61 transformer blocks › 60 MoE blocks › Latent attention › Query compression
Query latent
×tensorself.q_a_proj(hidden_states)
The compressed query features before normalization.
- shape
- [B batch, T positions, 1536 q latent]
Sources
Contextual evidence from Query down-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
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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 down-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
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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
)Contextual evidence from Query down-projection
Weights are stored in FP8 (e4m3) with one scale per 128 × 128 block; the scales are quantization metadata and are excluded from parameter counts.
linear · moonshotai/Kimi-K2-Instruct / config.json
Pinned revision fd1984e2b7a3 ↗ · lines 38–46
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"quantization_config": {
"activation_scheme": "dynamic",
"fmt": "e4m3",
"quant_method": "fp8",
"weight_block_size": [
128,
128
]
},Contextual evidence from Query latent RMSNorm
RMSNorm owns one gain per feature and computes in float32.
rmsnorm · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 94–111
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class DeepseekV3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
DeepseekV3RMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
ALL_LAYERNORM_LAYERS.append(DeepseekV3RMSNorm)