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Zero
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import subprocess
import os
from joblib import Parallel, delayed, cpu_count
from tqdm import tqdm
import pandas as pd
import torch
import torch.nn as nn
import shutil
from .PretrainESM_model import PretrainESM_Model
class MemoESM(nn.Module):
def __init__(self, args):
super().__init__()
self.PretrainESM = PretrainESM_Model(args)
self.tokenizer = self.PretrainESM.tokenizer
self.memory = {}
# self.fix_memory = False
# def save_memory(self, path):
# params = {key:val for key,val in self.state_dict().items() if "GNNTuning" in key}
# torch.save({"params":params,"memory": self.memory}, path)
# def load_memory(self, path):
# data = torch.load(path)
# self.load_state_dict(data['params'], strict=False)
# self.memory = data['memory']
def clean_input(self, batch, score_cut=0.99):
'''
require: batch['pred_ids'], batch['attention_mask'], batch['confs']
'''
symbol = "<mask>"
replace_dict = {"-":symbol,
".":symbol,
"<eos>":symbol,
"<unk>":symbol,
"<cls>":symbol,
"<pad>":symbol,
"<null_1>":symbol,
"<mask>":symbol,
"U":symbol,
"O":symbol}
device = batch['pred_ids'].device
query_seqs = []
for pred_ids, mask, score in zip(batch['pred_ids'], batch['attention_mask'], batch['confs']):
seq = self.tokenizer.decode(pred_ids[mask], clean_up_tokenization_spaces=False)
elements = []
for idx, x in enumerate(seq.split(" ")):
symbol = replace_dict.get(x, x)
if score[idx] < score_cut:
symbol = "<mask>"
elements.append(symbol)
seq = "".join(elements)
query_seqs.append(seq)
results = self.tokenizer.batch_encode_plus(query_seqs, return_tensors="pt", padding=True)
return query_seqs
def initoutput(self, B, maxL, device):
self.out_pred_ids = torch.zeros(B, maxL, dtype=torch.long, device=device)
self.out_confs = torch.zeros(B, maxL, dtype=torch.float, device=device)
self.out_embeds = torch.zeros(B, maxL, 1280, dtype=torch.float, device=device)
self.titles = [None for i in range(B)]
def retrivel(self, titles, num_nodes, device, use_memory):
# retrieval
unseen = []
for idx in range(len(titles)):
name = titles[idx]
if (name in self.memory) and use_memory:
memo_pred_ids = self.memory[name]['pred_ids'].to(device)
memo_confs = self.memory[name]['confs'].to(device)
memo_embeds = self.memory[name]['embeds'].to(device)
self.out_pred_ids[idx, :num_nodes[idx]] = memo_pred_ids
self.out_confs[idx, :num_nodes[idx]] = memo_confs
self.out_embeds[idx, :num_nodes[idx]] = memo_embeds
self.titles[idx] = name
else:
unseen.append(idx)
return unseen
def rebatch(self, unseen, batch):
unseen_pred_ids = []
unseen_attention_mask = []
for i in unseen:
unseen_pred_ids.append(batch['pred_ids'][i])
unseen_attention_mask.append(batch['attention_mask'][i])
unseen_pred_ids = torch.stack(unseen_pred_ids)
unseen_attention_mask = torch.stack(unseen_attention_mask)
return {"pred_ids":unseen_pred_ids, "attention_mask":unseen_attention_mask}
def save2memory(self, unseen,outputs, titles, unseen_attention_mask):
# save to memory
for i in range(len(unseen)):
name = titles[unseen[i]]
self.titles[unseen[i]] = name
mask = unseen_attention_mask[i]
self.memory[name] = {"pred_ids":outputs['pred_ids'][i][mask].detach().to('cpu'),
"confs":outputs['confs'][i][mask].detach().to('cpu'),
"embeds":outputs['embeds'][i][mask].detach().to('cpu')}
def update(self, unseen, unseen_attention_mask, num_nodes, outputs):
# update
for idx in range(len(unseen)):
mask = unseen_attention_mask[idx]==1
self.out_pred_ids[unseen[idx], :num_nodes[unseen[idx]]] = outputs['pred_ids'][idx][mask]
self.out_confs[unseen[idx], :num_nodes[unseen[idx]]] = outputs['confs'][idx][mask]
self.out_embeds[unseen[idx], :num_nodes[unseen[idx]]] = outputs['embeds'][idx][mask]
@torch.no_grad()
def forward(self, batch, use_memory=False):
# debatch
# clean_seqs = self.clean_input(batch)
device = batch['probs'].device
B, maxL, _ = batch['probs'].shape
num_nodes = batch['attention_mask'].sum(dim=-1).tolist()
self.initoutput(B, maxL, device)
unseen = self.retrivel(batch['title'], num_nodes, device, use_memory)
if len(unseen)>0:
# batch forward
new_batch = self.rebatch(unseen, batch)
outputs = self.PretrainESM(new_batch)
self.save2memory(unseen,outputs, batch['title'], new_batch['attention_mask'])
self.update(unseen, new_batch['attention_mask'], num_nodes, outputs)
return {'title':self.titles,'pred_ids':self.out_pred_ids, 'confs':self.out_confs, 'embeds':self.out_embeds, 'attention_mask':batch['attention_mask']}
if __name__ == '__main__':
# work_space = '/gaozhangyang/experiments/PiFoldV2/data/mmseq_workspace2'
# target_seqs = ["MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPQTKTYFPHFDLSHGSAQVKGHG", "MVHLTPEEKSAVTALWGKVNVDEVGVEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV",
# "MVLSPADKTNVKAAWGKVGAGGAEALERMFLSFPQKTYYTYFPHFDLSHGSAQVKGHG"]
# query_seqs = ["MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKFPHFDLSHGSAQV", "MVHLTPEEKSAVTALWGKVNVDEVGGGRLLVVYPWTQRFFESFGDLSTPDAV",]
# results = search_seqs(query_seqs, target_seqs, work_space)
# print(results)
import biotite.sequence as seq
import biotite.sequence.align as align
# Create example query and target protein sequences
query_seq1 = seq.ProteinSequence("MSKXXKAFLNKXXL")
target_seq1 = seq.ProteinSequence("MSKVKAALNKVLL")
target_seq2 = seq.ProteinSequence("MSKVKKALNKVLL")
target_seq3 = seq.ProteinSequence("MSTVAAALKMLLL")
results = search_seqs_biotite(["MSKXXKAFLNKXXL"], ["MSKVKAALNKVLL", "MSKVKKALNKVLL", "MSTVAAALKMLLL"])
# Print the alignments
print("Query alignments:")
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