File size: 6,643 Bytes
5aa3fcd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Union
import torch
import torch.nn as nn

import esm
from esm.modules import ContactPredictionHead, ESM1bLayerNorm, RobertaLMHead, TransformerLayer


class ESM2(nn.Module):
    def __init__(
        self,
        num_layers: int = 33,
        embed_dim: int = 1280,
        attention_heads: int = 20,
        alphabet: Union[esm.data.Alphabet, str] = "ESM-1b",
        token_dropout: bool = True,
    ):
        super().__init__()
        self.num_layers = num_layers
        self.embed_dim = embed_dim
        self.attention_heads = attention_heads
        if not isinstance(alphabet, esm.data.Alphabet):
            alphabet = esm.data.Alphabet.from_architecture(alphabet)
        self.alphabet = alphabet
        self.alphabet_size = len(alphabet)
        self.padding_idx = alphabet.padding_idx
        self.mask_idx = alphabet.mask_idx
        self.cls_idx = alphabet.cls_idx
        self.eos_idx = alphabet.eos_idx
        self.prepend_bos = alphabet.prepend_bos
        self.append_eos = alphabet.append_eos
        self.token_dropout = token_dropout

        self._init_submodules()

    def _init_submodules(self):
        self.embed_scale = 1
        self.embed_tokens = nn.Embedding(
            self.alphabet_size,
            self.embed_dim,
            padding_idx=self.padding_idx,
        )

        self.layers = nn.ModuleList(
            [
                TransformerLayer(
                    self.embed_dim,
                    4 * self.embed_dim,
                    self.attention_heads,
                    add_bias_kv=False,
                    use_esm1b_layer_norm=True,
                    use_rotary_embeddings=True,
                )
                for _ in range(self.num_layers)
            ]
        )

        self.contact_head = ContactPredictionHead(
            self.num_layers * self.attention_heads,
            self.prepend_bos,
            self.append_eos,
            eos_idx=self.eos_idx,
        )
        self.emb_layer_norm_after = ESM1bLayerNorm(self.embed_dim)

        self.lm_head = RobertaLMHead(
            embed_dim=self.embed_dim,
            output_dim=self.alphabet_size,
            weight=self.embed_tokens.weight,
        )
        self.supervised_linear = nn.Linear(self.embed_dim, 1)
    def forward(self, tokens, repr_layers=[], need_head_weights=True, return_contacts=True, return_representation=True, return_attentions_symm = False, return_attentions = False):
        if return_contacts:
            need_head_weights = True
        
        assert tokens.ndim == 2
        padding_mask = tokens.eq(self.padding_idx)  # B, T

        x = self.embed_scale * self.embed_tokens(tokens)

        if self.token_dropout:
            x.masked_fill_((tokens == self.mask_idx).unsqueeze(-1), 0.0)
            #print(f'tokens = {tokens}')
            #print(f'self.mask_idx = {self.mask_idx}')
            #print('x.shape = ', x.shape)
            # x: B x T x C
            mask_ratio_train = 0.15 * 0.8
            src_lengths = (~padding_mask).sum(-1)
            #print(f'mask_ratio_train = {mask_ratio_train}')
            #print(f'padding_mask = {padding_mask}')
            #print(f'src_lengths = {src_lengths}')
            mask_ratio_observed = (tokens == self.mask_idx).sum(-1).to(x.dtype) / src_lengths
            #print('mask_ratio_observed = ',mask_ratio_observed)
            x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]
            #print(f'x.shape = {x.shape}:\n', x)
        if padding_mask is not None:
            x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
            #print(f'x.shape = {x.shape}:\n', x)
        repr_layers = set(repr_layers)
        hidden_representations = {}
        if 0 in repr_layers:
            hidden_representations[0] = x

        if need_head_weights:
            attn_weights = []

        # (B, T, E) => (T, B, E)
        x = x.transpose(0, 1)

        if not padding_mask.any():
            padding_mask = None

        for layer_idx, layer in enumerate(self.layers):
            x, attn = layer(
                x,
                self_attn_padding_mask=padding_mask,
                need_head_weights=need_head_weights,
            )
            if (layer_idx + 1) in repr_layers:
                hidden_representations[layer_idx + 1] = x.transpose(0, 1)
            if need_head_weights:
                # (H, B, T, T) => (B, H, T, T)
                attn_weights.append(attn.transpose(1, 0))
#         print(x.shape) # 73, 2, 1280
        x = self.emb_layer_norm_after(x)
        x = x.transpose(0, 1)  # (T, B, E) => (B, T, E)

        # last hidden representation should have layer norm applied
        if (layer_idx + 1) in repr_layers:
            hidden_representations[layer_idx + 1] = x
        x_supervised = self.supervised_linear(x[:,0,:])
        x = self.lm_head(x)

        if return_representation:
            result = {"logits": x, "logits_supervised": x_supervised,  "representations": hidden_representations}
        else:
            result = {"logits": x, "logits_supervised": x_supervised}
        if need_head_weights:
            # attentions: B x L x H x T x T
            attentions = torch.stack(attn_weights, 1)
            if padding_mask is not None:
                attention_mask = 1 - padding_mask.type_as(attentions)
                attention_mask = attention_mask.unsqueeze(1) * attention_mask.unsqueeze(2)
                attentions = attentions * attention_mask[:, None, None, :, :]
            if return_attentions: result["attentions"] = attentions
            if return_contacts:
                attentions_symm, contacts = self.contact_head(tokens, attentions)
                result["contacts"] = contacts
                if return_attentions_symm: result["attentions_symm"] = attentions_symm

        return result

    def predict_contacts(self, tokens):
        return self(tokens, return_contacts=True)["contacts"]

    def predict_symmetric_attentions(self, tokens):
        return self(tokens, return_contacts=True)["attentions_symm"]
    
    def predict_attentions(self, tokens):
        return self(tokens, need_head_weights=True)["attentions"]

    def predict_representations(self, tokens):
        return self(tokens, return_representation=True)['representations']

    def predict_logits(self, tokens):
        return self(tokens)['logits']

    def predict_logits_supervised(self, tokens):
        return self(tokens)['logits_supervised']