Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,803 Bytes
7968cb0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
import time
import torch
import torch.nn as nn
from .MemoryTuning import MemoTuning
import copy
from .MemoryESM import MemoESM
from .MemoryPiFold import MemoPiFold_model
from .MemoryESMIF import MemoESMIF
import torch
from torch_scatter import scatter_sum
def beam_search(post, k):
"""Beam Search Decoder
Parameters:
post(Tensor) – the posterior of network.
k(int) – beam size of decoder.
Outputs:
indices(Tensor) – a beam of index sequence.
log_prob(Tensor) – a beam of log likelihood of sequence.
Shape:
post: (batch_size, seq_length, vocab_size).
indices: (batch_size, beam_size, seq_length).
log_prob: (batch_size, beam_size).
Examples:
>>> post = torch.softmax(torch.randn([32, 20, 1000]), -1)
>>> indices, log_prob = beam_search_decoder(post, 3)
"""
batch_size, seq_length, token_size = post.shape
log_post = post.log()
log_prob, indices = log_post[:, 0, :].topk(k, sorted=True)
indices = indices.unsqueeze(-1)
for i in range(1, seq_length):
log_prob = log_prob.unsqueeze(-1) + log_post[:, i, :].unsqueeze(1).repeat(1, k, 1) # [batch, k, 33]
log_prob, index = log_prob.view(batch_size, -1).topk(k, sorted=True)
index = index%token_size
indices = torch.cat([indices, index.unsqueeze(-1)], dim=-1)
return indices, log_prob
class Design_Model(nn.Module):
def __init__(self, args, temporature, msa_n, tunning_layers_n, tunning_layers_dim, input_design_dim, input_esm_dim, tunning_dropout, design_model, LM_model, ESMIF_model, param_path=None):
super(Design_Model, self).__init__()
self.args = args
self.temporature = temporature
self.msa_n = msa_n
self.design_model = design_model
self.LM_model = LM_model
self.ESMIF_model = ESMIF_model
# self.GNNTuning = GNNTuning_Model(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.GNNTuning = MemoTuning(args, tunning_layers_n, tunning_layers_dim, input_design_dim, input_esm_dim, tunning_dropout, tokenizer=self.LM_model.tokenizer)
# self.Predictor = nn.Linear(tunning_layers_dim, 21)
self.conf_max = 0
self.patience = 0
self.best_params = None
self.confidence = []
if param_path is not None:
self.GNNTuning.load_state_dict(torch.load(param_path))
def get_MSA(self, pretrain_design):
pretrain_gnn_msa = pretrain_design
B, N = pretrain_gnn_msa['confs'].shape
probs, pred_ids, confs, attention_mask, titles = [], [], [], [], []
for m in range(self.msa_n):
titles.append(pretrain_design['title'])
probs.append(torch.softmax(pretrain_gnn_msa['probs']/self.temporature, dim=-1))
# pred_ids.append( msa_pred_ids[:,m,:])
pred_ids.append(torch.multinomial(probs[-1].reshape(-1,33), 1).reshape(B,N))
confs.append(probs[-1].reshape(-1,33)[torch.arange(pred_ids[-1].reshape(-1).shape[0]).cuda(),pred_ids[-1].reshape(-1)].reshape(B,N))
attention_mask.append(pretrain_gnn_msa['attention_mask'])
pretrain_esm_msa = {}
pretrain_esm_msa['title'] = sum(titles,[])
pretrain_esm_msa['probs'] = torch.cat(probs, dim=0)
pretrain_esm_msa['pred_ids'] = torch.cat(pred_ids, dim=0)
pretrain_esm_msa['confs'] = torch.cat(confs, dim=0)
pretrain_esm_msa['attention_mask'] = torch.cat(attention_mask, dim=0)
return pretrain_esm_msa
def forward(self, batch, design_memory=True, LM_memory=True, Struct_memory=True, Tuning_memory=True, ESMIF_memory=True):
'''
MemoPiFold: batch_id,titile, E_idx, h_V, h_E
MemoESM: pred_ids, attention_mask, confs
Tunning: pretrain_design
- pred_ids, confs, embeds
pretrain_esm_msa
- pred_ids, confs, embeds
h_E, E_idx, batch_id
'''
with torch.no_grad():
pretrain_design = self.design_model(batch, design_memory)
if self.args.use_LM:
# language model forward
pretrain_msa = self.get_MSA(pretrain_design)
pretrain_esm_msa = self.LM_model(pretrain_msa, LM_memory)
B, N = pretrain_design['confs'].shape
pretrain_esm_msa['embeds'] = pretrain_esm_msa['embeds'].reshape(self.msa_n, B, N, -1)
pretrain_esm_msa['pred_ids'] = pretrain_esm_msa['pred_ids'].reshape(self.msa_n, B, N)
pretrain_esm_msa['confs'] = pretrain_esm_msa['confs'].reshape(self.msa_n, B, N)
pretrain_esm_msa['attention_mask'] = pretrain_esm_msa['attention_mask'].reshape(self.msa_n, B, N)
if self.args.use_gearnet:
# structure model forward
pretrain_msa = self.get_MSA(pretrain_design)
protein_seqs_msa = self.LM_model.tokenizer.decode(pretrain_msa['pred_ids'][pretrain_msa['attention_mask']], clean_up_tokenization_spaces=False).split(" ")
protein_coords_msa = batch['position'][:,1,:].repeat((self.msa_n,1))
num_nodes = pretrain_msa['attention_mask'].sum(dim=1)
msa_id = torch.arange(self.msa_n, device=num_nodes.device).repeat_interleave(pretrain_design['attention_mask'].shape[0])
pretrain_struct_msa = self.Struct_model(protein_seqs_msa, protein_coords_msa, num_nodes, msa_id, pretrain_msa['title'], Struct_memory)
if self.args.use_esmif:
# esmif model forward
esm_feat = self.ESMIF_model(batch, ESMIF_memory)
new_batch = {}
new_batch['title'] = pretrain_design['title']
new_batch['pretrain_design'] = pretrain_design
new_batch['h_E'] = batch['h_E']
new_batch['E_idx'] = batch['E_idx']
new_batch['batch_id'] = batch['batch_id']
new_batch['attention_mask'] = pretrain_design['attention_mask']
if self.args.use_LM:
new_batch['pretrain_esm_msa'] = pretrain_esm_msa
if self.args.use_gearnet:
new_batch['pretrain_struct'] = pretrain_struct_msa
if self.args.use_esmif:
new_batch['esm_feat'] = esm_feat
results = self.GNNTuning(new_batch, Tuning_memory)
avg_confs = (results['attention_mask']*results['confs']).sum(dim=1)/results['attention_mask'].sum(dim=1)
self.confidence.append(avg_confs)
return results
class KWDesign_model(nn.Module):
def __init__(self, args):
super(KWDesign_model, self).__init__()
self.args = args
input_design_dim, input_esm_dim = args.input_design_dim, args.input_esm_dim
tunning_layers_dim = args.tunning_layers_dim
self.memo_pifold = MemoPiFold_model(args)
self.memo_esmif = MemoESMIF()
# if args.load_memory:
# memory = torch.load(args.memory_path)
# self.memo_pifold = memory['memo_pifold']
# self.memo_esmif = memory['memo_esmif']
for i in range(1, self.args.recycle_n+1):
if i==1:
self.register_module(f"Design{i}",
Design_Model(args, args.temporature, args.msa_n, args.tunning_layers_n, args.tunning_layers_dim, input_design_dim, input_esm_dim, args.tunning_dropout, self.memo_pifold, MemoESM(args), self.memo_esmif))
else:
self.register_module(f"Design{i}",
Design_Model(args, args.temporature, args.msa_n, args.tunning_layers_n, args.tunning_layers_dim, tunning_layers_dim, input_esm_dim, args.tunning_dropout, self.get_submodule(f"Design{i-1}"), MemoESM(args), self.memo_esmif))
def update(self, batch, node_nums, conf, results, log_probs_mat, threshold, current_batch_id):
fix_mask = conf>threshold
log_probs_mat[current_batch_id[fix_mask]] = results['log_probs'][fix_mask]
current_batch_id = current_batch_id[conf<=threshold]
batch_id_old = batch['batch_id']
batch_id_old2new = torch.zeros_like(batch_id_old)-1
batch_id_old2new[current_batch_id] = torch.arange(current_batch_id.shape[0], device=conf.device)
node_mask = (batch_id_old.view(-1,1) == current_batch_id).any(dim=1)
edge_mask = node_mask[batch['E_idx'][0]]
shift_old = torch.cat([torch.zeros(1, device=node_nums.device),node_nums.cumsum(dim=0)]).long()
shift_new = torch.cat([torch.zeros(1, device=node_nums.device),node_nums[current_batch_id].cumsum(dim=0)]).long()
edge_batch_id = batch_id_old[batch['E_idx'][0]]
E_idx = (batch['E_idx'] - shift_old[edge_batch_id] + shift_new[batch_id_old2new[edge_batch_id]])[:,edge_mask]
new_batch = {"title": [batch['title'][int(idx)] for idx in current_batch_id],
"h_V": batch['h_V'][node_mask],
"h_E": batch['h_E'][edge_mask],
"E_idx": E_idx,
"batch_id": batch_id_old2new[batch_id_old[node_mask]],
"alphabet": batch["alphabet"],
"S": batch["S"],
"position": batch["position"]}
return new_batch, log_probs_mat, current_batch_id
def forward(self, batch):
mask_select_feat = lambda x, mask_attend: torch.masked_select(x, mask_attend.bool().unsqueeze(-1)).reshape(-1,x.shape[-1])
log_probs_list, confs_list = [], []
for i in range(1, self.args.recycle_n+1):
module = self.get_submodule(f"Design{i}")
if i< self.args.recycle_n:
results = module(batch, Tuning_memory=True)
else:
results = module(batch, Tuning_memory=False)
log_probs = mask_select_feat(results['log_probs'], results['attention_mask'])
log_probs_list.append(log_probs)
confs = mask_select_feat(results['confs'][:,:,None], results['attention_mask'])
confs_list.append(confs)
max_conf_idx = torch.cat(confs_list, dim=1).argmax(dim=1)
log_probs_mat = torch.stack(log_probs_list)
log_probs = log_probs_mat[max_conf_idx, torch.arange(max_conf_idx.shape[0], device=max_conf_idx.device)]
outputs = {f"log_probs{i+1}": log_probs_list[i] for i in range(len(log_probs_list))}
outputs["log_probs"]=log_probs
return outputs
|