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import torch
import torch.nn as nn
import torch.nn.functional as F
from opt_einsum import contract as einsum
import torch.utils.checkpoint as checkpoint
from rfdiffusion.util import get_tips
from rfdiffusion.util_module import Dropout, create_custom_forward, rbf, init_lecun_normal
from rfdiffusion.Attention_module import Attention, FeedForwardLayer, AttentionWithBias
from rfdiffusion.Track_module import PairStr2Pair
import math
# Module contains classes and functions to generate initial embeddings
class PositionalEncoding2D(nn.Module):
# Add relative positional encoding to pair features
def __init__(self, d_model, minpos=-32, maxpos=32, p_drop=0.1):
super(PositionalEncoding2D, self).__init__()
self.minpos = minpos
self.maxpos = maxpos
self.nbin = abs(minpos)+maxpos+1
self.emb = nn.Embedding(self.nbin, d_model)
self.drop = nn.Dropout(p_drop)
def forward(self, x, idx):
bins = torch.arange(self.minpos, self.maxpos, device=x.device)
seqsep = idx[:,None,:] - idx[:,:,None] # (B, L, L)
#
ib = torch.bucketize(seqsep, bins).long() # (B, L, L)
emb = self.emb(ib) #(B, L, L, d_model)
x = x + emb # add relative positional encoding
return self.drop(x)
class MSA_emb(nn.Module):
# Get initial seed MSA embedding
def __init__(self, d_msa=256, d_pair=128, d_state=32, d_init=22+22+2+2,
minpos=-32, maxpos=32, p_drop=0.1, input_seq_onehot=False):
super(MSA_emb, self).__init__()
self.emb = nn.Linear(d_init, d_msa) # embedding for general MSA
self.emb_q = nn.Embedding(22, d_msa) # embedding for query sequence -- used for MSA embedding
self.emb_left = nn.Embedding(22, d_pair) # embedding for query sequence -- used for pair embedding
self.emb_right = nn.Embedding(22, d_pair) # embedding for query sequence -- used for pair embedding
self.emb_state = nn.Embedding(22, d_state)
self.drop = nn.Dropout(p_drop)
self.pos = PositionalEncoding2D(d_pair, minpos=minpos, maxpos=maxpos, p_drop=p_drop)
self.input_seq_onehot=input_seq_onehot
self.reset_parameter()
def reset_parameter(self):
self.emb = init_lecun_normal(self.emb)
self.emb_q = init_lecun_normal(self.emb_q)
self.emb_left = init_lecun_normal(self.emb_left)
self.emb_right = init_lecun_normal(self.emb_right)
self.emb_state = init_lecun_normal(self.emb_state)
nn.init.zeros_(self.emb.bias)
def forward(self, msa, seq, idx):
# Inputs:
# - msa: Input MSA (B, N, L, d_init)
# - seq: Input Sequence (B, L)
# - idx: Residue index
# Outputs:
# - msa: Initial MSA embedding (B, N, L, d_msa)
# - pair: Initial Pair embedding (B, L, L, d_pair)
N = msa.shape[1] # number of sequenes in MSA
# msa embedding
msa = self.emb(msa) # (B, N, L, d_model) # MSA embedding
# Sergey's one hot trick
tmp = (seq @ self.emb_q.weight).unsqueeze(1) # (B, 1, L, d_model) -- query embedding
msa = msa + tmp.expand(-1, N, -1, -1) # adding query embedding to MSA
msa = self.drop(msa)
# pair embedding
# Sergey's one hot trick
left = (seq @ self.emb_left.weight)[:,None] # (B, 1, L, d_pair)
right = (seq @ self.emb_right.weight)[:,:,None] # (B, L, 1, d_pair)
pair = left + right # (B, L, L, d_pair)
pair = self.pos(pair, idx) # add relative position
# state embedding
# Sergey's one hot trick
state = self.drop(seq @ self.emb_state.weight)
return msa, pair, state
class Extra_emb(nn.Module):
# Get initial seed MSA embedding
def __init__(self, d_msa=256, d_init=22+1+2, p_drop=0.1, input_seq_onehot=False):
super(Extra_emb, self).__init__()
self.emb = nn.Linear(d_init, d_msa) # embedding for general MSA
self.emb_q = nn.Embedding(22, d_msa) # embedding for query sequence
self.drop = nn.Dropout(p_drop)
self.input_seq_onehot=input_seq_onehot
self.reset_parameter()
def reset_parameter(self):
self.emb = init_lecun_normal(self.emb)
nn.init.zeros_(self.emb.bias)
def forward(self, msa, seq, idx):
# Inputs:
# - msa: Input MSA (B, N, L, d_init)
# - seq: Input Sequence (B, L)
# - idx: Residue index
# Outputs:
# - msa: Initial MSA embedding (B, N, L, d_msa)
N = msa.shape[1] # number of sequenes in MSA
msa = self.emb(msa) # (B, N, L, d_model) # MSA embedding
# Sergey's one hot trick
seq = (seq @ self.emb_q.weight).unsqueeze(1) # (B, 1, L, d_model) -- query embedding
msa = msa + seq.expand(-1, N, -1, -1) # adding query embedding to MSA
return self.drop(msa)
class TemplatePairStack(nn.Module):
# process template pairwise features
# use structure-biased attention
def __init__(self, n_block=2, d_templ=64, n_head=4, d_hidden=16, p_drop=0.25):
super(TemplatePairStack, self).__init__()
self.n_block = n_block
proc_s = [PairStr2Pair(d_pair=d_templ, n_head=n_head, d_hidden=d_hidden, p_drop=p_drop) for i in range(n_block)]
self.block = nn.ModuleList(proc_s)
self.norm = nn.LayerNorm(d_templ)
def forward(self, templ, rbf_feat, use_checkpoint=False):
B, T, L = templ.shape[:3]
templ = templ.reshape(B*T, L, L, -1)
for i_block in range(self.n_block):
if use_checkpoint:
templ = checkpoint.checkpoint(create_custom_forward(self.block[i_block]), templ, rbf_feat)
else:
templ = self.block[i_block](templ, rbf_feat)
return self.norm(templ).reshape(B, T, L, L, -1)
class TemplateTorsionStack(nn.Module):
def __init__(self, n_block=2, d_templ=64, n_head=4, d_hidden=16, p_drop=0.15):
super(TemplateTorsionStack, self).__init__()
self.n_block=n_block
self.proj_pair = nn.Linear(d_templ+36, d_templ)
proc_s = [AttentionWithBias(d_in=d_templ, d_bias=d_templ,
n_head=n_head, d_hidden=d_hidden) for i in range(n_block)]
self.row_attn = nn.ModuleList(proc_s)
proc_s = [FeedForwardLayer(d_templ, 4, p_drop=p_drop) for i in range(n_block)]
self.ff = nn.ModuleList(proc_s)
self.norm = nn.LayerNorm(d_templ)
def reset_parameter(self):
self.proj_pair = init_lecun_normal(self.proj_pair)
nn.init.zeros_(self.proj_pair.bias)
def forward(self, tors, pair, rbf_feat, use_checkpoint=False):
B, T, L = tors.shape[:3]
tors = tors.reshape(B*T, L, -1)
pair = pair.reshape(B*T, L, L, -1)
pair = torch.cat((pair, rbf_feat), dim=-1)
pair = self.proj_pair(pair)
for i_block in range(self.n_block):
if use_checkpoint:
tors = tors + checkpoint.checkpoint(create_custom_forward(self.row_attn[i_block]), tors, pair)
else:
tors = tors + self.row_attn[i_block](tors, pair)
tors = tors + self.ff[i_block](tors)
return self.norm(tors).reshape(B, T, L, -1)
class Templ_emb(nn.Module):
# Get template embedding
# Features are
# t2d:
# - 37 distogram bins + 6 orientations (43)
# - Mask (missing/unaligned) (1)
# t1d:
# - tiled AA sequence (20 standard aa + gap)
# - confidence (1)
# - contacting or note (1). NB this is added for diffusion model. Used only in complex training examples - 1 signifies that a residue in the non-diffused chain\
# i.e. the context, is in contact with the diffused chain.
#
#Added extra t1d dimension for contacting or not
def __init__(self, d_t1d=21+1+1, d_t2d=43+1, d_tor=30, d_pair=128, d_state=32,
n_block=2, d_templ=64,
n_head=4, d_hidden=16, p_drop=0.25):
super(Templ_emb, self).__init__()
# process 2D features
self.emb = nn.Linear(d_t1d*2+d_t2d, d_templ)
self.templ_stack = TemplatePairStack(n_block=n_block, d_templ=d_templ, n_head=n_head,
d_hidden=d_hidden, p_drop=p_drop)
self.attn = Attention(d_pair, d_templ, n_head, d_hidden, d_pair)
# process torsion angles
self.emb_t1d = nn.Linear(d_t1d+d_tor, d_templ)
self.proj_t1d = nn.Linear(d_templ, d_templ)
#self.tor_stack = TemplateTorsionStack(n_block=n_block, d_templ=d_templ, n_head=n_head,
# d_hidden=d_hidden, p_drop=p_drop)
self.attn_tor = Attention(d_state, d_templ, n_head, d_hidden, d_state)
self.reset_parameter()
def reset_parameter(self):
self.emb = init_lecun_normal(self.emb)
nn.init.zeros_(self.emb.bias)
nn.init.kaiming_normal_(self.emb_t1d.weight, nonlinearity='relu')
nn.init.zeros_(self.emb_t1d.bias)
self.proj_t1d = init_lecun_normal(self.proj_t1d)
nn.init.zeros_(self.proj_t1d.bias)
def forward(self, t1d, t2d, alpha_t, xyz_t, pair, state, use_checkpoint=False):
# Input
# - t1d: 1D template info (B, T, L, 23)
# - t2d: 2D template info (B, T, L, L, 44)
B, T, L, _ = t1d.shape
# Prepare 2D template features
left = t1d.unsqueeze(3).expand(-1,-1,-1,L,-1)
right = t1d.unsqueeze(2).expand(-1,-1,L,-1,-1)
#
templ = torch.cat((t2d, left, right), -1) # (B, T, L, L, 90)
templ = self.emb(templ) # Template templures (B, T, L, L, d_templ)
# process each template features
xyz_t = xyz_t.reshape(B*T, L, -1, 3)
rbf_feat = rbf(torch.cdist(xyz_t[:,:,1], xyz_t[:,:,1]))
templ = self.templ_stack(templ, rbf_feat, use_checkpoint=use_checkpoint) # (B, T, L,L, d_templ)
# Prepare 1D template torsion angle features
t1d = torch.cat((t1d, alpha_t), dim=-1) # (B, T, L, 23+30)
# process each template features
t1d = self.proj_t1d(F.relu_(self.emb_t1d(t1d)))
# mixing query state features to template state features
state = state.reshape(B*L, 1, -1)
t1d = t1d.permute(0,2,1,3).reshape(B*L, T, -1)
if use_checkpoint:
out = checkpoint.checkpoint(create_custom_forward(self.attn_tor), state, t1d, t1d)
out = out.reshape(B, L, -1)
else:
out = self.attn_tor(state, t1d, t1d).reshape(B, L, -1)
state = state.reshape(B, L, -1)
state = state + out
# mixing query pair features to template information (Template pointwise attention)
pair = pair.reshape(B*L*L, 1, -1)
templ = templ.permute(0, 2, 3, 1, 4).reshape(B*L*L, T, -1)
if use_checkpoint:
out = checkpoint.checkpoint(create_custom_forward(self.attn), pair, templ, templ)
out = out.reshape(B, L, L, -1)
else:
out = self.attn(pair, templ, templ).reshape(B, L, L, -1)
#
pair = pair.reshape(B, L, L, -1)
pair = pair + out
return pair, state
class Recycling(nn.Module):
def __init__(self, d_msa=256, d_pair=128, d_state=32):
super(Recycling, self).__init__()
self.proj_dist = nn.Linear(36+d_state*2, d_pair)
self.norm_state = nn.LayerNorm(d_state)
self.norm_pair = nn.LayerNorm(d_pair)
self.norm_msa = nn.LayerNorm(d_msa)
self.reset_parameter()
def reset_parameter(self):
self.proj_dist = init_lecun_normal(self.proj_dist)
nn.init.zeros_(self.proj_dist.bias)
def forward(self, seq, msa, pair, xyz, state):
B, L = pair.shape[:2]
state = self.norm_state(state)
#
left = state.unsqueeze(2).expand(-1,-1,L,-1)
right = state.unsqueeze(1).expand(-1,L,-1,-1)
# three anchor atoms
N = xyz[:,:,0]
Ca = xyz[:,:,1]
C = xyz[:,:,2]
# recreate Cb given N,Ca,C
b = Ca - N
c = C - Ca
a = torch.cross(b, c, dim=-1)
Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca
dist = rbf(torch.cdist(Cb, Cb))
dist = torch.cat((dist, left, right), dim=-1)
dist = self.proj_dist(dist)
pair = dist + self.norm_pair(pair)
msa = self.norm_msa(msa)
return msa, pair, state
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