Kimi K2 · Key–value latent RMSNorm

61 transformer blocks › 60 MoE blocks › Latent attention › Key–value compression

Key–value latent RMSNorm

learnedself.kv_a_layernorm(compressed_kv)

Rescale each position’s features using their root mean square, then apply learned gain.

\begin{aligned}m_2(x)&=\frac{1}{d}\sum_{j=1}^d x_j^2\\y&=\gamma_{KV}\odot\frac{x}{\sqrt{m_2(x)+\varepsilon}}\\\varepsilon&=10^{-6}\end{aligned}

in: $c^{KV}$ [B batch, 1 positions, 512 kv latent]
out: $\hat c^{KV}$ [B batch, 1 positions, 512 kv latent]
parameters: 512 · × 60 MoE blocks = 30,720

Normalized in float32, cast back to the input type, then multiplied by the gain (modeling_deepseek.py 104–108).

Parameters

512 parameters per instance.

Exact per-instance accounting
Component	Calculation	Parameters
Learned gain	$512$	512
Per-instance total		512

Each normalization instance owns its learned gain vector.

Across 60 independent MoE blocks: $512 \times 60 = 30{,}720$ parameters.

Derived from the configured feature width; the model total matches the checkpoint’s tensors.

Codemodeling_deepseek.py 94–789

modeling_deepseek.py 94–111

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)

modeling_deepseek.py 662–688

            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()

modeling_deepseek.py 776–789

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        compressed_kv, k_pe = torch.split(
            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
        )
        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
        kv = (
            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
            .transpose(1, 2)
        )

        k_nope, value_states = torch.split(
            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
        )

Sources3

Direct evidence

RMSNorm owns one gain per feature and computes in float32.

rmsnorm · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 94–111

Read code excerpt

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)

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 · 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()

Direct evidence

The joint down-projection yields the key–value latent and one rotary key shared by all heads; the latent is normalized and up-projected into per-head content keys and values.

mla-kv · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 776–789

Read code excerpt

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        compressed_kv, k_pe = torch.split(
            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
        )
        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
        kv = (
            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
            .transpose(1, 2)
        )

        k_nope, value_states = torch.split(
            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
        )