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# -*- coding: utf-8 -*-
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING, Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from fla.modules import RMSNorm, ShortConvolution
from fla.modules.feature_map import ReLUFeatureMap, SwishFeatureMap, T2RFeatureMap
from fla.modules.layernorm import rms_norm_linear
from fla.ops.gsa import chunk_gsa, fused_recurrent_gsa
if TYPE_CHECKING:
from transformers.processing_utils import Unpack
from fla.models.utils import Cache
class GatedSlotAttention(nn.Module):
def __init__(
self,
mode: str = 'chunk',
hidden_size: int = 1024,
expand_k: float = 1.,
expand_v: float = 1.,
num_heads: int = 4,
num_kv_heads: Optional[int] = None,
use_short_conv: bool = False,
conv_size: int = 4,
conv_bias: bool = False,
num_slots: Optional[int] = None,
elementwise_affine: Optional[bool] = True,
norm_eps: float = 1e-5,
gate_logit_normalizer: int = 8,
feature_map: str = 'swish',
use_output_gate: bool = False,
use_norm: bool = True,
layer_idx: Optional[int] = None,
scale: Optional[float] = 1.,
**kwargs
) -> GatedSlotAttention:
super().__init__()
self.mode = mode
self.hidden_size = hidden_size
self.expand_k = expand_k
self.expand_v = expand_v
self.num_heads = num_heads
self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
self.num_kv_groups = self.num_heads // self.num_kv_heads
self.key_dim = int(hidden_size * expand_k)
self.value_dim = int(hidden_size * expand_v)
self.key_dim_per_group = self.key_dim // self.num_kv_groups
self.value_dim_per_group = self.value_dim // self.num_kv_groups
self.head_k_dim = self.key_dim // self.num_heads
self.head_v_dim = self.value_dim // self.num_heads
self.use_short_conv = use_short_conv
self.conv_size = conv_size
self.conv_bias = conv_bias
self.gate_logit_normalizer = gate_logit_normalizer
self.use_output_gate = use_output_gate
self.use_norm = use_norm
self.scale = scale
if num_slots is None:
num_slots = self.head_k_dim
self.num_slots = num_slots
self.layer_idx = layer_idx
if layer_idx is None:
warnings.warn(
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.register_module('feature_map', None)
if feature_map == 'swish':
self.feature_map = SwishFeatureMap()
elif feature_map == 'relu':
self.feature_map = ReLUFeatureMap()
elif feature_map == 't2r':
self.feature_map = T2RFeatureMap(self.head_k_dim, self.head_k_dim)
else:
raise NotImplementedError(f"Feature map `{feature_map}` is not supported now.")
self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.key_dim_per_group, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.value_dim_per_group, bias=False)
self.f_proj = nn.Linear(self.hidden_size, self.num_kv_heads * self.num_slots, bias=False)
if use_short_conv:
self.conv_size = conv_size
self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu')
self.k_conv1d = ShortConvolution(self.key_dim_per_group, conv_size, activation='silu')
self.v_conv1d = ShortConvolution(self.value_dim_per_group, conv_size, activation='silu')
self.g_norm = RMSNorm(self.hidden_size, elementwise_affine, eps=norm_eps)
self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Cache] = None,
use_cache: Optional[bool] = False,
output_attentions: Optional[bool] = False,
**kwargs: Unpack[Dict]
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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."
)
# launching the triton kernel for just one token will actually be slower
mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode
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', None)
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']
conv_mask = attention_mask[:, -hidden_states.shape[1]:] if attention_mask is not None else None
q, conv_state_q = self.q_conv1d(
x=self.q_proj(hidden_states),
mask=conv_mask,
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),
mask=conv_mask,
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),
mask=conv_mask,
cache=conv_state_v,
output_final_state=use_cache,
cu_seqlens=cu_seqlens
)
else:
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
f = self.f_proj(hidden_states)
q = rearrange(q, 'b t (h d) -> b t h d', d=self.head_k_dim)
k = rearrange(k, 'b t (h d) -> b t h d', d=self.head_k_dim)
v = rearrange(v, 'b t (h d) -> b t h d', d=self.head_v_dim)
f = rearrange(f, 'b t (h m) -> b t h m', m=self.num_slots)
if self.feature_map is not None:
q, k = map(lambda x: self.feature_map(x), (q, k))
v = F.silu(v)
f = F.logsigmoid(f) / self.gate_logit_normalizer
s = (1 - f.exp()).to(f.dtype)
# dealing with left-padding
if attention_mask is not None:
s = s.mul_(attention_mask[:, -s.shape[1]:, None, None])
v = v.mul_(attention_mask[:, -v.shape[1]:, None, None])
recurrent_state = last_state['recurrent_state'] if last_state is not None else None
if mode == 'fused_recurrent':
o, recurrent_state = fused_recurrent_gsa(
q=q,
k=k,
v=v,
s=s,
g=f,
initial_state=recurrent_state,
output_final_state=use_cache,
scale=self.scale,
cu_seqlens=cu_seqlens,
head_first=False
)
elif mode == 'chunk':
o, recurrent_state = chunk_gsa(
q=q,
k=k,
v=v,
s=s,
g=f,
initial_state=recurrent_state,
output_final_state=use_cache,
scale=self.scale,
cu_seqlens=cu_seqlens,
head_first=False
)
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.shape[1]
)
o = rearrange(o, 'b t h d -> b t (h d)')
o = rms_norm_linear(F.silu(o), self.g_norm.weight, self.g_norm.bias, self.o_proj.weight, self.o_proj.bias)
return o, None, past_key_values
def state_size(self, *args, **kwargs) -> int:
return 2 * self.num_slots * self.hidden_size
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