- in
- [B batch, 96 heads, 1 position, 128 d_h] [B batch, 1 position, 12288 heads × d_h]
- out
- [B batch, 1 position, 7168 d]
- parameters
- 88,080,512
- mode
- decode · one new token after P = 1,000 positions
Parameters
88,080,512 parameters per instance, across its learned components
| Component | Calculation | Parameters |
|---|---|---|
| Gated RMSNorm | 128 | |
| Output projection | 88,080,384 | |
| Per-instance total | 88,080,512 | |
Across 1 independent layers: parameters.
Derived from the pinned configuration and implementation, with the per-head log-scale at the checkpoint’s 128 elements where the code creates 96; equal to the checkpoint’s own tensors. The quantisation scales are not counted.
Explanation
The gate is computed from the layer’s input: one logit for each feature of each head’s read-out.
Codemodeling_kimi_linear.py 478–541, 651–659
if self.use_full_rank_gate:
g = self.g_proj(hidden_states)
else:
g = self.g_b_proj(self.g_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
o = self.o_norm(o, g)
o = rearrange(o, 'b t h d -> b t (h d)')
o = self.o_proj(o) def __init__(self, config: KimiLinearConfig, layer_idx: int):
super().__init__()
self.config = config
self.mode = "chunk"
self.hidden_size = config.hidden_size
self.conv_size = config.linear_attn_config["short_conv_kernel_size"]
self.head_dim = config.linear_attn_config["head_dim"]
self.num_heads = config.linear_attn_config["num_heads"]
self.head_k_dim = self.head_dim
self.num_k_heads = self.num_heads
self.layer_idx = layer_idx
assert self.mode in [
'chunk', 'fused_recurrent'], f"Not supported mode `{self.mode}`."
projection_k_size = self.head_k_dim * self.num_k_heads
projection_size = self.head_dim * self.num_heads
self.q_proj = nn.Linear(
self.hidden_size, projection_k_size, bias=False)
self.k_proj = nn.Linear(
self.hidden_size, projection_k_size, bias=False)
self.v_proj = nn.Linear(self.hidden_size, projection_size, bias=False)
self.q_conv1d = ShortConvolution(
hidden_size=projection_k_size,
kernel_size=self.conv_size,
activation='silu',
)
self.k_conv1d = ShortConvolution(
hidden_size=projection_k_size,
kernel_size=self.conv_size,
activation='silu',
)
self.v_conv1d = ShortConvolution(
hidden_size=projection_size,
kernel_size=self.conv_size,
activation='silu',
)
self.A_log = torch.nn.Parameter(torch.log(torch.empty(
self.num_heads, dtype=torch.float32).uniform_(1, 16)))
self.f_a_proj = nn.Linear(self.hidden_size, self.head_dim, bias=False)
self.f_b_proj = nn.Linear(self.head_dim, projection_size, bias=False)
self.dt_bias = nn.Parameter(
torch.empty(projection_size, dtype=torch.float32))
self.b_proj = nn.Linear(self.hidden_size, self.num_heads, bias=False)
self.use_full_rank_gate = config.linear_attn_config.get("use_full_rank_gate", False)
self.gate_lower_bound = config.linear_attn_config.get("gate_lower_bound", None)
if self.use_full_rank_gate:
self.g_proj = nn.Linear(self.hidden_size, projection_size, bias=False)
else:
self.g_a_proj = nn.Linear(self.hidden_size, self.head_dim, bias=False)
self.g_b_proj = nn.Linear(self.head_dim, projection_size, bias=False)
self.o_norm = FusedRMSNormGated(
self.head_dim, eps=config.rms_norm_eps, activation='sigmoid')
self.o_proj = nn.Linear(projection_size, self.hidden_size, bias=False)Sources2
Direct evidence
The kernel’s output passes a gated RMSNorm, whose gate is the sigmoid of a full-rank projection of the layer’s input, and an output projection.
kda-output · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 651–659
Read code excerpt
if self.use_full_rank_gate:
g = self.g_proj(hidden_states)
else:
g = self.g_b_proj(self.g_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
o = self.o_norm(o, g)
o = rearrange(o, 'b t h d -> b t (h d)')
o = self.o_proj(o)Direct evidence
Kimi Delta Attention owns query, key and value projections to 96 heads of 128 features, a causal short convolution on each, a per-head log-scale A_log, a rank-128 pair of projections and a bias for the decay logits, a per-head write-strength projection, a full-rank output gate projection, a gated RMSNorm over each head and an output projection.
kda-init · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 478–541
Read code excerpt
def __init__(self, config: KimiLinearConfig, layer_idx: int):
super().__init__()
self.config = config
self.mode = "chunk"
self.hidden_size = config.hidden_size
self.conv_size = config.linear_attn_config["short_conv_kernel_size"]
self.head_dim = config.linear_attn_config["head_dim"]
self.num_heads = config.linear_attn_config["num_heads"]
self.head_k_dim = self.head_dim
self.num_k_heads = self.num_heads
self.layer_idx = layer_idx
assert self.mode in [
'chunk', 'fused_recurrent'], f"Not supported mode `{self.mode}`."
projection_k_size = self.head_k_dim * self.num_k_heads
projection_size = self.head_dim * self.num_heads
self.q_proj = nn.Linear(
self.hidden_size, projection_k_size, bias=False)
self.k_proj = nn.Linear(
self.hidden_size, projection_k_size, bias=False)
self.v_proj = nn.Linear(self.hidden_size, projection_size, bias=False)
self.q_conv1d = ShortConvolution(
hidden_size=projection_k_size,
kernel_size=self.conv_size,
activation='silu',
)
self.k_conv1d = ShortConvolution(
hidden_size=projection_k_size,
kernel_size=self.conv_size,
activation='silu',
)
self.v_conv1d = ShortConvolution(
hidden_size=projection_size,
kernel_size=self.conv_size,
activation='silu',
)
self.A_log = torch.nn.Parameter(torch.log(torch.empty(
self.num_heads, dtype=torch.float32).uniform_(1, 16)))
self.f_a_proj = nn.Linear(self.hidden_size, self.head_dim, bias=False)
self.f_b_proj = nn.Linear(self.head_dim, projection_size, bias=False)
self.dt_bias = nn.Parameter(
torch.empty(projection_size, dtype=torch.float32))
self.b_proj = nn.Linear(self.hidden_size, self.num_heads, bias=False)
self.use_full_rank_gate = config.linear_attn_config.get("use_full_rank_gate", False)
self.gate_lower_bound = config.linear_attn_config.get("gate_lower_bound", None)
if self.use_full_rank_gate:
self.g_proj = nn.Linear(self.hidden_size, projection_size, bias=False)
else:
self.g_a_proj = nn.Linear(self.hidden_size, self.head_dim, bias=False)
self.g_b_proj = nn.Linear(self.head_dim, projection_size, bias=False)
self.o_norm = FusedRMSNormGated(
self.head_dim, eps=config.rms_norm_eps, activation='sigmoid')
self.o_proj = nn.Linear(projection_size, self.hidden_size, bias=False)