File size: 6,505 Bytes
eafbe80 | 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 | # Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
import torch
import torch.nn as nn
from einops import rearrange
from transformers.utils import logging
from fla.layers.utils import pad_input, unpad_input
from fla.modules import RotaryEmbedding
from fla.modules.fused_bitlinear import FusedBitLinear
from fla.ops.utils.index import prepare_lens_from_mask
if TYPE_CHECKING:
from fla.models.utils import Cache
try:
from flash_attn import flash_attn_func, flash_attn_varlen_func
except ImportError:
warnings.warn(
"Flash Attention is not installed. Please install it via `pip install flash-attn --no-build-isolation`",
category=ImportWarning,
)
flash_attn_func = None
logger = logging.get_logger(__name__)
class BitAttention(nn.Module):
def __init__(
self,
hidden_size: int = 2048,
num_heads: int = 32,
num_kv_heads: int | None = None,
window_size: int | None = None,
rope_theta: float | None = 10000.,
max_position_embeddings: int | None = None,
norm_eps: float = 1e-5,
layer_idx: int = None,
):
super().__init__()
self.num_heads = num_heads
if num_kv_heads is None:
self.num_kv_heads = self.num_heads
else:
self.num_kv_heads = num_kv_heads
self.num_kv_groups = num_heads // self.num_kv_heads
self.hidden_size = hidden_size
self.head_dim = self.hidden_size // self.num_heads
self.kv_dim = self.num_kv_heads * self.head_dim
self.kv_dim = self.num_kv_heads * self.head_dim
self.window_size = window_size
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.layer_idx = layer_idx
self.q_proj = FusedBitLinear(self.hidden_size, self.hidden_size, bias=False)
self.k_proj = FusedBitLinear(self.hidden_size, self.kv_dim, bias=False)
self.v_proj = FusedBitLinear(self.hidden_size, self.kv_dim, bias=False)
self.o_proj = FusedBitLinear(self.hidden_size, self.hidden_size, bias=False)
self.rotary = RotaryEmbedding(dim=self.head_dim, base=self.rope_theta)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
output_attentions: bool = False,
use_cache: bool = False,
**kwargs,
) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | 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.size()
q = rearrange(self.q_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim)
k = rearrange(self.k_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim)
v = rearrange(self.v_proj(hidden_states), '... (h d) -> ... h d', d=self.head_dim)
# equivalent to cu_seqlens in `flash_attn`
cu_seqlens = kwargs.get('cu_seqlens')
seqlen_offset, max_seqlen = 0, q_len
if past_key_values is not None:
seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
max_seqlen = q.shape[1] + seqlen_offset
if attention_mask is not None:
# to deliminate the offsets of padding tokens
seqlen_offset = seqlen_offset + prepare_lens_from_mask(attention_mask) - attention_mask.shape[-1]
max_seqlen = q.shape[1] + max(seqlen_offset)
if self.max_position_embeddings is not None:
max_seqlen = max(max_seqlen, self.max_position_embeddings)
q, k = self.rotary(q, k, seqlen_offset=seqlen_offset, max_seqlen=max_seqlen, cu_seqlens=cu_seqlens)
if past_key_values is not None:
cache_has_content = past_key_values.get_seq_length(self.layer_idx) > 0
k_cached, v_cached = past_key_values.update(
attn_state=(k.flatten(-2, -1), v.flatten(-2, -1)),
layer_idx=self.layer_idx,
offset=q_len,
cache_kwargs=dict(window_size=self.window_size),
)['attn_state']
if cache_has_content:
k, v = k_cached, v_cached
k = rearrange(k, '... (h d) -> ... h d', d=self.head_dim)
v = rearrange(v, '... (h d) -> ... h d', d=self.head_dim)
if flash_attn_func is None:
raise ImportError("Please install Flash Attention via `pip install flash-attn --no-build-isolation` first")
# Contains at least one padding token in the sequence
if attention_mask is not None:
q, (k, v), indices_q, cu_seqlens, max_seq_lens = unpad_input(q, (k, v), attention_mask, q_len)
cu_seqlens_q, cu_seqlens_k = cu_seqlens
max_seqlen_q, max_seqlen_k = max_seq_lens
o = flash_attn_varlen_func(
q, k, v,
cu_seqlens_q=cu_seqlens_q,
cu_seqlens_k=cu_seqlens_k,
max_seqlen_q=max_seqlen_q,
max_seqlen_k=max_seqlen_k,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
)
o = pad_input(o, indices_q, batch_size, q_len)
elif cu_seqlens is not None:
o = flash_attn_varlen_func(
q.squeeze(0), k.squeeze(0), v.squeeze(0),
cu_seqlens_q=cu_seqlens,
cu_seqlens_k=cu_seqlens,
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
).unsqueeze(0)
else:
o = flash_attn_func(
q, k, v,
causal=True,
window_size=(-1, -1) if self.window_size is None else (self.window_size-1, 0),
)
o = o.reshape(batch_size, q_len, -1)
o = self.o_proj(o)
if not output_attentions:
attentions = None
return o, attentions, past_key_values
|