File size: 5,138 Bytes
72b790c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) 2023, Dan Fu and Simran Arora.
# Adapted from https://github.com/HazyResearch/safari/blob/main/src/models/sequence/hyena.py

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

contract = oe.contract
from .hyena_utils import HyenaFilter


class MonarchMixerSequenceMixing(nn.Module):
    def __init__(
        self,
        d_model,
        l_max=128,
        dropout=0.0,
        hyena_kernel_lr=None,
        bidirectional=False,
        hyena_lr_pos_emb=1e-5,
        hyena_w=10,
        hyena_w_mod=1,
        hyena_wd=0.1,
        hyena_emb_dim=3,
        hyena_filter_dropout=0.0,
        hyena_filter_order=16,
        residual_long_conv=False,
        hyena_training_additions=False,
    ):
        super().__init__()

        self.d_model = d_model
        self.l_max = l_max
        self.kernel_lr = hyena_kernel_lr
        self.channels = 1
        self.bidirectional = bidirectional
        self.residual_long_conv = residual_long_conv
        self.NUM_PROJECTIONS = 3

        print('-- Bidirectional:', self.bidirectional)
        print("-- Using Long Conv Residual:", self.residual_long_conv)
        print('-- Hyena w:', hyena_w)
        print('-- Hyena w mod:', hyena_w_mod)
        print(f"-- Hyena filter order: {hyena_filter_order}")
        print(f"-- Hyena filter dropout: {hyena_filter_dropout}")
        print(f"-- Hyena filter wd: {hyena_wd}")
        print(f"-- Hyena filter emb dim: {hyena_emb_dim}")
        print(f"-- Hyena filter lr: {hyena_kernel_lr}")
        print(f"-- Hyena filter lr pos emb: {hyena_lr_pos_emb}")

        self.filter_fn = HyenaFilter(
            self.d_model,
            order=hyena_filter_order,
            seq_len=self.l_max,
            dropout=hyena_filter_dropout,
            bidirectional=self.bidirectional,
            lr=hyena_kernel_lr,
            lr_pos_emb=hyena_lr_pos_emb,
            w=hyena_w,  # frequency of periodic activations
            w_mod=hyena_w_mod,
            wd=hyena_wd,  # weight decay of kernel parameters
            emb_dim=hyena_emb_dim,
        )
        
        if self.residual_long_conv:
            self.filter_fn2 = HyenaFilter(
                self.d_model,
                order=hyena_filter_order,
                seq_len=self.l_max,
                dropout=hyena_filter_dropout,
                bidirectional=self.bidirectional,
                lr=hyena_kernel_lr,
                lr_pos_emb=hyena_lr_pos_emb,
                w=hyena_w,  # frequency of periodic activations
                w_mod=hyena_w_mod,
                wd=hyena_wd,  # weight decay of kernel parameters
                emb_dim=hyena_emb_dim,
            )
        
        # setup projections
        self.in_linear = nn.Linear(d_model, 3 * d_model)
        self.out_linear = nn.Linear(d_model, d_model)
        self.hyena_training_additions = hyena_training_additions
        if self.hyena_training_additions:
            self.act = nn.Identity()
            self.drop = nn.Dropout(dropout)
            self.layernorm = nn.LayerNorm(d_model)
        
        # setup short conv
        total_width = self.d_model * self.NUM_PROJECTIONS
        self.short_filter = nn.Conv1d(
            in_channels=total_width,
            out_channels=total_width,
            kernel_size=3,
            groups=total_width,
            padding=2,
        )


    def forward(self, u, **kwargs):
        # u is B L H
        if self.hyena_training_additions:
            u = self.layernorm(u)
        L = u.size(-2)

        # in projection
        u_orig = u
        u = self.in_linear(u)
        u = rearrange(u, "b l d -> b d l")
        
        # short filter
        uc = self.short_filter(u)[..., :L]

        x1, x2, v = uc.split(self.d_model, dim=1)
        
        v = v * x1
        if self.hyena_training_additions:
            v = self.drop(v)

        k = self.filter_fn.filter(L, device=u.device)
        k = rearrange(k, "c l d -> c d l")[0] # `c` is always 1 by default

        if self.bidirectional:
            k_rev = self.filter_fn.filter_rev(L, device=u.device)
            k_rev = rearrange(k_rev, "c l d -> c d l")[0] # `c` is always 1 by default
        else:
            k_rev = None

        y = self.filter_fn(v, L, k_fwd=k, k_rev=k_rev, bias= self.filter_fn.bias[None, :, None])

        if self.residual_long_conv:
            k2 = self.filter_fn2.filter(L, device=u.device)
            k2 = rearrange(k2, "c l d -> c d l")[0]

            if self.bidirectional:
                k2_rev = self.filter_fn2.filter_rev(L, device=u.device)
                k2_rev = rearrange(k2_rev, "c l d -> c d l")[0] # `c` is always 1 by default
            else:
                k2_rev = None                

            yu = self.filter_fn2(u_orig.transpose(-1, -2), L, k_fwd=k2, k_rev=k2_rev, bias= self.filter_fn2.bias[None, :, None])
        
        # post gating
        y = y * x2

        if self.residual_long_conv:
            y = y + yu

        y = y.transpose(-1, -2)
        if self.hyena_training_additions:
            y = self.drop(self.act(y))
        y = self.out_linear(y)

        return y, None