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from __future__ import annotations
import math
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from einops import rearrange, repeat
from torch.nn import functional as F
from fla.layers.utils import get_unpad_data, index_first_axis, pad_input
from fla.modules import FusedRMSNormGated, ShortConvolution
from fla.ops.kda import chunk_kda, fused_recurrent_kda
from fla.ops.kda.gate import fused_kda_gate
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
class KimiDeltaAttention(nn.Module):
"""
Kimi Delta Attention (KDA) layer implementation.
Args:
hidden_size (int, Optional):
The hidden size of the input. Default: 2048.
expand_v (float, Optional):
The expansion ratio for the value dimension. Default: 1.0.
head_dim (int, Optional):
The dimension of each head. Default: 128.
num_heads (int, Optional):
The number of heads. Default: 16.
num_v_heads (int, Optional):
The number of heads for the value projection, equal to `num_heads` if `None`.
GVA (Grouped Value Attention) is applied if `num_v_heads` > `num_heads`. Default: `None`.
mode (str, Optional):
Which Kimi Delta Attention kernel to use.
Currently available: `chunk` and `fused_recurrent`.
Default: `chunk`.
use_short_conv (bool, Optional):
Whether to use short convolutions. Default: `True`.
allow_neg_eigval (bool, Optional):
Allow negative eigenvalues. Default: `False`. If set to `True`, the beta will be multiplied by 2.
See reference:
[Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues](https://arxiv.org/abs/2411.12537)
conv_size (int, Optional):
The kernel size of the short convolution, only used when `use_short_conv` is `True`. Default: 4.
conv_bias (bool, Optional):
Whether to use bias in the short convolution, only used when `use_short_conv` is `True`. Default: `False`.
layer_idx (int, Optional):
The index of the layer. Default: None.
norm_eps (float, Optional):
The epsilon value for the normalization layer. Default: 1e-5.
"""
def __init__(
self,
hidden_size: int = 2048,
expand_v: float = 1,
head_dim: int = 128,
num_heads: int = 16,
num_v_heads: int = None,
mode: str = 'chunk',
use_short_conv: bool = True,
allow_neg_eigval: bool = False,
conv_size: int = 4,
conv_bias: bool = False,
layer_idx: int = None,
norm_eps: float = 1e-5,
**kwargs,
) -> KimiDeltaAttention:
super().__init__()
self.mode = mode
self.allow_neg_eigval = allow_neg_eigval
self.hidden_size = hidden_size
self.expand_v = expand_v
self.use_short_conv = use_short_conv
self.conv_size = conv_size
self.conv_bias = conv_bias
self.head_dim = head_dim
self.num_heads = num_heads
self.num_v_heads = num_v_heads if num_v_heads is not None else num_heads
self.head_k_dim = head_dim
self.head_v_dim = int(self.head_dim * self.expand_v)
self.key_dim = int(self.num_heads * self.head_k_dim)
self.value_dim = int(self.num_v_heads * self.head_v_dim)
self.layer_idx = layer_idx
# Consistency check: Ensure expand_v produces integer values
if not math.isclose(self.num_v_heads * self.head_dim * expand_v, self.value_dim, rel_tol=1e-5):
raise ValueError(
f"expand_v={expand_v} does not produce an integer value when multiplied by key_dim={self.key_dim}. "
f"Resulting value_dim would be {self.num_v_heads * self.head_dim * expand_v}, which is invalid for nn.Linear.",
)
if self.num_v_heads > self.num_heads and self.num_v_heads % self.num_heads != 0:
raise ValueError(
f"num_v_heads={self.num_v_heads} must be divisible by num_heads={self.num_heads}.",
)
if not math.isclose(head_dim * expand_v, self.head_v_dim, rel_tol=1e-5):
raise ValueError(
f"expand_v={expand_v} does not produce an integer value when multiplied by head_dim={head_dim}. "
f"Resulting head_v_dim would be {head_dim * expand_v}, which is invalid for FusedRMSNormGated.",
)
assert mode in ['chunk', 'fused_recurrent'], f"Not supported mode `{mode}`."
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 = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
self.k_conv1d = ShortConvolution(
hidden_size=self.key_dim,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
self.v_conv1d = ShortConvolution(
hidden_size=self.value_dim,
kernel_size=conv_size,
bias=conv_bias,
activation='silu',
)
self.f_proj = nn.Sequential(
nn.Linear(hidden_size, self.head_v_dim, bias=False),
nn.Linear(self.head_v_dim, self.key_dim, bias=False),
)
self.b_proj = nn.Linear(hidden_size, self.num_heads, bias=False)
self.A_log = nn.Parameter(torch.log(torch.empty(self.num_heads, dtype=torch.float32).uniform_(1, 16)))
self.A_log._no_weight_decay = True
self.dt_bias = nn.Parameter(torch.zeros(self.key_dim, dtype=torch.float32))
self.dt_bias._no_weight_decay = True
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 = FusedRMSNormGated(self.head_v_dim, activation='sigmoid', eps=norm_eps)
self.o_proj = nn.Linear(self.value_dim, hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor | None = None,
past_key_values: Cache | None = None,
use_cache: bool | None = False,
output_attentions: bool | None = False,
**kwargs: Unpack[dict],
) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]:
if attention_mask is not None:
assert len(attention_mask.shape) == 2, (
"Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
"for padding purposes (0 indicating padding). "
"Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
)
batch_size, q_len, _ = hidden_states.shape
# change to inference mode.
mode = 'fused_recurrent' if q_len <= 64 else self.mode
if self.training:
assert mode == 'chunk', "Only chunk mode is supported in training."
last_state = None
if past_key_values is not None and len(past_key_values) > self.layer_idx:
last_state = past_key_values[self.layer_idx]
cu_seqlens = kwargs.get('cu_seqlens')
if attention_mask is not None:
indices, cu_seqlens, _ = get_unpad_data(attention_mask[:, -q_len:])
hidden_states = index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices).unsqueeze(0)
if self.use_short_conv:
conv_state_q, conv_state_k, conv_state_v = None, None, None
if last_state is not None:
conv_state_q, conv_state_k, conv_state_v = last_state['conv_state']
q, conv_state_q = self.q_conv1d(
x=self.q_proj(hidden_states),
cache=conv_state_q,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
k, conv_state_k = self.k_conv1d(
x=self.k_proj(hidden_states),
cache=conv_state_k,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
v, conv_state_v = self.v_conv1d(
x=self.v_proj(hidden_states),
cache=conv_state_v,
output_final_state=use_cache,
cu_seqlens=cu_seqlens,
)
else:
q = F.silu(self.q_proj(hidden_states))
k = F.silu(self.k_proj(hidden_states))
v = F.silu(self.v_proj(hidden_states))
g = self.f_proj(hidden_states)
g = fused_kda_gate(g, self.A_log, self.head_k_dim, g_bias=self.dt_bias)
beta = self.b_proj(hidden_states).sigmoid()
q, k = (rearrange(x, '... (h d) -> ... h d', d=self.head_k_dim) for x in (q, k))
v = rearrange(v, '... (h d) -> ... h d', d=self.head_v_dim)
# for multi-value attention, we repeat the inputs for simplicity.
if self.num_v_heads > self.num_heads:
q, k, g = (repeat(x, '... h d -> ... (h g) d', g=self.num_v_heads // self.num_heads) for x in (q, k, g))
beta = repeat(beta, '... h -> ... (h g)', g=self.num_v_heads // self.num_heads)
if self.allow_neg_eigval:
beta = beta * 2.
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
if mode == 'chunk':
o, recurrent_state = chunk_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
use_qk_l2norm_in_kernel=True,
cu_seqlens=cu_seqlens,
)
elif mode == 'fused_recurrent':
o, recurrent_state = fused_recurrent_kda(
q=q,
k=k,
v=v,
g=g,
beta=beta,
initial_state=recurrent_state,
output_final_state=use_cache,
use_qk_l2norm_in_kernel=True,
cu_seqlens=cu_seqlens,
)
else:
raise NotImplementedError(f"Not supported mode `{mode}`.")
if past_key_values is not None:
past_key_values.update(
recurrent_state=recurrent_state,
conv_state=(conv_state_q, conv_state_k, conv_state_v) if self.use_short_conv else None,
layer_idx=self.layer_idx,
offset=q_len,
)
o = self.o_norm(o, rearrange(self.g_proj(hidden_states), '... (h d) -> ... h d', d=self.head_v_dim))
o = rearrange(o, 'b t h d -> b t (h d)')
o = self.o_proj(o)
if attention_mask is not None:
o = pad_input(o.squeeze(0), indices, batch_size, q_len)
return o, None, past_key_values
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