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# 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']
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