93 layers › First layer › Kimi Delta Attention (KDA) › Value heads
Split value heads
×no parametersrearrange(x, '... (h d) -> ... h d') · 605–607
Read the 12,288 features as 96 heads of 128.
- in
- [B batch, T positions, 12288 heads × d_h]
- out
- [B batch, 96 heads, T positions, 128 d_h]
Codemodeling_kimi_linear.py 559–561, 572–649
use_cache = cache_params is not None
batch_size, q_len, _ = hidden_states.shape
mode = 'fused_recurrent' if use_cache and q_len == 1 else self.mode conv_state_q, conv_state_k, conv_state_v = None, None, None
recurrent_state = None
if cache_params is not None:
if cache_params.conv_states[self.layer_idx] is not None:
conv_state_q, conv_state_k, conv_state_v = cache_params.conv_states[
self.layer_idx]
recurrent_state = cache_params.recurrent_states[self.layer_idx]
q_proj_states = self.q_proj(hidden_states)
k_proj_states = self.k_proj(hidden_states)
v_proj_states = self.v_proj(hidden_states)
q, conv_state_q = self.q_conv1d(
x=q_proj_states,
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
k, conv_state_k = self.k_conv1d(
x=k_proj_states,
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
v, conv_state_v = self.v_conv1d(
x=v_proj_states,
cache=conv_state_v,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
g = self.f_b_proj(self.f_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
beta = self.b_proj(hidden_states).float()
q, k = map(lambda x: rearrange(
x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
if mode == 'chunk':
o, recurrent_state = chunk_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
A_log=self.A_log,
dt_bias=self.dt_bias,
initial_state=recurrent_state,
output_final_state=True,
use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=True,
use_beta_sigmoid_in_kernel=True,
safe_gate=self.gate_lower_bound is not None,
lower_bound=self.gate_lower_bound,
transpose_state_layout=True,
cu_seqlens=cu_seqlens,
)
else:
o, recurrent_state = fused_recurrent_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
A_log=self.A_log,
dt_bias=self.dt_bias,
initial_state=recurrent_state,
output_final_state=True,
use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=True,
use_beta_sigmoid_in_kernel=True,
lower_bound=self.gate_lower_bound,
transpose_state_layout=True,
cu_seqlens=cu_seqlens,
)
if cache_params is not None:
cache_params.recurrent_states[self.layer_idx] = recurrent_state
cache_params.conv_states[self.layer_idx] = (
conv_state_q, conv_state_k, conv_state_v)Sources1
Direct evidence
Queries, keys and values are projected and convolved; the decay logits and the write strength are projected from the layer’s input; the kernel returns the output and the recurrent state, in the recurrent form for one new position with a cache and chunked otherwise; the state and the convolution states are kept per layer.
kda-forward · moonshotai/Kimi-K3 / modeling_kimi_linear.py
Pinned revision f831ab668142 ↗ · lines 559–649
Read code excerpt
use_cache = cache_params is not None
batch_size, q_len, _ = hidden_states.shape
mode = 'fused_recurrent' if use_cache and q_len == 1 else self.mode… lines 562–571 omitted …
conv_state_q, conv_state_k, conv_state_v = None, None, None
recurrent_state = None
if cache_params is not None:
if cache_params.conv_states[self.layer_idx] is not None:
conv_state_q, conv_state_k, conv_state_v = cache_params.conv_states[
self.layer_idx]
recurrent_state = cache_params.recurrent_states[self.layer_idx]
q_proj_states = self.q_proj(hidden_states)
k_proj_states = self.k_proj(hidden_states)
v_proj_states = self.v_proj(hidden_states)
q, conv_state_q = self.q_conv1d(
x=q_proj_states,
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
k, conv_state_k = self.k_conv1d(
x=k_proj_states,
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
v, conv_state_v = self.v_conv1d(
x=v_proj_states,
cache=conv_state_v,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
g = self.f_b_proj(self.f_a_proj(hidden_states))
g = rearrange(g, '... (h d) -> ... h d', d=self.head_dim)
beta = self.b_proj(hidden_states).float()
q, k = map(lambda x: rearrange(
x, '... (h d) -> ... h d', d=self.head_k_dim), (q, k))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
if mode == 'chunk':
o, recurrent_state = chunk_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
A_log=self.A_log,
dt_bias=self.dt_bias,
initial_state=recurrent_state,
output_final_state=True,
use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=True,
use_beta_sigmoid_in_kernel=True,
safe_gate=self.gate_lower_bound is not None,
lower_bound=self.gate_lower_bound,
transpose_state_layout=True,
cu_seqlens=cu_seqlens,
)
else:
o, recurrent_state = fused_recurrent_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
A_log=self.A_log,
dt_bias=self.dt_bias,
initial_state=recurrent_state,
output_final_state=True,
use_qk_l2norm_in_kernel=True,
use_gate_in_kernel=True,
use_beta_sigmoid_in_kernel=True,
lower_bound=self.gate_lower_bound,
transpose_state_layout=True,
cu_seqlens=cu_seqlens,
)
if cache_params is not None:
cache_params.recurrent_states[self.layer_idx] = recurrent_state
cache_params.conv_states[self.layer_idx] = (
conv_state_q, conv_state_k, conv_state_v)