Kimi K3 · Output projection

93 layers › 24 MLA layers › Gated MLA

Output projection

learnedself.o_proj(attn_output)

Map the gated features back to the model width.

{\htmlData{light=update output-projection}{\Delta_{\mathrm{attn}}}}={\htmlData{light=gate}{o}}W_{O}

in: $o$ [B batch, T positions, 12288 heads × value d_h]
out: $\Delta_{\mathrm{attn}}$ [B batch, T positions, 7168 d]
parameters: 88,080,384 · × 24 MLA layers = 2,113,929,216

Parameters

88,080,384 parameters per instance.

Exact per-instance accounting
Component	Calculation	Parameters
Weight matrix	$12{,}288 \times 7{,}168$	88,080,384
Per-instance total		88,080,384

This projection has no bias parameter.

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

Across 24 independent MLA layers: $88{,}080{,}384 \times 24 = 2{,}113{,}929{,}216$ parameters.

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

Codemodeling_kimi_linear.py 340–403, 468–474

modeling_kimi_linear.py 340–403

    def __init__(self, config: KimiLinearConfig, layer_idx: int):
        nn.Module.__init__(self)
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads

        self.attention_dropout = getattr(config, "attention_dropout", 0.0)

        try:
            self.q_lora_rank = config.q_lora_rank
            self.qk_rope_head_dim = config.qk_rope_head_dim
            self.kv_lora_rank = config.kv_lora_rank
            self.v_head_dim = config.v_head_dim
            self.qk_nope_head_dim = config.qk_nope_head_dim
            self.q_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
            self.use_nope = config.mla_use_nope
            self.scaling = self.q_head_dim ** (-0.5)
        except Exception as e:
            raise ValueError(
                f"Kimi MLA config is not found or not properly formatted: {e}")

        if self.q_lora_rank is not None:
            self.q_a_proj = nn.Linear(
                self.hidden_size, self.q_lora_rank, bias=False,
            )
            self.q_a_layernorm = KimiRMSNorm(self.q_lora_rank)
            self.q_b_proj = nn.Linear(
                self.q_lora_rank,
                self.num_heads * self.q_head_dim,
                bias=False,
            )
        else:
            self.q_proj = nn.Linear(
                self.hidden_size, self.num_heads * self.q_head_dim, bias=False,
            )
        self.kv_a_proj_with_mqa = nn.Linear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
        )
        self.kv_a_layernorm = KimiRMSNorm(self.kv_lora_rank)
        self.kv_b_proj = nn.Linear(
            self.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=False,
        )
        self.is_causal = True
        assert self.use_nope

        self.use_output_gate = getattr(config, "mla_use_output_gate", False)
        if self.use_output_gate:
            projection_size = self.num_heads * self.v_head_dim
            self.g_proj = nn.Linear(self.hidden_size, projection_size, bias=False)

        self.rotary_emb = None

modeling_kimi_linear.py 468–474

        attn_output = attn_output.reshape(
            batch_size, seq_length, -1).contiguous()
        if self.use_output_gate:
            g = self.g_proj(hidden_states).sigmoid()
            attn_output = attn_output * g
        attn_output = self.o_proj(attn_output)
        return attn_output

Sources2

Direct evidence

Latent attention owns a query down-projection, query RMSNorm and up-projection, a joint key–value down-projection that also emits 64 unrotated shared key features, a latent RMSNorm, a key–value up-projection, an output projection and a full-rank output gate projection; it creates no rotary embedding.

mla-init · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 340–403

Read code excerpt

    def __init__(self, config: KimiLinearConfig, layer_idx: int):
        nn.Module.__init__(self)
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads

        self.attention_dropout = getattr(config, "attention_dropout", 0.0)

        try:
            self.q_lora_rank = config.q_lora_rank
            self.qk_rope_head_dim = config.qk_rope_head_dim
            self.kv_lora_rank = config.kv_lora_rank
            self.v_head_dim = config.v_head_dim
            self.qk_nope_head_dim = config.qk_nope_head_dim
            self.q_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
            self.use_nope = config.mla_use_nope
            self.scaling = self.q_head_dim ** (-0.5)
        except Exception as e:
            raise ValueError(
                f"Kimi MLA config is not found or not properly formatted: {e}")

        if self.q_lora_rank is not None:
            self.q_a_proj = nn.Linear(
                self.hidden_size, self.q_lora_rank, bias=False,
            )
            self.q_a_layernorm = KimiRMSNorm(self.q_lora_rank)
            self.q_b_proj = nn.Linear(
                self.q_lora_rank,
                self.num_heads * self.q_head_dim,
                bias=False,
            )
        else:
            self.q_proj = nn.Linear(
                self.hidden_size, self.num_heads * self.q_head_dim, bias=False,
            )
        self.kv_a_proj_with_mqa = nn.Linear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
        )
        self.kv_a_layernorm = KimiRMSNorm(self.kv_lora_rank)
        self.kv_b_proj = nn.Linear(
            self.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=False,
        )
        self.is_causal = True
        assert self.use_nope

        self.use_output_gate = getattr(config, "mla_use_output_gate", False)
        if self.use_output_gate:
            projection_size = self.num_heads * self.v_head_dim
            self.g_proj = nn.Linear(self.hidden_size, projection_size, bias=False)

        self.rotary_emb = None

Direct evidence

Head outputs are flattened, multiplied by the sigmoid of a full-rank projection of the layer’s input, and projected back to the model width.

mla-output · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 468–474

Read code excerpt

        attn_output = attn_output.reshape(
            batch_size, seq_length, -1).contiguous()
        if self.use_output_gate:
            g = self.g_proj(hidden_states).sigmoid()
            attn_output = attn_output * g
        attn_output = self.o_proj(attn_output)
        return attn_output