Kimi K2 · Output projection

61 transformer blocks › 60 MoE blocks › Latent attention › Causal attention › Mix and project

Output projection

learnedself.o_proj(attn_output)

Map 8,192 head features to the 7,168-feature update.

{\htmlData{light=output projection}{\Delta_{\mathrm{attn}}}}={\htmlData{light=merged merge}{o}}W_{O}

in: $o$ [B batch, T positions, 8192 heads × value]
out: $\Delta_{\mathrm{attn}}$ [B batch, T positions, 7168 d]
parameters: 58,720,256 · × 60 MoE blocks = 3,523,215,360

Parameters

58,720,256 parameters per instance.

Exact per-instance accounting
Component	Calculation	Parameters
Weight matrix	$8{,}192 \times 7{,}168$	58,720,256
Per-instance total		58,720,256

This projection has no bias parameter.

The reference module holds the weight output by input, [out, in].

Across 60 independent MoE blocks: $58{,}720{,}256 \times 60 = 3{,}523{,}215{,}360$ parameters.

Derived from the configured matrix dimensions; the model total matches the checkpoint’s tensors.

Codemodeling_deepseek.py 662–688, 848–852

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 848–852

        attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)

        attn_output = self.o_proj(attn_output)

Sources2

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

Head outputs are flattened and projected back to the model width.

mla-output · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 848–852

Read code excerpt

        attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.reshape(bsz, q_len, self.num_heads * self.v_head_dim)

        attn_output = self.o_proj(attn_output)