flash-linear-attention/fla/layers/rebased.py at main · fla-org/flash-linear-attention · GitHub

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
# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
#   https://github.com/fla-org/flash-linear-attention/graphs/contributors

"""
https://github.com/corl-team/rebased/blob/main/flash_linear_attention/fla/layers/rebased_fast.py
"""

from __future__ import annotations

import torch
import torch.nn as nn
from einops import rearrange

from fla.modules.feature_map import RebasedFeatureMap
from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn
from fla.ops.rebased import parallel_rebased


class ReBasedLinearAttention(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        l_max: int = 2048,
        feature_dim: int = 16,
        num_key_value_heads: int = 16,
        num_heads: int = 16,
        use_gamma: bool | None = True,
        use_beta: bool | None = True,
        normalize: bool | None = True,
        causal: bool = True,
        eps: float = 1e-5,
        mode: str = "parallel",
        layer_idx: int | None = None,
        **kwargs,
    ) -> ReBasedLinearAttention:
        super().__init__()
        self.hidden_size = hidden_size
        self.l_max = l_max
        self.mode = mode
        assert self.mode in ["fused_chunk", "parallel", 'chunk']

        self.feature_dim = feature_dim
        self.num_key_value_heads = num_key_value_heads
        self.num_heads = num_heads
        self.head_dim = self.hidden_size // self.num_key_value_heads
        self.use_gamma = use_gamma
        self.use_beta = use_beta
        self.normalize = normalize
        self.causal = causal
        self.eps = eps
        self.mode = mode
        self.layer_idx = layer_idx

        self.feature_map = RebasedFeatureMap(self.feature_dim, use_gamma, use_beta, normalize)
        self.q_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.feature_dim * self.num_heads, bias=False)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
        self.dropout = nn.Identity()

    def forward(self, hidden_states: torch.Tensor, **kwargs):
        mode = self.mode
        q = rearrange(
            self.q_proj(hidden_states),
            "... (h d) -> ... h d",
            h=self.num_heads,
            d=self.feature_dim,
        )
        k = rearrange(
            self.k_proj(hidden_states),
            "... (h d) -> ... h d",
            h=self.num_heads,
            d=self.feature_dim,
        )
        v = rearrange(
            self.v_proj(hidden_states),
            "... (h d) -> ... h d",
            h=self.num_key_value_heads,
            d=self.head_dim,
        )
        q, k = self.feature_map(q, flatten=(mode != 'parallel')), self.feature_map(k, flatten=(mode != 'parallel'))
        if mode == "fused_chunk":
            o, _ = fused_chunk_linear_attn(
                q=q,
                k=k,
                v=v,
                normalize=True,
                scale=1,
            )
        elif mode == 'chunk':
            o, _ = chunk_linear_attn(
                q=q,
                k=k,
                v=v,
                normalize=True,
                scale=1,
            )
        elif mode == 'parallel':
            assert q.shape[-1] <= 128
            o = parallel_rebased(
                q=q,
                k=k,
                v=v,
                eps=self.eps,
                use_scale=True,
                use_normalize=True,
            )
        o = rearrange(o, "... h d -> ... (h d)")
        o = self.o_proj(o)
        o = self.dropout(o)
        return o

    # https://github.com/HazyResearch/zoology/blob/main/zoology/mixers/based.py#L119
    def forward_reference(
        self,
        hidden_states: torch.Tensor,
        filters: torch.Tensor = None,
        *args,
        **kwargs,
    ):
        """
        x (torch.Tensor): tensor of shape (b, d, t)
        y (torch.Tensor): tensor of shape (b, d, t)
        """
        b, t, _ = hidden_states.size()
        q, k, v = self.q_proj(hidden_states), self.k_proj(hidden_states), self.v_proj(hidden_states)

        q = q.view(b, t, -1, self.feature_dim).transpose(1, 2)
        k = k.view(b, t, -1, self.feature_dim).transpose(1, 2)
        v = v.view(b, t, -1, self.head_dim).transpose(1, 2)

        # Linear attention
        q, k = self.feature_map(q), self.feature_map(k)
        q, k, v = q.unsqueeze(-2), k.unsqueeze(-2), v.unsqueeze(-1)

        # Compute attention
        if self.causal:
            y = ((q * (k * v).cumsum(2)).sum(-1) / ((q * k.cumsum(2)).sum(-1) + self.eps))
        else:
            y = ((q * (k * v).sum(2, True)).sum(-1) / ((q * k.sum(2, True)).sum(-1) + self.eps))
        y = rearrange(y, 'b h t d -> b t (h d)')
        y = self.o_proj(y.to(hidden_states.dtype))
        y = self.dropout(y)
        return y.to(hidden_states.dtype)