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| from __future__ import print_function | |
| import json, time, os, sys, glob | |
| import shutil | |
| import numpy as np | |
| import torch | |
| from torch import optim | |
| from torch.utils.data import DataLoader | |
| from torch.utils.data.dataset import random_split, Subset | |
| import copy | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import random | |
| import itertools | |
| #A number of functions/classes are adopted from: https://github.com/jingraham/neurips19-graph-protein-design | |
| def _scores(S, log_probs, mask): | |
| """ Negative log probabilities """ | |
| criterion = torch.nn.NLLLoss(reduction='none') | |
| loss = criterion( | |
| log_probs.contiguous().view(-1,log_probs.size(-1)), | |
| S.contiguous().view(-1) | |
| ).view(S.size()) | |
| scores = torch.sum(loss * mask, dim=-1) / torch.sum(mask, dim=-1) | |
| return scores | |
| def _S_to_seq(S, mask): | |
| alphabet = 'ACDEFGHIKLMNPQRSTVWYX' | |
| seq = ''.join([alphabet[c] for c, m in zip(S.tolist(), mask.tolist()) if m > 0]) | |
| return seq | |
| def parse_PDB_biounits(x, atoms=['N','CA','C'], chain=None): | |
| ''' | |
| input: x = PDB filename | |
| atoms = atoms to extract (optional) | |
| output: (length, atoms, coords=(x,y,z)), sequence | |
| ''' | |
| alpha_1 = list("ARNDCQEGHILKMFPSTWYV-") | |
| states = len(alpha_1) | |
| alpha_3 = ['ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE', | |
| 'LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL','GAP'] | |
| aa_1_N = {a:n for n,a in enumerate(alpha_1)} | |
| aa_3_N = {a:n for n,a in enumerate(alpha_3)} | |
| aa_N_1 = {n:a for n,a in enumerate(alpha_1)} | |
| aa_1_3 = {a:b for a,b in zip(alpha_1,alpha_3)} | |
| aa_3_1 = {b:a for a,b in zip(alpha_1,alpha_3)} | |
| def AA_to_N(x): | |
| # ["ARND"] -> [[0,1,2,3]] | |
| x = np.array(x); | |
| if x.ndim == 0: x = x[None] | |
| return [[aa_1_N.get(a, states-1) for a in y] for y in x] | |
| def N_to_AA(x): | |
| # [[0,1,2,3]] -> ["ARND"] | |
| x = np.array(x); | |
| if x.ndim == 1: x = x[None] | |
| return ["".join([aa_N_1.get(a,"-") for a in y]) for y in x] | |
| xyz,seq,min_resn,max_resn = {},{},1e6,-1e6 | |
| for line in open(x,"rb"): | |
| line = line.decode("utf-8","ignore").rstrip() | |
| if line[:6] == "HETATM" and line[17:17+3] == "MSE": | |
| line = line.replace("HETATM","ATOM ") | |
| line = line.replace("MSE","MET") | |
| if line[:4] == "ATOM": | |
| ch = line[21:22] | |
| if ch == chain or chain is None: | |
| atom = line[12:12+4].strip() | |
| resi = line[17:17+3] | |
| resn = line[22:22+5].strip() | |
| x,y,z = [float(line[i:(i+8)]) for i in [30,38,46]] | |
| if resn[-1].isalpha(): | |
| resa,resn = resn[-1],int(resn[:-1])-1 | |
| else: | |
| resa,resn = "",int(resn)-1 | |
| # resn = int(resn) | |
| if resn < min_resn: | |
| min_resn = resn | |
| if resn > max_resn: | |
| max_resn = resn | |
| if resn not in xyz: | |
| xyz[resn] = {} | |
| if resa not in xyz[resn]: | |
| xyz[resn][resa] = {} | |
| if resn not in seq: | |
| seq[resn] = {} | |
| if resa not in seq[resn]: | |
| seq[resn][resa] = resi | |
| if atom not in xyz[resn][resa]: | |
| xyz[resn][resa][atom] = np.array([x,y,z]) | |
| # convert to numpy arrays, fill in missing values | |
| seq_,xyz_ = [],[] | |
| try: | |
| for resn in range(min_resn,max_resn+1): | |
| if resn in seq: | |
| for k in sorted(seq[resn]): seq_.append(aa_3_N.get(seq[resn][k],20)) | |
| else: seq_.append(20) | |
| if resn in xyz: | |
| for k in sorted(xyz[resn]): | |
| for atom in atoms: | |
| if atom in xyz[resn][k]: xyz_.append(xyz[resn][k][atom]) | |
| else: xyz_.append(np.full(3,np.nan)) | |
| else: | |
| for atom in atoms: xyz_.append(np.full(3,np.nan)) | |
| return np.array(xyz_).reshape(-1,len(atoms),3), N_to_AA(np.array(seq_)) | |
| except TypeError: | |
| return 'no_chain', 'no_chain' | |
| def parse_PDB(path_to_pdb, input_chain_list=None): | |
| c=0 | |
| pdb_dict_list = [] | |
| init_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G','H', 'I', 'J','K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T','U', 'V','W','X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j','k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't','u', 'v','w','x', 'y', 'z'] | |
| extra_alphabet = [str(item) for item in list(np.arange(300))] | |
| chain_alphabet = init_alphabet + extra_alphabet | |
| if input_chain_list: | |
| chain_alphabet = input_chain_list | |
| biounit_names = [path_to_pdb] | |
| for biounit in biounit_names: | |
| my_dict = {} | |
| s = 0 | |
| concat_seq = '' | |
| concat_N = [] | |
| concat_CA = [] | |
| concat_C = [] | |
| concat_O = [] | |
| concat_mask = [] | |
| coords_dict = {} | |
| for letter in chain_alphabet: | |
| xyz, seq = parse_PDB_biounits(biounit, atoms=['N','CA','C','O'], chain=letter) | |
| if type(xyz) != str: | |
| concat_seq += seq[0] | |
| my_dict['seq_chain_'+letter]=seq[0] | |
| coords_dict_chain = {} | |
| coords_dict_chain['N_chain_'+letter]=xyz[:,0,:].tolist() | |
| coords_dict_chain['CA_chain_'+letter]=xyz[:,1,:].tolist() | |
| coords_dict_chain['C_chain_'+letter]=xyz[:,2,:].tolist() | |
| coords_dict_chain['O_chain_'+letter]=xyz[:,3,:].tolist() | |
| my_dict['coords_chain_'+letter]=coords_dict_chain | |
| s += 1 | |
| fi = biounit.rfind("/") | |
| my_dict['name']=biounit[(fi+1):-4] | |
| my_dict['num_of_chains'] = s | |
| my_dict['seq'] = concat_seq | |
| if s <= len(chain_alphabet): | |
| pdb_dict_list.append(my_dict) | |
| c+=1 | |
| return pdb_dict_list | |
| def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None): | |
| """ Pack and pad batch into torch tensors """ | |
| alphabet = 'ACDEFGHIKLMNPQRSTVWYX' | |
| B = len(batch) | |
| lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths | |
| L_max = max([len(b['seq']) for b in batch]) | |
| X = np.zeros([B, L_max, 4, 3]) | |
| residue_idx = -100*np.ones([B, L_max], dtype=np.int32) | |
| chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted | |
| pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted | |
| pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted | |
| pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted | |
| chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted | |
| bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32) | |
| chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted | |
| S = np.zeros([B, L_max], dtype=np.int32) | |
| omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32) | |
| # Build the batch | |
| letter_list_list = [] | |
| visible_list_list = [] | |
| masked_list_list = [] | |
| masked_chain_length_list_list = [] | |
| tied_pos_list_of_lists_list = [] | |
| #shuffle all chains before the main loop | |
| for i, b in enumerate(batch): | |
| if chain_dict != None: | |
| masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F] | |
| else: | |
| masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_'] | |
| visible_chains = [] | |
| num_chains = b['num_of_chains'] | |
| all_chains = masked_chains + visible_chains | |
| #random.shuffle(all_chains) | |
| for i, b in enumerate(batch): | |
| mask_dict = {} | |
| a = 0 | |
| x_chain_list = [] | |
| chain_mask_list = [] | |
| chain_seq_list = [] | |
| chain_encoding_list = [] | |
| c = 1 | |
| letter_list = [] | |
| global_idx_start_list = [0] | |
| visible_list = [] | |
| masked_list = [] | |
| masked_chain_length_list = [] | |
| fixed_position_mask_list = [] | |
| omit_AA_mask_list = [] | |
| pssm_coef_list = [] | |
| pssm_bias_list = [] | |
| pssm_log_odds_list = [] | |
| bias_by_res_list = [] | |
| l0 = 0 | |
| l1 = 0 | |
| for step, letter in enumerate(all_chains): | |
| if letter in visible_chains: | |
| letter_list.append(letter) | |
| visible_list.append(letter) | |
| chain_seq = b[f'seq_chain_{letter}'] | |
| chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq]) | |
| chain_length = len(chain_seq) | |
| global_idx_start_list.append(global_idx_start_list[-1]+chain_length) | |
| chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary | |
| chain_mask = np.zeros(chain_length) #0.0 for visible chains | |
| x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] | |
| x_chain_list.append(x_chain) | |
| chain_mask_list.append(chain_mask) | |
| chain_seq_list.append(chain_seq) | |
| chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0])) | |
| l1 += chain_length | |
| residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1) | |
| l0 += chain_length | |
| c+=1 | |
| fixed_position_mask = np.ones(chain_length) | |
| fixed_position_mask_list.append(fixed_position_mask) | |
| omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32) | |
| omit_AA_mask_list.append(omit_AA_mask_temp) | |
| pssm_coef = np.zeros(chain_length) | |
| pssm_bias = np.zeros([chain_length, 21]) | |
| pssm_log_odds = 10000.0*np.ones([chain_length, 21]) | |
| pssm_coef_list.append(pssm_coef) | |
| pssm_bias_list.append(pssm_bias) | |
| pssm_log_odds_list.append(pssm_log_odds) | |
| bias_by_res_list.append(np.zeros([chain_length, 21])) | |
| if letter in masked_chains: | |
| masked_list.append(letter) | |
| letter_list.append(letter) | |
| chain_seq = b[f'seq_chain_{letter}'] | |
| chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq]) | |
| chain_length = len(chain_seq) | |
| global_idx_start_list.append(global_idx_start_list[-1]+chain_length) | |
| masked_chain_length_list.append(chain_length) | |
| chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary | |
| chain_mask = np.ones(chain_length) #1.0 for masked | |
| x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3] | |
| x_chain_list.append(x_chain) | |
| chain_mask_list.append(chain_mask) | |
| chain_seq_list.append(chain_seq) | |
| chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0])) | |
| l1 += chain_length | |
| residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1) | |
| l0 += chain_length | |
| c+=1 | |
| fixed_position_mask = np.ones(chain_length) | |
| if fixed_position_dict!=None: | |
| fixed_pos_list = fixed_position_dict[b['name']][letter] | |
| if fixed_pos_list: | |
| fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0 | |
| fixed_position_mask_list.append(fixed_position_mask) | |
| omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32) | |
| if omit_AA_dict!=None: | |
| for item in omit_AA_dict[b['name']][letter]: | |
| idx_AA = np.array(item[0])-1 | |
| AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0]) | |
| idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx]) | |
| omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1 | |
| omit_AA_mask_list.append(omit_AA_mask_temp) | |
| pssm_coef = np.zeros(chain_length) | |
| pssm_bias = np.zeros([chain_length, 21]) | |
| pssm_log_odds = 10000.0*np.ones([chain_length, 21]) | |
| if pssm_dict: | |
| if pssm_dict[b['name']][letter]: | |
| pssm_coef = pssm_dict[b['name']][letter]['pssm_coef'] | |
| pssm_bias = pssm_dict[b['name']][letter]['pssm_bias'] | |
| pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds'] | |
| pssm_coef_list.append(pssm_coef) | |
| pssm_bias_list.append(pssm_bias) | |
| pssm_log_odds_list.append(pssm_log_odds) | |
| if bias_by_res_dict: | |
| bias_by_res_list.append(bias_by_res_dict[b['name']][letter]) | |
| else: | |
| bias_by_res_list.append(np.zeros([chain_length, 21])) | |
| letter_list_np = np.array(letter_list) | |
| tied_pos_list_of_lists = [] | |
| tied_beta = np.ones(L_max) | |
| if tied_positions_dict!=None: | |
| tied_pos_list = tied_positions_dict[b['name']] | |
| if tied_pos_list: | |
| set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list]))) | |
| for tied_item in tied_pos_list: | |
| one_list = [] | |
| for k, v in tied_item.items(): | |
| start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]] | |
| if isinstance(v[0], list): | |
| for v_count in range(len(v[0])): | |
| one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first | |
| tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count] | |
| else: | |
| for v_ in v: | |
| one_list.append(start_idx+v_-1)#make 0 to be the first | |
| tied_pos_list_of_lists.append(one_list) | |
| tied_pos_list_of_lists_list.append(tied_pos_list_of_lists) | |
| x = np.concatenate(x_chain_list,0) #[L, 4, 3] | |
| all_sequence = "".join(chain_seq_list) | |
| m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted | |
| chain_encoding = np.concatenate(chain_encoding_list,0) | |
| m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted | |
| pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted | |
| pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted | |
| pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted | |
| bias_by_res_ = np.concatenate(bias_by_res_list, 0) #[L,21], 0.0 for places where AA frequencies don't need to be tweaked | |
| l = len(all_sequence) | |
| x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, )) | |
| X[i,:,:,:] = x_pad | |
| m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, )) | |
| m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, )) | |
| omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, )) | |
| chain_M[i,:] = m_pad | |
| chain_M_pos[i,:] = m_pos_pad | |
| omit_AA_mask[i,] = omit_AA_mask_pad | |
| chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, )) | |
| chain_encoding_all[i,:] = chain_encoding_pad | |
| pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, )) | |
| pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) | |
| pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) | |
| pssm_coef_all[i,:] = pssm_coef_pad | |
| pssm_bias_all[i,:] = pssm_bias_pad | |
| pssm_log_odds_all[i,:] = pssm_log_odds_pad | |
| bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, )) | |
| bias_by_res_all[i,:] = bias_by_res_pad | |
| # Convert to labels | |
| indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32) | |
| S[i, :l] = indices | |
| letter_list_list.append(letter_list) | |
| visible_list_list.append(visible_list) | |
| masked_list_list.append(masked_list) | |
| masked_chain_length_list_list.append(masked_chain_length_list) | |
| isnan = np.isnan(X) | |
| mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32) | |
| X[isnan] = 0. | |
| # Conversion | |
| pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device) | |
| pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device) | |
| pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device) | |
| tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device) | |
| jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32) | |
| bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device) | |
| phi_mask = np.pad(jumps, [[0,0],[1,0]]) | |
| psi_mask = np.pad(jumps, [[0,0],[0,1]]) | |
| omega_mask = np.pad(jumps, [[0,0],[0,1]]) | |
| dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3] | |
| dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device) | |
| residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device) | |
| S = torch.from_numpy(S).to(dtype=torch.long,device=device) | |
| X = torch.from_numpy(X).to(dtype=torch.float32, device=device) | |
| mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device) | |
| chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device) | |
| chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device) | |
| omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device) | |
| chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device) | |
| return X, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta | |
| def loss_nll(S, log_probs, mask): | |
| """ Negative log probabilities """ | |
| criterion = torch.nn.NLLLoss(reduction='none') | |
| loss = criterion( | |
| log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1) | |
| ).view(S.size()) | |
| loss_av = torch.sum(loss * mask) / torch.sum(mask) | |
| return loss, loss_av | |
| def loss_smoothed(S, log_probs, mask, weight=0.1): | |
| """ Negative log probabilities """ | |
| S_onehot = torch.nn.functional.one_hot(S, 21).float() | |
| # Label smoothing | |
| S_onehot = S_onehot + weight / float(S_onehot.size(-1)) | |
| S_onehot = S_onehot / S_onehot.sum(-1, keepdim=True) | |
| loss = -(S_onehot * log_probs).sum(-1) | |
| loss_av = torch.sum(loss * mask) / torch.sum(mask) | |
| return loss, loss_av | |
| class StructureDataset(): | |
| def __init__(self, jsonl_file, verbose=True, truncate=None, max_length=100, | |
| alphabet='ACDEFGHIKLMNPQRSTVWYX-'): | |
| alphabet_set = set([a for a in alphabet]) | |
| discard_count = { | |
| 'bad_chars': 0, | |
| 'too_long': 0, | |
| 'bad_seq_length': 0 | |
| } | |
| with open(jsonl_file) as f: | |
| self.data = [] | |
| lines = f.readlines() | |
| start = time.time() | |
| for i, line in enumerate(lines): | |
| entry = json.loads(line) | |
| seq = entry['seq'] | |
| name = entry['name'] | |
| # Convert raw coords to np arrays | |
| #for key, val in entry['coords'].items(): | |
| # entry['coords'][key] = np.asarray(val) | |
| # Check if in alphabet | |
| bad_chars = set([s for s in seq]).difference(alphabet_set) | |
| if len(bad_chars) == 0: | |
| if len(entry['seq']) <= max_length: | |
| if True: | |
| self.data.append(entry) | |
| else: | |
| discard_count['bad_seq_length'] += 1 | |
| else: | |
| discard_count['too_long'] += 1 | |
| else: | |
| print(name, bad_chars, entry['seq']) | |
| discard_count['bad_chars'] += 1 | |
| # Truncate early | |
| if truncate is not None and len(self.data) == truncate: | |
| return | |
| if verbose and (i + 1) % 1000 == 0: | |
| elapsed = time.time() - start | |
| print('{} entries ({} loaded) in {:.1f} s'.format(len(self.data), i+1, elapsed)) | |
| print('discarded', discard_count) | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| return self.data[idx] | |
| class StructureDatasetPDB(): | |
| def __init__(self, pdb_dict_list, verbose=True, truncate=None, max_length=100, | |
| alphabet='ACDEFGHIKLMNPQRSTVWYX-'): | |
| alphabet_set = set([a for a in alphabet]) | |
| discard_count = { | |
| 'bad_chars': 0, | |
| 'too_long': 0, | |
| 'bad_seq_length': 0 | |
| } | |
| self.data = [] | |
| start = time.time() | |
| for i, entry in enumerate(pdb_dict_list): | |
| seq = entry['seq'] | |
| name = entry['name'] | |
| bad_chars = set([s for s in seq]).difference(alphabet_set) | |
| if len(bad_chars) == 0: | |
| if len(entry['seq']) <= max_length: | |
| self.data.append(entry) | |
| else: | |
| discard_count['too_long'] += 1 | |
| else: | |
| discard_count['bad_chars'] += 1 | |
| # Truncate early | |
| if truncate is not None and len(self.data) == truncate: | |
| return | |
| if verbose and (i + 1) % 1000 == 0: | |
| elapsed = time.time() - start | |
| #print('Discarded', discard_count) | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| return self.data[idx] | |
| class StructureLoader(): | |
| def __init__(self, dataset, batch_size=100, shuffle=True, | |
| collate_fn=lambda x:x, drop_last=False): | |
| self.dataset = dataset | |
| self.size = len(dataset) | |
| self.lengths = [len(dataset[i]['seq']) for i in range(self.size)] | |
| self.batch_size = batch_size | |
| sorted_ix = np.argsort(self.lengths) | |
| # Cluster into batches of similar sizes | |
| clusters, batch = [], [] | |
| batch_max = 0 | |
| for ix in sorted_ix: | |
| size = self.lengths[ix] | |
| if size * (len(batch) + 1) <= self.batch_size: | |
| batch.append(ix) | |
| batch_max = size | |
| else: | |
| clusters.append(batch) | |
| batch, batch_max = [], 0 | |
| if len(batch) > 0: | |
| clusters.append(batch) | |
| self.clusters = clusters | |
| def __len__(self): | |
| return len(self.clusters) | |
| def __iter__(self): | |
| np.random.shuffle(self.clusters) | |
| for b_idx in self.clusters: | |
| batch = [self.dataset[i] for i in b_idx] | |
| yield batch | |
| # The following gather functions | |
| def gather_edges(edges, neighbor_idx): | |
| # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C] | |
| neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1)) | |
| edge_features = torch.gather(edges, 2, neighbors) | |
| return edge_features | |
| def gather_nodes(nodes, neighbor_idx): | |
| # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C] | |
| # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C] | |
| neighbors_flat = neighbor_idx.view((neighbor_idx.shape[0], -1)) | |
| neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2)) | |
| # Gather and re-pack | |
| neighbor_features = torch.gather(nodes, 1, neighbors_flat) | |
| neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1]) | |
| return neighbor_features | |
| def gather_nodes_t(nodes, neighbor_idx): | |
| # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C] | |
| idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2)) | |
| neighbor_features = torch.gather(nodes, 1, idx_flat) | |
| return neighbor_features | |
| def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx): | |
| h_nodes = gather_nodes(h_nodes, E_idx) | |
| h_nn = torch.cat([h_neighbors, h_nodes], -1) | |
| return h_nn | |
| class EncLayer(nn.Module): | |
| def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): | |
| super(EncLayer, self).__init__() | |
| self.num_hidden = num_hidden | |
| self.num_in = num_in | |
| self.scale = scale | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.dropout3 = nn.Dropout(dropout) | |
| self.norm1 = nn.LayerNorm(num_hidden) | |
| self.norm2 = nn.LayerNorm(num_hidden) | |
| self.norm3 = nn.LayerNorm(num_hidden) | |
| self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) | |
| self.W2 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.W3 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.W11 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) | |
| self.W12 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.W13 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.act = torch.nn.GELU() | |
| self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) | |
| def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None): | |
| """ Parallel computation of full transformer layer """ | |
| h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) | |
| h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1) | |
| h_EV = torch.cat([h_V_expand, h_EV], -1) | |
| h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) | |
| if mask_attend is not None: | |
| h_message = mask_attend.unsqueeze(-1) * h_message | |
| dh = torch.sum(h_message, -2) / self.scale | |
| h_V = self.norm1(h_V + self.dropout1(dh)) | |
| dh = self.dense(h_V) | |
| h_V = self.norm2(h_V + self.dropout2(dh)) | |
| if mask_V is not None: | |
| mask_V = mask_V.unsqueeze(-1) | |
| h_V = mask_V * h_V | |
| h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) | |
| h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1) | |
| h_EV = torch.cat([h_V_expand, h_EV], -1) | |
| h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV))))) | |
| h_E = self.norm3(h_E + self.dropout3(h_message)) | |
| return h_V, h_E | |
| class DecLayer(nn.Module): | |
| def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): | |
| super(DecLayer, self).__init__() | |
| self.num_hidden = num_hidden | |
| self.num_in = num_in | |
| self.scale = scale | |
| self.dropout1 = nn.Dropout(dropout) | |
| self.dropout2 = nn.Dropout(dropout) | |
| self.norm1 = nn.LayerNorm(num_hidden) | |
| self.norm2 = nn.LayerNorm(num_hidden) | |
| self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True) | |
| self.W2 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.W3 = nn.Linear(num_hidden, num_hidden, bias=True) | |
| self.act = torch.nn.GELU() | |
| self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) | |
| def forward(self, h_V, h_E, mask_V=None, mask_attend=None): | |
| """ Parallel computation of full transformer layer """ | |
| # Concatenate h_V_i to h_E_ij | |
| h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_E.size(-2),-1) | |
| h_EV = torch.cat([h_V_expand, h_E], -1) | |
| h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) | |
| if mask_attend is not None: | |
| h_message = mask_attend.unsqueeze(-1) * h_message | |
| dh = torch.sum(h_message, -2) / self.scale | |
| h_V = self.norm1(h_V + self.dropout1(dh)) | |
| # Position-wise feedforward | |
| dh = self.dense(h_V) | |
| h_V = self.norm2(h_V + self.dropout2(dh)) | |
| if mask_V is not None: | |
| mask_V = mask_V.unsqueeze(-1) | |
| h_V = mask_V * h_V | |
| return h_V | |
| class PositionWiseFeedForward(nn.Module): | |
| def __init__(self, num_hidden, num_ff): | |
| super(PositionWiseFeedForward, self).__init__() | |
| self.W_in = nn.Linear(num_hidden, num_ff, bias=True) | |
| self.W_out = nn.Linear(num_ff, num_hidden, bias=True) | |
| self.act = torch.nn.GELU() | |
| def forward(self, h_V): | |
| h = self.act(self.W_in(h_V)) | |
| h = self.W_out(h) | |
| return h | |
| class PositionalEncodings(nn.Module): | |
| def __init__(self, num_embeddings, max_relative_feature=32): | |
| super(PositionalEncodings, self).__init__() | |
| self.num_embeddings = num_embeddings | |
| self.max_relative_feature = max_relative_feature | |
| self.linear = nn.Linear(2*max_relative_feature+1+1, num_embeddings) | |
| def forward(self, offset, mask): | |
| d = torch.clip(offset + self.max_relative_feature, 0, 2*self.max_relative_feature)*mask + (1-mask)*(2*self.max_relative_feature+1) | |
| d_onehot = torch.nn.functional.one_hot(d, 2*self.max_relative_feature+1+1) | |
| E = self.linear(d_onehot.float()) | |
| return E | |
| class ProteinFeatures(nn.Module): | |
| def __init__(self, edge_features, node_features, num_positional_embeddings=16, | |
| num_rbf=16, top_k=30, augment_eps=0., num_chain_embeddings=16): | |
| """ Extract protein features """ | |
| super(ProteinFeatures, self).__init__() | |
| self.edge_features = edge_features | |
| self.node_features = node_features | |
| self.top_k = top_k | |
| self.augment_eps = augment_eps | |
| self.num_rbf = num_rbf | |
| self.num_positional_embeddings = num_positional_embeddings | |
| self.embeddings = PositionalEncodings(num_positional_embeddings) | |
| node_in, edge_in = 6, num_positional_embeddings + num_rbf*25 | |
| self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False) | |
| self.norm_edges = nn.LayerNorm(edge_features) | |
| def _dist(self, X, mask, eps=1E-6): | |
| mask_2D = torch.unsqueeze(mask,1) * torch.unsqueeze(mask,2) | |
| dX = torch.unsqueeze(X,1) - torch.unsqueeze(X,2) | |
| D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) | |
| D_max, _ = torch.max(D, -1, keepdim=True) | |
| D_adjust = D + (1. - mask_2D) * D_max | |
| sampled_top_k = self.top_k | |
| D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False) | |
| return D_neighbors, E_idx | |
| def _rbf(self, D): | |
| device = D.device | |
| D_min, D_max, D_count = 2., 22., self.num_rbf | |
| D_mu = torch.linspace(D_min, D_max, D_count, device=device) | |
| D_mu = D_mu.view([1,1,1,-1]) | |
| D_sigma = (D_max - D_min) / D_count | |
| D_expand = torch.unsqueeze(D, -1) | |
| RBF = torch.exp(-((D_expand - D_mu) / D_sigma)**2) | |
| return RBF | |
| def _get_rbf(self, A, B, E_idx): | |
| D_A_B = torch.sqrt(torch.sum((A[:,:,None,:] - B[:,None,:,:])**2,-1) + 1e-6) #[B, L, L] | |
| D_A_B_neighbors = gather_edges(D_A_B[:,:,:,None], E_idx)[:,:,:,0] #[B,L,K] | |
| RBF_A_B = self._rbf(D_A_B_neighbors) | |
| return RBF_A_B | |
| def forward(self, X, mask, residue_idx, chain_labels): | |
| if self.augment_eps !='': | |
| if self.augment_eps > 0: | |
| X = X + self.augment_eps * torch.randn_like(X) | |
| b = X[:,:,1,:] - X[:,:,0,:] | |
| c = X[:,:,2,:] - X[:,:,1,:] | |
| a = torch.cross(b, c, dim=-1) | |
| Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + X[:,:,1,:] | |
| Ca = X[:,:,1,:] | |
| N = X[:,:,0,:] | |
| C = X[:,:,2,:] | |
| O = X[:,:,3,:] | |
| D_neighbors, E_idx = self._dist(Ca, mask) | |
| RBF_all = [] | |
| RBF_all.append(self._rbf(D_neighbors)) #Ca-Ca | |
| RBF_all.append(self._get_rbf(N, N, E_idx)) #N-N | |
| RBF_all.append(self._get_rbf(C, C, E_idx)) #C-C | |
| RBF_all.append(self._get_rbf(O, O, E_idx)) #O-O | |
| RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) #Cb-Cb | |
| RBF_all.append(self._get_rbf(Ca, N, E_idx)) #Ca-N | |
| RBF_all.append(self._get_rbf(Ca, C, E_idx)) #Ca-C | |
| RBF_all.append(self._get_rbf(Ca, O, E_idx)) #Ca-O | |
| RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) #Ca-Cb | |
| RBF_all.append(self._get_rbf(N, C, E_idx)) #N-C | |
| RBF_all.append(self._get_rbf(N, O, E_idx)) #N-O | |
| RBF_all.append(self._get_rbf(N, Cb, E_idx)) #N-Cb | |
| RBF_all.append(self._get_rbf(Cb, C, E_idx)) #Cb-C | |
| RBF_all.append(self._get_rbf(Cb, O, E_idx)) #Cb-O | |
| RBF_all.append(self._get_rbf(O, C, E_idx)) #O-C | |
| RBF_all.append(self._get_rbf(N, Ca, E_idx)) #N-Ca | |
| RBF_all.append(self._get_rbf(C, Ca, E_idx)) #C-Ca | |
| RBF_all.append(self._get_rbf(O, Ca, E_idx)) #O-Ca | |
| RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) #Cb-Ca | |
| RBF_all.append(self._get_rbf(C, N, E_idx)) #C-N | |
| RBF_all.append(self._get_rbf(O, N, E_idx)) #O-N | |
| RBF_all.append(self._get_rbf(Cb, N, E_idx)) #Cb-N | |
| RBF_all.append(self._get_rbf(C, Cb, E_idx)) #C-Cb | |
| RBF_all.append(self._get_rbf(O, Cb, E_idx)) #O-Cb | |
| RBF_all.append(self._get_rbf(C, O, E_idx)) #C-O | |
| RBF_all = torch.cat(tuple(RBF_all), dim=-1) | |
| offset = residue_idx[:,:,None]-residue_idx[:,None,:] | |
| offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K] | |
| d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long() #find self vs non-self interaction | |
| E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0] | |
| E_positional = self.embeddings(offset.long(), E_chains) | |
| E = torch.cat((E_positional, RBF_all), -1) | |
| E = self.edge_embedding(E) | |
| E = self.norm_edges(E) | |
| return E, E_idx | |
| class ProteinMPNN(nn.Module): | |
| def __init__(self, num_letters, node_features, edge_features, | |
| hidden_dim, num_encoder_layers=3, num_decoder_layers=3, | |
| vocab=21, k_neighbors=64, augment_eps=0.05, dropout=0.1): | |
| super(ProteinMPNN, self).__init__() | |
| # Hyperparameters | |
| self.node_features = node_features | |
| self.edge_features = edge_features | |
| self.hidden_dim = hidden_dim | |
| # Featurization layers | |
| self.features = ProteinFeatures(node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps) | |
| self.W_e = nn.Linear(edge_features, hidden_dim, bias=True) | |
| self.W_s = nn.Embedding(vocab, hidden_dim) | |
| # Encoder layers | |
| self.encoder_layers = nn.ModuleList([ | |
| EncLayer(hidden_dim, hidden_dim*2, dropout=dropout) | |
| for _ in range(num_encoder_layers) | |
| ]) | |
| # Decoder layers | |
| self.decoder_layers = nn.ModuleList([ | |
| DecLayer(hidden_dim, hidden_dim*3, dropout=dropout) | |
| for _ in range(num_decoder_layers) | |
| ]) | |
| self.W_out = nn.Linear(hidden_dim, num_letters, bias=True) | |
| for p in self.parameters(): | |
| if p.dim() > 1: | |
| nn.init.xavier_uniform_(p) | |
| def forward(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, use_input_decoding_order=False, decoding_order=None): | |
| """ Graph-conditioned sequence model """ | |
| device=X.device | |
| # Prepare node and edge embeddings | |
| E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) | |
| h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) | |
| h_E = self.W_e(E) | |
| # Encoder is unmasked self-attention | |
| mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) | |
| mask_attend = mask.unsqueeze(-1) * mask_attend | |
| for layer in self.encoder_layers: | |
| h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) | |
| # Concatenate sequence embeddings for autoregressive decoder | |
| h_S = self.W_s(S) | |
| h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) | |
| # Build encoder embeddings | |
| h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) | |
| h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) | |
| chain_M = chain_M*mask #update chain_M to include missing regions | |
| if not use_input_decoding_order: | |
| decoding_order = torch.argsort((chain_M+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] | |
| mask_size = E_idx.shape[1] | |
| permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() | |
| order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) | |
| mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) | |
| mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) | |
| mask_bw = mask_1D * mask_attend | |
| mask_fw = mask_1D * (1. - mask_attend) | |
| h_EXV_encoder_fw = mask_fw * h_EXV_encoder | |
| for layer in self.decoder_layers: | |
| # Masked positions attend to encoder information, unmasked see. | |
| h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) | |
| h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw | |
| h_V = layer(h_V, h_ESV, mask) | |
| logits = self.W_out(h_V) | |
| log_probs = F.log_softmax(logits, dim=-1) | |
| return log_probs | |
| def sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, bias_by_res=None): | |
| device = X.device | |
| # Prepare node and edge embeddings | |
| E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) | |
| h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) | |
| h_E = self.W_e(E) | |
| # Encoder is unmasked self-attention | |
| mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) | |
| mask_attend = mask.unsqueeze(-1) * mask_attend | |
| for layer in self.encoder_layers: | |
| h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) | |
| # Decoder uses masked self-attention | |
| chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions | |
| decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] | |
| mask_size = E_idx.shape[1] | |
| permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() | |
| order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) | |
| mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) | |
| mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) | |
| mask_bw = mask_1D * mask_attend | |
| mask_fw = mask_1D * (1. - mask_attend) | |
| N_batch, N_nodes = X.size(0), X.size(1) | |
| log_probs = torch.zeros((N_batch, N_nodes, 21), device=device) | |
| all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32) | |
| h_S = torch.zeros_like(h_V, device=device) | |
| S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device) | |
| h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))] | |
| constant = torch.tensor(omit_AAs_np, device=device) | |
| constant_bias = torch.tensor(bias_AAs_np, device=device) | |
| #chain_mask_combined = chain_mask*chain_M_pos | |
| omit_AA_mask_flag = omit_AA_mask != None | |
| h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) | |
| h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) | |
| h_EXV_encoder_fw = mask_fw * h_EXV_encoder | |
| for t_ in range(N_nodes): | |
| t = decoding_order[:,t_] #[B] | |
| chain_mask_gathered = torch.gather(chain_mask, 1, t[:,None]) #[B] | |
| bias_by_res_gathered = torch.gather(bias_by_res, 1, t[:,None,None].repeat(1,1,21))[:,0,:] #[B, 21] | |
| if (chain_mask_gathered==0).all(): | |
| S_t = torch.gather(S_true, 1, t[:,None]) | |
| else: | |
| # Hidden layers | |
| E_idx_t = torch.gather(E_idx, 1, t[:,None,None].repeat(1,1,E_idx.shape[-1])) | |
| h_E_t = torch.gather(h_E, 1, t[:,None,None,None].repeat(1,1,h_E.shape[-2], h_E.shape[-1])) | |
| h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) | |
| h_EXV_encoder_t = torch.gather(h_EXV_encoder_fw, 1, t[:,None,None,None].repeat(1,1,h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1])) | |
| mask_t = torch.gather(mask, 1, t[:,None]) | |
| for l, layer in enumerate(self.decoder_layers): | |
| # Updated relational features for future states | |
| h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) | |
| h_V_t = torch.gather(h_V_stack[l], 1, t[:,None,None].repeat(1,1,h_V_stack[l].shape[-1])) | |
| h_ESV_t = torch.gather(mask_bw, 1, t[:,None,None,None].repeat(1,1,mask_bw.shape[-2], mask_bw.shape[-1])) * h_ESV_decoder_t + h_EXV_encoder_t | |
| h_V_stack[l+1].scatter_(1, t[:,None,None].repeat(1,1,h_V.shape[-1]), layer(h_V_t, h_ESV_t, mask_V=mask_t)) | |
| # Sampling step | |
| h_V_t = torch.gather(h_V_stack[-1], 1, t[:,None,None].repeat(1,1,h_V_stack[-1].shape[-1]))[:,0] | |
| logits = self.W_out(h_V_t) / temperature | |
| probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1) | |
| if pssm_bias_flag: | |
| pssm_coef_gathered = torch.gather(pssm_coef, 1, t[:,None])[:,0] | |
| pssm_bias_gathered = torch.gather(pssm_bias, 1, t[:,None,None].repeat(1,1,pssm_bias.shape[-1]))[:,0] | |
| probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered | |
| if pssm_log_odds_flag: | |
| pssm_log_odds_mask_gathered = torch.gather(pssm_log_odds_mask, 1, t[:,None, None].repeat(1,1,pssm_log_odds_mask.shape[-1]))[:,0] #[B, 21] | |
| probs_masked = probs*pssm_log_odds_mask_gathered | |
| probs_masked += probs * 0.001 | |
| probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] | |
| if omit_AA_mask_flag: | |
| omit_AA_mask_gathered = torch.gather(omit_AA_mask, 1, t[:,None, None].repeat(1,1,omit_AA_mask.shape[-1]))[:,0] #[B, 21] | |
| probs_masked = probs*(1.0-omit_AA_mask_gathered) | |
| probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] | |
| S_t = torch.multinomial(probs, 1) | |
| all_probs.scatter_(1, t[:,None,None].repeat(1,1,21), (chain_mask_gathered[:,:,None,]*probs[:,None,:]).float()) | |
| S_true_gathered = torch.gather(S_true, 1, t[:,None]) | |
| S_t = (S_t*chain_mask_gathered+S_true_gathered*(1.0-chain_mask_gathered)).long() | |
| temp1 = self.W_s(S_t) | |
| h_S.scatter_(1, t[:,None,None].repeat(1,1,temp1.shape[-1]), temp1) | |
| S.scatter_(1, t[:,None], S_t) | |
| output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order} | |
| return output_dict | |
| def tied_sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, tied_pos=None, tied_beta=None, bias_by_res=None): | |
| device = X.device | |
| # Prepare node and edge embeddings | |
| E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) | |
| h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) | |
| h_E = self.W_e(E) | |
| # Encoder is unmasked self-attention | |
| mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) | |
| mask_attend = mask.unsqueeze(-1) * mask_attend | |
| for layer in self.encoder_layers: | |
| h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) | |
| # Decoder uses masked self-attention | |
| chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions | |
| decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] | |
| new_decoding_order = [] | |
| for t_dec in list(decoding_order[0,].cpu().data.numpy()): | |
| if t_dec not in list(itertools.chain(*new_decoding_order)): | |
| list_a = [item for item in tied_pos if t_dec in item] | |
| if list_a: | |
| new_decoding_order.append(list_a[0]) | |
| else: | |
| new_decoding_order.append([t_dec]) | |
| decoding_order = torch.tensor(list(itertools.chain(*new_decoding_order)), device=device)[None,].repeat(X.shape[0],1) | |
| mask_size = E_idx.shape[1] | |
| permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() | |
| order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) | |
| mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) | |
| mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) | |
| mask_bw = mask_1D * mask_attend | |
| mask_fw = mask_1D * (1. - mask_attend) | |
| N_batch, N_nodes = X.size(0), X.size(1) | |
| log_probs = torch.zeros((N_batch, N_nodes, 21), device=device) | |
| all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32) | |
| h_S = torch.zeros_like(h_V, device=device) | |
| S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device) | |
| h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))] | |
| constant = torch.tensor(omit_AAs_np, device=device) | |
| constant_bias = torch.tensor(bias_AAs_np, device=device) | |
| omit_AA_mask_flag = omit_AA_mask != None | |
| h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) | |
| h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) | |
| h_EXV_encoder_fw = mask_fw * h_EXV_encoder | |
| for t_list in new_decoding_order: | |
| logits = 0.0 | |
| logit_list = [] | |
| done_flag = False | |
| for t in t_list: | |
| if (chain_mask[:,t]==0).all(): | |
| S_t = S_true[:,t] | |
| for t in t_list: | |
| h_S[:,t,:] = self.W_s(S_t) | |
| S[:,t] = S_t | |
| done_flag = True | |
| break | |
| else: | |
| E_idx_t = E_idx[:,t:t+1,:] | |
| h_E_t = h_E[:,t:t+1,:,:] | |
| h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) | |
| h_EXV_encoder_t = h_EXV_encoder_fw[:,t:t+1,:,:] | |
| mask_t = mask[:,t:t+1] | |
| for l, layer in enumerate(self.decoder_layers): | |
| h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) | |
| h_V_t = h_V_stack[l][:,t:t+1,:] | |
| h_ESV_t = mask_bw[:,t:t+1,:,:] * h_ESV_decoder_t + h_EXV_encoder_t | |
| h_V_stack[l+1][:,t,:] = layer(h_V_t, h_ESV_t, mask_V=mask_t).squeeze(1) | |
| h_V_t = h_V_stack[-1][:,t,:] | |
| logit_list.append((self.W_out(h_V_t) / temperature)/len(t_list)) | |
| logits += tied_beta[t]*(self.W_out(h_V_t) / temperature)/len(t_list) | |
| if done_flag: | |
| pass | |
| else: | |
| bias_by_res_gathered = bias_by_res[:,t,:] #[B, 21] | |
| probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1) | |
| if pssm_bias_flag: | |
| pssm_coef_gathered = pssm_coef[:,t] | |
| pssm_bias_gathered = pssm_bias[:,t] | |
| probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered | |
| if pssm_log_odds_flag: | |
| pssm_log_odds_mask_gathered = pssm_log_odds_mask[:,t] | |
| probs_masked = probs*pssm_log_odds_mask_gathered | |
| probs_masked += probs * 0.001 | |
| probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] | |
| if omit_AA_mask_flag: | |
| omit_AA_mask_gathered = omit_AA_mask[:,t] | |
| probs_masked = probs*(1.0-omit_AA_mask_gathered) | |
| probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21] | |
| S_t_repeat = torch.multinomial(probs, 1).squeeze(-1) | |
| for t in t_list: | |
| h_S[:,t,:] = self.W_s(S_t_repeat) | |
| S[:,t] = S_t_repeat | |
| all_probs[:,t,:] = probs.float() | |
| output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order} | |
| return output_dict | |
| def conditional_probs(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, backbone_only=False): | |
| """ Graph-conditioned sequence model """ | |
| device=X.device | |
| # Prepare node and edge embeddings | |
| E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all) | |
| h_V_enc = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device) | |
| h_E = self.W_e(E) | |
| # Encoder is unmasked self-attention | |
| mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) | |
| mask_attend = mask.unsqueeze(-1) * mask_attend | |
| for layer in self.encoder_layers: | |
| h_V_enc, h_E = layer(h_V_enc, h_E, E_idx, mask, mask_attend) | |
| # Concatenate sequence embeddings for autoregressive decoder | |
| h_S = self.W_s(S) | |
| h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) | |
| # Build encoder embeddings | |
| h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) | |
| h_EXV_encoder = cat_neighbors_nodes(h_V_enc, h_EX_encoder, E_idx) | |
| chain_M = chain_M*mask #update chain_M to include missing regions | |
| chain_M_np = chain_M.cpu().numpy() | |
| idx_to_loop = np.argwhere(chain_M_np[0,:]==1)[:,0] | |
| log_conditional_probs = torch.zeros([X.shape[0], chain_M.shape[1], 21], device=device).float() | |
| for idx in idx_to_loop: | |
| h_V = torch.clone(h_V_enc) | |
| order_mask = torch.zeros(chain_M.shape[1], device=device).float() | |
| if backbone_only: | |
| order_mask = torch.ones(chain_M.shape[1], device=device).float() | |
| order_mask[idx] = 0. | |
| else: | |
| order_mask = torch.zeros(chain_M.shape[1], device=device).float() | |
| order_mask[idx] = 1. | |
| decoding_order = torch.argsort((order_mask[None,]+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] | |
| mask_size = E_idx.shape[1] | |
| permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float() | |
| order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse) | |
| mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) | |
| mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1]) | |
| mask_bw = mask_1D * mask_attend | |
| mask_fw = mask_1D * (1. - mask_attend) | |
| h_EXV_encoder_fw = mask_fw * h_EXV_encoder | |
| for layer in self.decoder_layers: | |
| # Masked positions attend to encoder information, unmasked see. | |
| h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) | |
| h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw | |
| h_V = layer(h_V, h_ESV, mask) | |
| logits = self.W_out(h_V) | |
| log_probs = F.log_softmax(logits, dim=-1) | |
| log_conditional_probs[:,idx,:] = log_probs[:,idx,:] | |
| return log_conditional_probs | |