Kimi K3 · Key–value up-projection

93 layers › 24 MLA layers › Gated MLA › Key and value heads

Key–value up-projection

learnedself.kv_b_proj(…) · 430

Expand the latent to 96 heads of 128 key and 128 value features.

[\,{\htmlClass{vl-stream-k}{k^{C}}}\;{\htmlClass{vl-stream-v}{v}}\,]={\htmlData{light=kv-norm}{\hat c^{KV}}}W_{UKV}

in: $\hat c^{KV}$ [B batch, T positions, 512 kv latent]
out: $\mathrm{kv}$ [B batch, T positions, 24576 heads × 256]
parameters: 12,582,912 · × 24 MLA layers = 301,989,888

Parameters

12,582,912 parameters per instance.

Exact per-instance accounting
Component	Calculation	Parameters
Weight matrix	$512 \times 24{,}576$	12,582,912
Per-instance total		12,582,912

This projection has no bias parameter.

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

Across 24 independent MLA layers: $12{,}582{,}912 \times 24 = 301{,}989{,}888$ parameters.

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

Codemodeling_kimi_linear.py 340–403, 405–444

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 405–444

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
        batch_size, seq_length = hidden_states.shape[:-1]
        query_shape = (batch_size, seq_length, -1, self.q_head_dim)
        key_shape = (batch_size, seq_length, -1,
                     self.qk_nope_head_dim + self.v_head_dim)

        if self.q_lora_rank is not None:
            q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
        else:
            q_states = self.q_proj(hidden_states)
        q_states = q_states.view(query_shape).transpose(1, 2)
        q_pass, q_rot = torch.split(
            q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        k_pass, k_rot = torch.split(
            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

        k_pass = self.kv_b_proj(self.kv_a_layernorm(
            k_pass)).view(key_shape).transpose(1, 2)
        k_pass, value_states = torch.split(
            k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)

        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)

        query_states = torch.cat((q_pass, q_rot), dim=-1)
        key_states = torch.cat((k_pass, k_rot), dim=-1)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(
                key_states, value_states, self.layer_idx)

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

Queries come through a low-rank latent; the joint down-projection yields the key–value latent and one 64-feature key part shared by all heads; neither part is rotated; the latent is normalized and up-projected into per-head keys and values; keys and values are handed to the cache; position_ids is not used.

mla-forward · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 405–444

Read code excerpt

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
        batch_size, seq_length = hidden_states.shape[:-1]
        query_shape = (batch_size, seq_length, -1, self.q_head_dim)
        key_shape = (batch_size, seq_length, -1,
                     self.qk_nope_head_dim + self.v_head_dim)

        if self.q_lora_rank is not None:
            q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
        else:
            q_states = self.q_proj(hidden_states)
        q_states = q_states.view(query_shape).transpose(1, 2)
        q_pass, q_rot = torch.split(
            q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        k_pass, k_rot = torch.split(
            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

        k_pass = self.kv_b_proj(self.kv_a_layernorm(
            k_pass)).view(key_shape).transpose(1, 2)
        k_pass, value_states = torch.split(
            k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)

        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)

        query_states = torch.cat((q_pass, q_rot), dim=-1)
        key_states = torch.cat((k_pass, k_rot), dim=-1)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(
                key_states, value_states, self.layer_idx)