rotary_emb(q_pe, freqs_cis)