- in
- [B batch, 1 positions, 7168 d] [1 positions, 32 pairs]
- out
- [B batch, 1 positions, 64 rotary]
- parameters
- 8,847,360 · × 58 MoE blocks = 513,146,880
- mode
- decode · P = 1,000 cached positions · L = P + 1 = 1,001 keys read
Parameters
8,847,360 parameters per instance, across its learned components
| Component | Calculation | Parameters |
|---|---|---|
| Key content up-projection | 8,388,608 | |
| Rotary key projection | 458,752 | |
| Per-instance total | 8,847,360 | |
Across 58 independent MoE blocks: parameters.
Derived from the pinned reference configuration and inference code. Checkpoint tensors have not been independently counted; FP8 block scales and the multi-token-prediction module are not counted.
Explanation
One matrix, wkv_b, produces the content keys and the values together; the reference splits them after the product (model.py 576–578).
Content keys carry no rotation, so when decoding the reference folds into the queries and scores them against the cached latent directly, which gives the same scores (model.py 591–597).
Codemodel.py 523–589
self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
self.v_head_dim = args.v_head_dim
self.wq_a = Linear(self.dim, self.q_lora_rank)
self.q_norm = RMSNorm(self.q_lora_rank)
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
self.kv_norm = RMSNorm(self.kv_lora_rank)
self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
self.softmax_scale = self.qk_head_dim ** -0.5
self.scale_fmt = args.scale_fmt
if args.max_seq_len > args.original_seq_len:
mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
self.softmax_scale = self.softmax_scale * mscale * mscale
self.indexer = Indexer(args) kv = self.wkv_a(x)
kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv = self.kv_norm(kv)
k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis) if mask is not None: # MHA prefill
q = torch.cat([q_nope, q_pe], dim=-1)
kv = self.wkv_b(kv)
kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
scores = torch.einsum("bshd,bthd->bsht", q, k).mul_(self.softmax_scale)
# indexer
topk_indices = self.indexer(x, qr, start_pos, freqs_cis, mask)
index_mask = torch.full((bsz, seqlen, seqlen), float("-inf"), device=x.device).scatter_(-1, topk_indices, 0)
index_mask += mask
scores += index_mask.unsqueeze(2)
scores = scores.softmax(dim=-1)
x = torch.einsum("bsht,bthd->bshd", scores, v)Sources3
Direct evidence
MLA owns a query down-projection, query RMSNorm and up-projection, a joint key–value down-projection that also emits the shared rotary key, a latent RMSNorm, a key–value up-projection and an output projection.
mla-init · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 523–539
Read code excerpt
self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
self.v_head_dim = args.v_head_dim
self.wq_a = Linear(self.dim, self.q_lora_rank)
self.q_norm = RMSNorm(self.q_lora_rank)
self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
self.kv_norm = RMSNorm(self.kv_lora_rank)
self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
self.softmax_scale = self.qk_head_dim ** -0.5
self.scale_fmt = args.scale_fmt
if args.max_seq_len > args.original_seq_len:
mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
self.softmax_scale = self.softmax_scale * mscale * mscale
self.indexer = Indexer(args)Direct evidence
The joint down-projection yields the key–value latent and one rotary key shared by all heads; the latent is normalized and the key rotated.
mla-kv · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 565–568
Read code excerpt
kv = self.wkv_a(x)
kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv = self.kv_norm(kv)
k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)Direct evidence
In the full-sequence (prefill) path, the latent is up-projected into per-head keys and values; each head’s key joins its non-rotated part with the shared rotary key; scores are scaled, masked by the indexer’s selection and the causal mask, normalized and used to mix values.
mla-prefill · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 574–589
Read code excerpt
if mask is not None: # MHA prefill
q = torch.cat([q_nope, q_pe], dim=-1)
kv = self.wkv_b(kv)
kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
scores = torch.einsum("bshd,bthd->bsht", q, k).mul_(self.softmax_scale)
# indexer
topk_indices = self.indexer(x, qr, start_pos, freqs_cis, mask)
index_mask = torch.full((bsz, seqlen, seqlen), float("-inf"), device=x.device).scatter_(-1, topk_indices, 0)
index_mask += mask
scores += index_mask.unsqueeze(2)
scores = scores.softmax(dim=-1)
x = torch.einsum("bsht,bthd->bshd", scores, v)