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32beb88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | # -*- coding: utf-8 -*-
from __future__ import annotations
import warnings
from typing import Optional, Tuple
import torch
import torch.nn as nn
from einops import rearrange
from transformers.cache_utils import Cache
from fla.modules import (FusedRMSNormSwishGate, RMSNorm, RotaryEmbedding,
ShortConvolution)
from fla.modules.activations import swiglu, swish
from fla.modules.convolution import proj_then_conv1d
from fla.ops.abc.chunk import chunk_abc
class ABCAttention(nn.Module):
def __init__(
self,
hidden_size: int = 1024,
expand_k: float = 0.5,
expand_v: float = 1.0,
num_heads: int = 4,
use_short_conv: bool = False,
conv_size: int = 4,
conv_bias: bool = False,
share_conv_kernel: bool = True,
num_slots: Optional[int] = None,
elementwise_affine: Optional[bool] = True,
norm_eps: float = 1e-5,
gate_low_rank_dim: int = 16,
gate_logit_normalizer: int = 16,
use_input_gate: bool = False,
use_output_gate: bool = True,
use_norm: bool = True,
clamp_min: Optional[float] = -32,
clamp_max: Optional[float] = 32,
layer_idx: Optional[int] = None,
**kwargs
) -> ABCAttention:
super().__init__()
self.hidden_size = hidden_size
self.expand_k = expand_k
self.expand_v = expand_v
self.num_heads = num_heads
self.key_dim = int(self.hidden_size * self.expand_k)
self.value_dim = int(self.hidden_size * self.expand_v)
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.share_conv_kernel = share_conv_kernel
self.gate_low_rank_dim = gate_low_rank_dim
self.gate_logit_normalizer = gate_logit_normalizer
self.use_input_gate = use_input_gate
self.use_output_gate = use_output_gate
self.use_norm = use_norm
if num_slots is None:
num_slots = self.head_k_dim
self.num_slots = num_slots
self.norm_eps = norm_eps
self.clamp_min = clamp_min
self.clamp_max = clamp_max
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.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False)
if use_output_gate:
self.g_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False)
self.s_proj = nn.Linear(self.hidden_size, self.num_heads * self.num_slots, bias=False)
self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
if use_short_conv:
self.conv_size = conv_size
if share_conv_kernel:
self.h_conv1d = ShortConvolution(hidden_size, conv_size, activation='silu')
else:
self.q_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu')
self.k_conv1d = ShortConvolution(self.key_dim, conv_size, activation='silu')
self.v_conv1d = ShortConvolution(self.value_dim, conv_size, activation='silu')
if self.use_norm:
if self.use_output_gate:
self.g_norm = FusedRMSNormSwishGate(self.head_v_dim, elementwise_affine, norm_eps)
else:
self.g_norm = RMSNorm(self.head_v_dim, elementwise_affine, norm_eps)
if self.use_rope:
self.rotary = RotaryEmbedding(self.head_k_dim)
self.apply(self._initialize_weights)
def _initialize_weights(self, module: nn.Module):
if getattr(module, "_is_hf_initialized", False):
return
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight, gain=2 ** -2.5)
if module.bias is not None:
nn.init.zeros_(module.bias)
module._is_hf_initialized = True
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
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
if self.use_short_conv:
if self.share_conv_kernel:
hidden_states = self.h_conv1d(hidden_states)
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
else:
q = proj_then_conv1d(hidden_states, self.q_proj.weight, self.q_conv1d.weight, self.q_conv1d.bias)
k = proj_then_conv1d(hidden_states, self.k_proj.weight, self.k_conv1d.weight, self.k_conv1d.bias)
v = proj_then_conv1d(hidden_states, self.v_proj.weight, self.v_conv1d.weight, self.v_conv1d.bias)
else:
q = self.q_proj(hidden_states)
k = self.k_proj(hidden_states)
v = self.v_proj(hidden_states)
if self.use_input_gate:
q, k, v = map(lambda x: swish(x), (q, k, v))
if self.use_rope:
q = rearrange(q, '... (h d) -> ... h d', h=self.num_heads)
k = rearrange(k, '... (h d) -> ... h d', h=self.num_heads)
seqlen_offset = 0
if past_key_values is not None:
seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
q, k = self.rotary(q, k, seqlen_offset)
q = rearrange(q, 'b n h d -> b h n d', h=self.num_heads)
k = rearrange(k, 'b n h d -> b h n d', h=self.num_heads)
else:
q = rearrange(q, 'b n (h d) -> b h n d', h=self.num_heads)
k = rearrange(k, 'b n (h d) -> b h n d', h=self.num_heads)
v = rearrange(v, 'b n (h d) -> b h n d', h=self.num_heads)
# [batch_size, n_heads, seq_len, num_slots]
s = rearrange(self.s_proj(hidden_states), 'b t (h m) -> b h t m', h=self.num_heads)
s = s.clamp_(self.clamp_min, self.clamp_max)
last_state = past_key_values[self.layer_idx] if use_cache else None
o, last_state = chunk_abc(q, k, v, s, initial_state=last_state, output_final_state=use_cache)
if past_key_values is not None and last_state is not None:
past_key_values.update(last_state, self.layer_idx, q.shape[2])
o = rearrange(o, 'b h t d -> b t h d')
if self.use_norm and not self.use_output_gate:
o = self.g_norm(o)
elif self.use_output_gate:
g = rearrange(self.g_proj(hidden_states), 'b t (h d) -> b t h d', h=self.num_heads)
o = self.g_norm(o, g) if self.use_norm else swiglu(g, o)
o = rearrange(o, 'b t h d -> b t (h d)')
o = self.o_proj(o)
return o, None, past_key_values
def init_state(self, batch_size: int) -> Tuple[torch.Tensor]:
param = next(self.parameters())
state = tuple()
if self.use_short_conv:
state += (param.new_zeros(batch_size, self.hidden_size, self.conv_size),)
state += (param.new_zeros(batch_size, self.num_heads, self.head_k_dim, self.num_slots),
param.new_zeros(batch_size, self.num_heads, self.num_slots, self.head_v_dim))
return state
def state_size(self, sequence_length: int = 2048):
return self.num_heads * self.key_dim * self.head_v_dim
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