61 transformer blocks › 60 MoE blocks › Latent attention › Query heads
Query content features
×tensorq · 770
All heads’ content features side by side.
- shape
- [B batch, T positions, 8192 heads × content]
Sources
Contextual evidence from Query content up-projection
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 · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 662–688
Read code excerpt
self.q_a_proj = nn.Linear(
self.hidden_size, config.q_lora_rank, bias=config.attention_bias
)
self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
self.q_b_proj = nn.Linear(
config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
)
self.kv_a_proj_with_mqa = nn.Linear(
self.hidden_size,
config.kv_lora_rank + config.qk_rope_head_dim,
bias=config.attention_bias,
)
self.kv_a_layernorm = DeepseekV3RMSNorm(config.kv_lora_rank)
self.kv_b_proj = nn.Linear(
config.kv_lora_rank,
self.num_heads
* (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
bias=False,
)
self.o_proj = nn.Linear(
self.num_heads * self.v_head_dim,
self.hidden_size,
bias=config.attention_bias,
)
self._init_rope()Contextual evidence from Query content up-projection
Queries are produced through a low-rank latent and split into a non-rotated and a rotated part.
mla-queries · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 767–774
Read code excerpt
if self.q_lora_rank is None:
q = self.q_proj(hidden_states)
else:
q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
q_nope, q_pe = torch.split(
q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
)