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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def causal_depthwise_conv1d(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor | None = None,
) -> torch.Tensor:
"""Torch reference for FLA's causal depthwise short convolution."""
if x.ndim != 3 or weight.ndim != 3 or weight.shape[1] != 1:
raise ValueError("expected x=[B,T,D] and depthwise weight=[D,1,W]")
sequence = x.shape[1]
channels = x.shape[2]
if weight.shape[0] != channels:
raise ValueError("convolution channels do not match the hidden projection")
convolved = F.conv1d(
x.transpose(1, 2),
weight,
bias,
padding=weight.shape[-1] - 1,
groups=channels,
)
return convolved[:, :, :sequence].transpose(1, 2)
def l2_normalize_sum(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
"""Match FLA's L2 normalization (sum, not mean, over head dim)."""
normalized = x.float() * torch.rsqrt(x.float().square().sum(dim=-1, keepdim=True) + eps)
return normalized.to(x.dtype)
def recurrent_kda_reference(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
log_decay: torch.Tensor,
beta: torch.Tensor,
*,
scale: float | None = None,
initial_state: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Differentiable token-loop KDA oracle following the published delta rule."""
if q.ndim != 4 or k.shape != q.shape or v.ndim != 4:
raise ValueError("q/k/v must be [B,T,H,D] tensors")
if v.shape[:3] != q.shape[:3] or log_decay.shape != q.shape:
raise ValueError("KDA q/k/v/gate dimensions must match")
if beta.shape != q.shape[:3]:
raise ValueError("KDA beta must be [B,T,H]")
batch, sequence, heads, key_dim = q.shape
value_dim = v.shape[-1]
state = q.new_zeros(batch, heads, key_dim, value_dim, dtype=torch.float32)
if initial_state is not None:
if initial_state.shape != state.shape:
raise ValueError("KDA initial state has the wrong shape")
state = state + initial_state.float()
attention_scale = key_dim**-0.5 if scale is None else scale
qf, kf, vf = q.float(), k.float(), v.float()
gf, bf = log_decay.float(), beta.float()
outputs: list[torch.Tensor] = []
for token in range(sequence):
key = kf[:, token]
state = state * gf[:, token].exp().unsqueeze(-1)
prediction = torch.einsum("bhk,bhkv->bhv", key, state)
residual = vf[:, token] - prediction
state = state + torch.einsum(
"bhk,bhv->bhkv", bf[:, token].unsqueeze(-1) * key, residual
)
outputs.append(
torch.einsum("bhk,bhkv->bhv", qf[:, token] * attention_scale, state)
)
return torch.stack(outputs, dim=1).to(v.dtype), state
class ReferenceRMSNormGated(nn.Module):
def __init__(self, hidden_size: int, eps: float) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.eps = eps
def forward(self, x: torch.Tensor, gate: torch.Tensor) -> torch.Tensor:
normalized = x.float() * torch.rsqrt(
x.float().square().mean(dim=-1, keepdim=True) + self.eps
)
return (
normalized
* self.weight.float()
* torch.sigmoid(gate.float())
).to(x.dtype)
class ReferenceKimiDeltaAttention(nn.Module):
"""Correctness-first KDA layer for CPU/MPS probes.
Parameter names and shapes intentionally match FLA's
``KimiDeltaAttention`` so a CUDA checkpoint can be loaded strictly. CUDA
production must continue to use FLA; this token loop is an oracle, not an
efficiency backend.
"""
def __init__(
self,
*,
hidden_size: int,
head_dim: int,
num_heads: int,
expand_v: float = 1.0,
use_short_conv: bool = True,
conv_size: int = 4,
conv_bias: bool = False,
layer_idx: int | None = None,
norm_eps: float = 1e-5,
safe_gate: bool = False,
lower_bound: float | None = None,
**_: object,
) -> None:
super().__init__()
self.hidden_size = hidden_size
self.head_dim = head_dim
self.num_heads = num_heads
self.head_v_dim = int(head_dim * expand_v)
self.key_dim = num_heads * head_dim
self.value_dim = num_heads * self.head_v_dim
self.gate_dim = num_heads * head_dim
self.use_short_conv = use_short_conv
self.layer_idx = layer_idx
self.safe_gate = safe_gate
self.lower_bound = lower_bound
if safe_gate and (lower_bound is None or not -5.0 <= lower_bound < 0.0):
raise ValueError("safe KDA gate requires lower_bound in [-5, 0)")
if self.head_v_dim != head_dim:
raise ValueError("reference KDA currently requires expand_v=1")
self.q_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.k_proj = nn.Linear(hidden_size, self.key_dim, bias=False)
self.v_proj = nn.Linear(hidden_size, self.value_dim, bias=False)
if use_short_conv:
self.q_conv1d = nn.Conv1d(
self.key_dim, self.key_dim, conv_size, groups=self.key_dim, bias=conv_bias
)
self.k_conv1d = nn.Conv1d(
self.key_dim, self.key_dim, conv_size, groups=self.key_dim, bias=conv_bias
)
self.v_conv1d = nn.Conv1d(
self.value_dim,
self.value_dim,
conv_size,
groups=self.value_dim,
bias=conv_bias,
)
self.f_proj = nn.Sequential(
nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.gate_dim, bias=False),
)
self.b_proj = nn.Linear(hidden_size, num_heads, bias=False)
self.A_log = nn.Parameter(
torch.zeros(num_heads)
if safe_gate
else torch.log(torch.empty(num_heads).uniform_(1, 16))
)
self.A_log._no_weight_decay = True # type: ignore[attr-defined]
dt = torch.exp(
torch.rand(self.gate_dim) * (math.log(0.1) - math.log(0.001))
+ math.log(0.001)
).clamp(min=1e-4)
self.dt_bias = nn.Parameter(dt + torch.log(-torch.expm1(-dt)))
self.dt_bias._no_weight_decay = True # type: ignore[attr-defined]
self.g_proj = nn.Sequential(
nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.value_dim, bias=True),
)
self.o_norm = ReferenceRMSNormGated(self.head_v_dim, norm_eps)
self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)
def _project(self, projection: nn.Linear, convolution: nn.Conv1d, x: torch.Tensor) -> torch.Tensor:
return F.silu(causal_depthwise_conv1d(projection(x), convolution.weight, convolution.bias))
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor | None = None,
past_key_values: object | None = None,
use_cache: bool = False,
output_attentions: bool = False,
cu_seqlens: torch.Tensor | None = None,
**_: object,
) -> tuple[torch.Tensor, None, None]:
if attention_mask is not None or past_key_values is not None or use_cache:
raise NotImplementedError("reference KDA currently supports training without cache/padding")
if output_attentions:
raise NotImplementedError("KDA does not materialize attention weights")
if cu_seqlens is not None:
if hidden_states.shape[0] != 1:
raise ValueError("packed reference KDA requires batch size one")
boundaries = [int(value) for value in cu_seqlens.detach().cpu().tolist()]
pieces = [
self.forward(hidden_states[:, start:end])[0]
for start, end in zip(boundaries[:-1], boundaries[1:], strict=True)
if end > start
]
return torch.cat(pieces, dim=1), None, None
if self.use_short_conv:
q = self._project(self.q_proj, self.q_conv1d, hidden_states)
k = self._project(self.k_proj, self.k_conv1d, hidden_states)
v = self._project(self.v_proj, self.v_conv1d, hidden_states)
else:
q, k, v = (
F.silu(projection(hidden_states))
for projection in (self.q_proj, self.k_proj, self.v_proj)
)
batch, sequence, _ = hidden_states.shape
shape = (batch, sequence, self.num_heads, self.head_dim)
q = l2_normalize_sum(q.view(shape))
k = l2_normalize_sum(k.view(shape))
v = v.view(batch, sequence, self.num_heads, self.head_v_dim)
raw_gate = self.f_proj(hidden_states).view(shape)
gate_input = raw_gate.float() + self.dt_bias.float().view(
1, 1, self.num_heads, self.head_dim
)
if self.safe_gate:
log_decay = self.lower_bound * torch.sigmoid(
self.A_log.float().exp().view(1, 1, self.num_heads, 1)
* gate_input
)
else:
log_decay = -self.A_log.float().exp().view(
1, 1, self.num_heads, 1
) * F.softplus(gate_input)
beta = torch.sigmoid(self.b_proj(hidden_states))
output, _ = recurrent_kda_reference(q, k, v, log_decay, beta)
gate = self.g_proj(hidden_states).view(
batch, sequence, self.num_heads, self.head_v_dim
)
output = self.o_norm(output, gate).reshape(batch, sequence, self.value_dim)
return self.o_proj(output), None, None
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