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# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Contents of this file were adapted from the open source fairseq repository.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import math
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from esm.modules import SinusoidalPositionalEmbedding
from .features import GVPInputFeaturizer, DihedralFeatures
from .gvp_encoder import GVPEncoder
from .transformer_layer import TransformerEncoderLayer
from .util import nan_to_num, get_rotation_frames, rotate, rbf
class GVPTransformerEncoder(nn.Module):
"""
Transformer encoder consisting of *args.encoder.layers* layers. Each layer
is a :class:`TransformerEncoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_tokens (torch.nn.Embedding): input embedding
"""
def __init__(self, args, dictionary, embed_tokens):
super().__init__()
self.args = args
self.dictionary = dictionary
self.dropout_module = nn.Dropout(args.dropout)
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = SinusoidalPositionalEmbedding(
embed_dim,
self.padding_idx,
)
self.embed_gvp_input_features = nn.Linear(15, embed_dim)
self.embed_confidence = nn.Linear(16, embed_dim)
self.embed_dihedrals = DihedralFeatures(embed_dim)
gvp_args = argparse.Namespace()
for k, v in vars(args).items():
if k.startswith("gvp_"):
setattr(gvp_args, k[4:], v)
self.gvp_encoder = GVPEncoder(gvp_args)
gvp_out_dim = gvp_args.node_hidden_dim_scalar + (3 *
gvp_args.node_hidden_dim_vector)
self.embed_gvp_output = nn.Linear(gvp_out_dim, embed_dim)
self.layers = nn.ModuleList([])
self.layers.extend(
[self.build_encoder_layer(args) for i in range(args.encoder_layers)]
)
self.num_layers = len(self.layers)
self.layer_norm = nn.LayerNorm(embed_dim)
def build_encoder_layer(self, args):
return TransformerEncoderLayer(args)
def forward_embedding(self, coords, padding_mask, confidence):
"""
Args:
coords: N, CA, C backbone coordinates in shape length x 3 (atoms) x 3
padding_mask: boolean Tensor (true for padding) of shape length
confidence: confidence scores between 0 and 1 of shape length
"""
components = dict()
coord_mask = torch.all(torch.all(torch.isfinite(coords), dim=-1), dim=-1)
coords = nan_to_num(coords)
mask_tokens = (
padding_mask * self.dictionary.padding_idx +
~padding_mask * self.dictionary.get_idx("<mask>")
)
components["tokens"] = self.embed_tokens(mask_tokens) * self.embed_scale
components["diherals"] = self.embed_dihedrals(coords)
# GVP encoder
gvp_out_scalars, gvp_out_vectors = self.gvp_encoder(coords,
coord_mask, padding_mask, confidence)
R = get_rotation_frames(coords)
# Rotate to local rotation frame for rotation-invariance
gvp_out_features = torch.cat([
gvp_out_scalars,
rotate(gvp_out_vectors, R.transpose(-2, -1)).flatten(-2, -1),
], dim=-1)
components["gvp_out"] = self.embed_gvp_output(gvp_out_features)
components["confidence"] = self.embed_confidence(
rbf(confidence, 0., 1.))
# In addition to GVP encoder outputs, also directly embed GVP input node
# features to the Transformer
scalar_features, vector_features = GVPInputFeaturizer.get_node_features(
coords, coord_mask, with_coord_mask=False)
features = torch.cat([
scalar_features,
rotate(vector_features, R.transpose(-2, -1)).flatten(-2, -1),
], dim=-1)
components["gvp_input_features"] = self.embed_gvp_input_features(features)
embed = sum(components.values())
# for k, v in components.items():
# print(k, torch.mean(v, dim=(0,1)), torch.std(v, dim=(0,1)))
x = embed
x = x + self.embed_positions(mask_tokens)
x = self.dropout_module(x)
return x, components
def forward(
self,
coords,
encoder_padding_mask,
confidence,
return_all_hiddens: bool = False,
):
"""
Args:
coords (Tensor): backbone coordinates
shape batch_size x num_residues x num_atoms (3 for N, CA, C) x 3
encoder_padding_mask (ByteTensor): the positions of
padding elements of shape `(batch_size x num_residues)`
confidence (Tensor): the confidence score of shape (batch_size x
num_residues). The value is between 0. and 1. for each residue
coordinate, or -1. if no coordinate is given
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(num_residues, batch_size, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch_size, num_residues)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch_size, num_residues, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(num_residues, batch_size, embed_dim)`.
Only populated if *return_all_hiddens* is True.
"""
x, encoder_embedding = self.forward_embedding(coords,
encoder_padding_mask, confidence)
# account for padding while computing the representation
x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states = []
if return_all_hiddens:
encoder_states.append(x)
# encoder layers
for layer in self.layers:
x = layer(
x, encoder_padding_mask=encoder_padding_mask
)
if return_all_hiddens:
assert encoder_states is not None
encoder_states.append(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [encoder_padding_mask], # B x T
"encoder_embedding": [encoder_embedding], # dictionary
"encoder_states": encoder_states, # List[T x B x C]
}
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