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Zero
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import torch
import torch.nn as nn
from .Tuning import GNNTuning_Model
class MemoTuning(nn.Module):
def __init__(self, args, tunning_layers_n, tunning_layers_dim, input_design_dim, input_esm_dim, tunning_dropout, tokenizer, fix_memory=False):
super().__init__()
self.args = args
self.tunning_layers_dim = tunning_layers_dim
self.GNNTuning = GNNTuning_Model(args, num_encoder_layers=tunning_layers_n, hidden_dim=tunning_layers_dim, input_design_dim=input_design_dim, input_esm_dim=input_esm_dim, dropout = tunning_dropout)
self.tokenizer = tokenizer
self.memory = {}
def save_param_memory(self, path):
torch.save({"params":self.state_dict(),"memory": self.memory}, path)
def load_param_memory(self, path):
data = torch.load(path)
self.load_state_dict(data['params'])
self.memory = data['memory']
def get_seqs(self, pred_ids_raw, attention_mask):
query_seqs = []
for pred_ids, mask in zip(pred_ids_raw, attention_mask):
seq = self.tokenizer.decode(pred_ids[mask], clean_up_tokenization_spaces=False)
seq = "".join(seq.split(" "))
query_seqs.append(seq)
return query_seqs
def initoutput(self, pretrain_design, B, max_L, device):
# initialize output
self.out_pred_ids = torch.zeros_like(pretrain_design['pred_ids'])
self.out_confs = torch.zeros_like(pretrain_design['confs'])
self.out_embeds = torch.zeros(B, max_L, self.tunning_layers_dim, device = device)
self.out_attention_mask = torch.zeros_like(pretrain_design['attention_mask'])
self.out_probs = torch.zeros_like(pretrain_design['probs'])
self.out_log_probs = torch.zeros_like(pretrain_design['probs'])
self.titles = [None for i in range(B)]
def retrivel(self, keys, num_nodes,device, use_memory):
unseen = []
for idx in range(len(keys)):
key = keys[idx]
if (key in self.memory) and use_memory:
self.out_pred_ids[idx, :num_nodes[idx]] = self.memory[key]['pred_ids'].to(device)
self.out_confs[idx, :num_nodes[idx]] = self.memory[key]['confs'].to(device)
self.out_embeds[idx, :num_nodes[idx]] = self.memory[key]['embeds'].to(device)
self.out_attention_mask[idx, :num_nodes[idx]] = self.memory[key]['attention_mask'].to(device)
self.out_probs[idx, :num_nodes[idx]] = self.memory[key]['probs'].to(device)
self.out_log_probs[idx, :num_nodes[idx]] = self.memory[key]['log_probs'].to(device)
self.titles[idx] = key
else:
unseen.append(idx)
return unseen
def rebatch(self,unseen, batch_id_raw, E_idx_raw, h_E_raw, shift, num_nodes, pretrain_design, pretrain_esm_msa, pretrain_struct, pretrain_esmif, device):
unseen_design_pred_ids = []
unseen_design_confs = []
unseen_design_embeds = []
unseen_design_attention_mask = []
unseen_esm_pred_ids = []
unseen_esm_confs = []
unseen_esm_embeds = []
unseen_esm_attention_mask = []
unseen_struct_embeds = []
unseen_esmif_embeds = []
h_E = []
E_idx = []
batch_id = []
new_shift = 0
for bid, i in enumerate(unseen):
edge_mask = batch_id_raw[E_idx_raw[0]] == i
h_E.append(h_E_raw[edge_mask])
E_idx.append(E_idx_raw[:,edge_mask]-shift[i]+new_shift)
batch_id.append(torch.ones(num_nodes[i], device=device).long()*bid)
new_shift += num_nodes[i]
unseen_design_pred_ids.append(pretrain_design['pred_ids'][i])
unseen_design_confs.append(pretrain_design['confs'][i])
unseen_design_embeds.append(pretrain_design['embeds'][i])
unseen_design_attention_mask.append(pretrain_design['attention_mask'][i])
if self.args.use_LM:
unseen_esm_pred_ids.append(pretrain_esm_msa['pred_ids'][:,i])
unseen_esm_confs.append(pretrain_esm_msa['confs'][:,i])
unseen_esm_embeds.append(pretrain_esm_msa['embeds'][:,i])
unseen_esm_attention_mask.append(pretrain_esm_msa['attention_mask'][:,i])
if self.args.use_gearnet:
unseen_struct_embeds.append(pretrain_struct['embeds'][:,i])
if self.args.use_esmif:
unseen_esmif_embeds.append(pretrain_esmif['embeds'][i])
unseen_design_pred_ids = torch.stack(unseen_design_pred_ids)
unseen_design_confs = torch.stack(unseen_design_confs)
unseen_design_embeds = torch.stack(unseen_design_embeds)
unseen_design_attention_mask = torch.stack(unseen_design_attention_mask)
if self.args.use_LM:
unseen_esm_pred_ids = torch.stack(unseen_esm_pred_ids, dim=1)
unseen_esm_confs = torch.stack(unseen_esm_confs, dim=1)
unseen_esm_embeds = torch.stack(unseen_esm_embeds, dim=1)
unseen_esm_attention_mask = torch.stack(unseen_esm_attention_mask, dim=1)
if self.args.use_gearnet:
unseen_struct_embeds = torch.stack(unseen_struct_embeds, dim=1)
if self.args.use_esmif:
unseen_esmif_embeds = torch.stack(unseen_esmif_embeds, dim=0)
unseen_batch = {"pretrain_design":
{"pred_ids": unseen_design_pred_ids,
"confs":unseen_design_confs,
"embeds": unseen_design_embeds,
"attention_mask":unseen_design_attention_mask},
"h_E": torch.cat(h_E),
"E_idx": torch.cat(E_idx, dim=1),
"batch_id": torch.cat(batch_id),
"attention_mask":unseen_design_attention_mask
}
if self.args.use_LM:
unseen_batch["pretrain_esm_msa"]={"pred_ids": unseen_esm_pred_ids,
"confs":unseen_esm_confs,
"embeds": unseen_esm_embeds,
"attention_mask":unseen_esm_attention_mask}
if self.args.use_gearnet:
unseen_batch["pretrain_struct"] = {
"embeds":unseen_struct_embeds}
if self.args.use_esmif:
unseen_batch["pretrain_esmif"] = {"embeds":unseen_esmif_embeds}
return unseen_batch
def save2memory(self,keys,unseen,num_nodes, unseen_results):
# save to memory
for i in range(len(unseen)):
key = keys[unseen[i]]
num = num_nodes[unseen[i]]
self.memory[key] = {"pred_ids":unseen_results['pred_ids'][i][:num].detach().to('cpu'),
"confs":unseen_results['confs'][i][:num].detach().to('cpu'),
"embeds":unseen_results['embeds'][i][:num].detach().to('cpu'),
"probs":unseen_results['probs'][i][:num].detach().to('cpu'),
"log_probs":unseen_results['log_probs'][i][:num].detach().to('cpu'),
"attention_mask":unseen_results['attention_mask'][i][:num].detach().to('cpu')}
def update(self, unseen, num_nodes, unseen_results, keys):
# update
for i in range(len(unseen)):
num = num_nodes[unseen[i]]
self.out_pred_ids[unseen[i], :num] = unseen_results['pred_ids'][i][:num]
self.out_confs[unseen[i], :num] = unseen_results['confs'][i][:num]
self.out_embeds[unseen[i], :num] = unseen_results['embeds'][i][:num]
self.out_probs[unseen[i], :num] = unseen_results['probs'][i][:num]
self.out_log_probs[unseen[i], :num] = unseen_results['log_probs'][i][:num]
self.titles[unseen[i]] = keys[unseen[i]]
def forward(self, batch, use_memory=False):
self.use_memory = use_memory
pretrain_design, h_E_raw, E_idx_raw, mask_attend, batch_id_raw = batch['pretrain_design'] ,batch['h_E'], batch['E_idx'], batch['attention_mask'], batch['batch_id']
device = h_E_raw.device
pretrain_esm_msa = None
if self.args.use_LM:
pretrain_esm_msa = batch['pretrain_esm_msa']
pretrain_struct = None
if self.args.use_gearnet:
pretrain_struct = batch['pretrain_struct']
pretrain_esmif = None
if self.args.use_esmif:
pretrain_esmif = batch['esm_feat']
num_nodes = batch['attention_mask'].sum(dim=-1)
shift = torch.cat([torch.zeros(1, device=device), torch.cumsum(num_nodes, dim=0)]).long()
B, max_L = num_nodes.shape[0], num_nodes.max()
self.initoutput(pretrain_design, B, max_L, device)
# keys = list(zip(design_seqs, *lm_seqs))
keys = batch['title']
unseen = self.retrivel(keys, num_nodes,device, use_memory)
if len(unseen)>0:
unseen_batch = self.rebatch(unseen, batch_id_raw, E_idx_raw, h_E_raw, shift, num_nodes, pretrain_design, pretrain_esm_msa, pretrain_struct, pretrain_esmif, device)
unseen_results = self.GNNTuning(unseen_batch)
self.save2memory(keys,unseen,num_nodes, unseen_results)
self.update(unseen, num_nodes, unseen_results, keys)
return {'title':self.titles,'pred_ids':self.out_pred_ids, 'confs':self.out_confs, 'embeds':self.out_embeds, 'probs':self.out_probs, "log_probs":self.out_log_probs, 'attention_mask':pretrain_design['attention_mask']}
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