File size: 7,551 Bytes
aa45001 | 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 | from .paths import ROOT, BACKBONE, PREDICTOR, DIFFUSION, TRAIN, configure
configure()
import argparse
import numpy as np
import torch
from absl import flags
from transformers import AutoTokenizer, EsmModel
from datasets import load_from_disk
from .continuous import GaussianDiffusionSampler, DiffusionProteinModel
from .discrete import MultinomialDiffusion
from .unet import tabularUnet
from .predictor import MultitaskProteinModel
FLAGS = flags.FLAGS
METAL_LABELS = ['Ag','Cd','Co','Cu','Fe','Hg','Mn','Mo','Ni','Pb','Pt','V','W','Zn','NA']
TOKEN_DROPOUT_SCALE = 0.88
CODI_CKPT, PRED_CKPT, TRAIN_DS, ESM_NAME = map(str, (DIFFUSION,PREDICTOR,TRAIN,BACKBONE))
def build_hparams():
ns = argparse.Namespace()
ns.date = 'test'
ns.attempts = '1'
ns.output_model_path = str(ROOT/'runs')
ns.num_workers = 8
ns.num_linear_layers = 3
ns.batch_size = 5
ns.devices = 1
ns.special_prefix = None
ns.focal_gamma = 1
ns.lr = 1e-3
ns.eps = 0.4
ns.n_splits = 2
ns.epochs = 100
ns.save_top_k = 3
return ns
def define_flags():
if not FLAGS.is_parsed():
FLAGS([""])
d = {
'encoder_dim_con': ('string', "64,128,256"), 'encoder_dim_dis': ('string', "64,128,256"),
'nf_con': ('int', 16), 'nf_dis': ('int', 64),
'input_size': ('int', None), 'cond_size': ('int', None), 'output_size': ('int', None),
'activation': ('string', 'relu'), 'nf': ('int', None),
'hidden_size': ('int', 768), 'num_layers': ('int', 12), 'num_heads': ('int', 12),
'intermediate_size': ('int', 4 * 768), 'max_seq_length': ('int', 1280),
'T': ('int', 50), 'beta_1': ('float', 0.00001), 'beta_T': ('float', 0.02),
}
for name, (kind, default) in d.items():
if not hasattr(FLAGS, name):
{'string': flags.DEFINE_string, 'int': flags.DEFINE_integer,
'float': flags.DEFINE_float}[kind](name, default, help=name)
if not hasattr(FLAGS, 'encoder_dim'):
flags.DEFINE_multi_integer('encoder_dim', None, help='encoder_dim')
if not hasattr(FLAGS, 'mean_type'):
flags.DEFINE_enum('mean_type', 'epsilon', ['xprev', 'xstart', 'epsilon'], help='mean type')
if not hasattr(FLAGS, 'var_type'):
flags.DEFINE_enum('var_type', 'fixedsmall', ['fixedlarge', 'fixedsmall'], help='var type')
def load_diffusion_models(checkpoint_path, base_model, device):
ckpt = torch.load(checkpoint_path, map_location=device)
model_con_state = ckpt['model_con']
base_keys = [k for k in model_con_state if 'base_model' in k]
if base_keys:
base_state = {k.replace('base_model.', ''): model_con_state[k] for k in base_keys}
base_model.load_state_dict(base_state, strict=False)
print("Loaded base model weights from CoDi checkpoint")
def strip(sd):
return {k.replace('module.', ''): v for k, v in sd.items()}
if 'module.' in next(iter(ckpt['model_con'])):
ckpt['model_con'] = strip(ckpt['model_con'])
if 'module.' in next(iter(ckpt['model_dis'])):
ckpt['model_dis'] = strip(ckpt['model_dis'])
FLAGS.input_size, FLAGS.cond_size, FLAGS.output_size = 1280, 15, 1280
FLAGS.encoder_dim = [int(x) for x in FLAGS.encoder_dim_con.split(',')]
FLAGS.nf = FLAGS.nf_con
FLAGS.hidden_size, FLAGS.num_layers, FLAGS.num_heads = 768, 12, 12
FLAGS.intermediate_size, FLAGS.max_seq_length = 4 * 768, 1280
model_con = DiffusionProteinModel(base_model, FLAGS).to(device)
filtered = {}
for k, v in ckpt['model_con'].items():
if not k.startswith('base_model.'):
filtered[k] = v
else:
bk = k.replace('base_model.', '')
if bk in base_model.state_dict() and v.shape == base_model.state_dict()[bk].shape:
filtered[k] = v
model_con.load_state_dict(filtered, strict=False)
FLAGS.input_size, FLAGS.cond_size, FLAGS.output_size = 15, 1280, 15
FLAGS.encoder_dim = [int(x) for x in FLAGS.encoder_dim_dis.split(',')]
FLAGS.nf = FLAGS.nf_dis
model_dis = tabularUnet(FLAGS).to(device)
model_dis.load_state_dict(ckpt['model_dis'])
trainer_dis = MultinomialDiffusion(
num_classes=np.array([15]), shape=torch.Size([10, 15]), denoise_fn=model_dis,
FLAGS=FLAGS, timesteps=FLAGS.T, loss_type='vb_stochastic').to(device)
net_sampler = GaussianDiffusionSampler(
model_con, FLAGS.beta_1, FLAGS.beta_T, FLAGS.T,
mean_type=FLAGS.mean_type, var_type=FLAGS.var_type).to(device)
return model_con, model_dis, trainer_dis, net_sampler
class Sampler:
def __init__(self, cfg, device='cuda:0'):
self.cfg = cfg
self.device = torch.device(device)
define_flags()
self.tokenizer = AutoTokenizer.from_pretrained(ESM_NAME)
self.cys_id = self.tokenizer.convert_tokens_to_ids('C')
self.cls_id = self.tokenizer.cls_token_id
self.eos_id = self.tokenizer.eos_token_id
ds = load_from_disk(TRAIN_DS)
labels = np.stack([l for b in ds['anchor_labels'] for l in b])
cw = 1.0 / (labels.sum(axis=0) + 1e-6)
cw = torch.tensor(cw / cw.sum() * labels.shape[1], dtype=torch.float32).to(self.device)
hp = build_hparams()
esm = EsmModel.from_pretrained(ESM_NAME).cpu()
base_model = MultitaskProteinModel.load_from_checkpoint(
PRED_CKPT, hparams=hp, esm_model=esm, num_layers_to_finetune=2,
input_dim=1280, output_dim=15, class_weights=cw).cpu()
esm2 = EsmModel.from_pretrained(ESM_NAME).cpu()
self.predictor = MultitaskProteinModel.load_from_checkpoint(
PRED_CKPT, hparams=hp, esm_model=esm2, num_layers_to_finetune=2,
input_dim=1280, output_dim=15, class_weights=cw).to(self.device).eval()
self.model_con, self.model_dis, self.trainer_dis, self.net_sampler = \
load_diffusion_models(CODI_CKPT, base_model, self.device)
self.model_con.eval()
self.model_dis.eval()
# Gradients are needed only with respect to the latent.
for m in (self.predictor, self.model_con, self.model_dis):
for p in m.parameters():
p.requires_grad_(False)
self.W = self.predictor.esm_model.embeddings.word_embeddings.weight
self.threshold = self.predictor.classification_thresholds[cfg.target_idx].item()
self.vocab_mask = torch.full((33,), float('-inf'), device=self.device)
self.vocab_mask[4:24] = 0.0
self.aa_bias = torch.zeros(33, device=self.device)
self.aa_bias[self.cys_id] = cfg.cys_logit_bias
self.others = [i for i in range(15) if i != cfg.target_idx]
print(f"target={METAL_LABELS[cfg.target_idx]} idx={cfg.target_idx} "
f"threshold={self.threshold:.4f} cys_token={self.cys_id}")
@torch.no_grad()
def hard_decode(self, x, attn_len):
logits = self.model_con.decode_embeddings(x[:, :attn_len])
biased = logits + self.vocab_mask[None, None, :] + self.aa_bias[None, None, :]
toks = torch.argmax(biased, dim=-1)
return [self.tokenizer.decode(t, skip_special_tokens=True).replace(' ', '')
for t in toks]
@torch.no_grad()
def score(self, seqs):
enc = self.tokenizer(seqs, return_tensors='pt', padding=True)
enc = {k: v.to(self.device) for k, v in enc.items()}
probs, fires, _ = self.predictor.predict(input_ids=enc['input_ids'],
attention_masks=enc['attention_mask'])
return probs, fires
|