File size: 5,115 Bytes
7409b0a |
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 |
# Copyright (c) Meta Platforms, Inc. and 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,
)
def forward(self, tokens, repr_layers=[], need_head_weights=False, return_contacts=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)
# x: B x T x C
mask_ratio_train = 0.15 * 0.8
src_lengths = (~padding_mask).sum(-1)
mask_ratio_observed = (tokens == self.mask_idx).sum(-1).to(x.dtype) / src_lengths
x = x * (1 - mask_ratio_train) / (1 - mask_ratio_observed)[:, None, None]
if padding_mask is not None:
x = x * (1 - padding_mask.unsqueeze(-1).type_as(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))
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 = self.lm_head(x)
result = {"logits": x, "representations": hidden_representations}
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, :, :]
result["attentions"] = attentions
if return_contacts:
contacts = self.contact_head(tokens, attentions)
result["contacts"] = contacts
return result
def predict_contacts(self, tokens):
return self(tokens, return_contacts=True)["contacts"]
|