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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers import AutoTokenizer
from torch.optim import AdamW
import numpy as np
from sklearn.metrics import f1_score
from typing import Tuple
import lightning.pytorch as pl
import gc
from torch.optim.lr_scheduler import _LRScheduler
from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, matthews_corrcoef, precision_score, recall_score
class GELU(nn.Module):
def forward(self, x):
return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
class FocalLoss(nn.Module):
"""Focal loss.
Sources:
https://github.com/fcakyon/balanced-loss/blob/main/balanced_loss/losses.py
https://github.com/vandit15/Class-balanced-loss-pytorch/blob/master/class_balanced_loss.py
"""
def __init__(self, gamma=2):
super(FocalLoss, self).__init__()
self.gamma = gamma
def forward(self, inputs, targets, sample_weights):
BCLoss = F.binary_cross_entropy_with_logits(input = inputs, target = targets,reduction = "none")
if self.gamma == 0.0:
modulator = 1.0
else:
modulator = torch.exp(-self.gamma * targets * inputs - self.gamma * torch.log(1 +
torch.exp(-1.0 * inputs)))
loss = modulator * BCLoss
weighted_loss = sample_weights * loss
focal_loss = torch.sum(weighted_loss)
focal_loss /= torch.sum(targets)
return focal_loss
class F1Loss(nn.Module):
def __init__(self, epsilon=1e-7):
super().__init__()
self.epsilon = epsilon
def forward(self, y_pred, y_true):
assert y_pred.ndim == 2
assert y_true.ndim == 2
y_true = y_true.float()
y_pred = torch.sigmoid(y_pred)
tp = (y_true * y_pred).sum(dim=0)
fp = ((1 - y_true) * y_pred).sum(dim=0)
fn = (y_true * (1 - y_pred)).sum(dim=0)
precision = tp / (tp + fp + self.epsilon)
recall = tp / (tp + fn + self.epsilon)
f1 = 2 * (precision * recall) / (precision + recall + self.epsilon)
f1 = f1.clamp(min=self.epsilon, max=1-self.epsilon)
return 1 - f1.mean()
def rotate_half(x):
x1, x2 = x.chunk(2, dim=-1)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(x, cos, sin):
# x: (B, L, H, D); cos and sin: (1, L, D).
cos = cos.unsqueeze(2)
sin = sin.unsqueeze(2)
return (x * cos) + (rotate_half(x) * sin)
class RotaryEmbedding(torch.nn.Module):
"""Rotary position embeddings (Su et al.), inspired by GPT-NeoX.
https://arxiv.org/abs/2104.09864
https://github.com/ZhuiyiTechnology/roformer
https://github.com/EleutherAI/gpt-neox
"""
def __init__(self, dim: int, *_, **__):
super().__init__()
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self._seq_len_cached = None
self._cos_cached = None
self._sin_cached = None
def _update_cos_sin_tables(self, x, seq_dimension=1):
seq_len = x.shape[seq_dimension]
if seq_len != self._seq_len_cached or self._cos_cached.device != x.device:
self._seq_len_cached = seq_len
t = torch.arange(x.shape[seq_dimension], device=x.device).type_as(self.inv_freq)
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self._cos_cached = emb.cos()[None, :, :]
self._sin_cached = emb.sin()[None, :, :]
return self._cos_cached, self._sin_cached
def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
self._cos_cached, self._sin_cached = self._update_cos_sin_tables(k)
return (
apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
)
def macro_f1(y_true, y_pred, thresholds):
y_pred_binary = (y_pred >= thresholds).float()
tp = (y_true * y_pred_binary).sum(dim=0)
fp = ((1 - y_true) * y_pred_binary).sum(dim=0)
fn = (y_true * (1 - y_pred_binary)).sum(dim=0)
precision = tp / (tp + fp + 1e-7)
recall = tp / (tp + fn + 1e-7)
f1 = 2 * precision * recall / (precision + recall + 1e-7)
macro_f1 = f1.mean()
return macro_f1
def safeguard_softmax(logits, dim=-1):
max_logits, _ = logits.max(dim=dim, keepdim=True)
exp_logits = torch.exp(logits - max_logits)
exp_sum = exp_logits.sum(dim=dim, keepdim=True)
probs = exp_logits / (exp_sum + 1e-7)
return probs
class PositionalAttentionHead(nn.Module):
def __init__(self, hidden_dim, n_heads):
super(PositionalAttentionHead, self).__init__()
self.n_heads = n_heads
self.hidden_dim = hidden_dim
self.head_dim = hidden_dim // n_heads
self.preattn_ln = nn.LayerNorm(self.head_dim)
self.Q = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.K = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.V = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.rot_emb = RotaryEmbedding(self.head_dim)
def forward(self, x, attention_mask):
batch_size, seq_len, _ = x.size()
x = x.view(batch_size, seq_len, self.n_heads, self.head_dim)
x = self.preattn_ln(x)
q = self.Q(x)
k = self.K(x)
v = self.V(x)
q, k = self.rot_emb(q, k)
gc.collect()
torch.cuda.empty_cache()
attn_scores = torch.einsum('bqhd,bkhd->bhqk', q, k) / math.sqrt(self.head_dim)
attn_scores = attn_scores.masked_fill(torch.logical_not(attention_mask.unsqueeze(1).unsqueeze(1)), float("-inf"))
attn_probs = safeguard_softmax(attn_scores, dim=-1)
x = torch.einsum('bhqk,bkhd->bqhd', attn_probs, v)
x = x.reshape(batch_size, seq_len, self.hidden_dim)
gc.collect()
torch.cuda.empty_cache()
return x, attn_probs
class CosineAnnealingWithWarmup(_LRScheduler):
def __init__(self, optimizer, warmup_steps, total_steps, eta_ratio=0.1, last_epoch=-1):
self.warmup_steps = warmup_steps
self.total_steps = total_steps
self.eta_ratio = eta_ratio
super(CosineAnnealingWithWarmup, self).__init__(optimizer, last_epoch)
def get_lr(self):
if self.last_epoch < self.warmup_steps:
return [base_lr * self.last_epoch / self.warmup_steps for base_lr in self.base_lrs]
progress = (self.last_epoch - self.warmup_steps) / (self.total_steps - self.warmup_steps)
cosine_decay = 0.5 * (1 + np.cos(np.pi * progress))
decayed_lr = (1 - self.eta_ratio) * cosine_decay + self.eta_ratio
return [decayed_lr * base_lr for base_lr in self.base_lrs]
class AdaptiveMarginTripletLoss(nn.Module):
def __init__(self, initial_margin=0.05, scale_factor=0.1, min_margin=0.05, max_margin=2.0):
super(AdaptiveMarginTripletLoss, self).__init__()
self.initial_margin = initial_margin
self.scale_factor = scale_factor
self.min_margin = min_margin
self.max_margin = max_margin
def forward(self, anchor, positive, negative):
pos_dist = F.pairwise_distance(anchor, positive, p=2)
neg_dist = F.pairwise_distance(anchor, negative, p=2)
adaptive_margins = self.initial_margin + self.scale_factor * (neg_dist - pos_dist)
adaptive_margins = torch.clamp(adaptive_margins, min=self.min_margin, max=self.max_margin)
losses = F.relu(pos_dist - neg_dist + adaptive_margins)
return losses.mean()
class RobertaLMHead(nn.Module):
def __init__(self, embed_dim, output_dim, weight):
super().__init__()
self.dense = nn.Linear(embed_dim, embed_dim)
self.layer_norm = nn.LayerNorm(embed_dim)
self.weight = weight
self.gelu = GELU()
self.bias = nn.Parameter(torch.zeros(output_dim))
def forward(self, features):
x = self.dense(features)
x = self.gelu(x)
x = self.layer_norm(x)
x = F.linear(x, self.weight) + self.bias
return x
def pad_to_max_length(tensor, max_length, pad_value=1):
return F.pad(tensor, (0, max_length - tensor.shape[1]), value=pad_value)
def compute_ece(probs, labels, n_bins=10):
"""Per-class calibration error for probabilities and labels of shape (B, C)."""
bin_boundaries = torch.linspace(0, 1, n_bins + 1)
bin_lowers = bin_boundaries[:-1]
bin_uppers = bin_boundaries[1:]
n_classes = probs.size(1)
ece_per_class = torch.zeros(n_classes, device=probs.device)
for class_idx in range(n_classes):
class_probs = probs[:, class_idx]
class_labels = labels[:, class_idx]
ece = torch.zeros(1, device=probs.device)
for bin_lower, bin_upper in zip(bin_lowers, bin_uppers):
in_bin = (class_probs.gt(bin_lower.item()) * class_probs.le(bin_upper.item())).float()
prop_in_bin = in_bin.mean()
if prop_in_bin > 0:
accuracy_in_bin = (class_labels * in_bin).sum() / in_bin.sum()
avg_confidence_in_bin = (class_probs * in_bin).sum() / in_bin.sum()
ece += torch.abs(avg_confidence_in_bin - accuracy_in_bin) * prop_in_bin
ece_per_class[class_idx] = ece.item()
return ece_per_class
class MultitaskProteinModel(pl.LightningModule):
def __init__(self, hparams, esm_model, num_layers_to_finetune, input_dim=1280, output_dim=12, class_weights=None, hidden_dim=512, n_heads=10):
super(MultitaskProteinModel, self).__init__()
self.save_hyperparameters(hparams)
self.esm_model = esm_model
for param in self.esm_model.parameters():
param.requires_grad = False
for i in range(num_layers_to_finetune):
for param in self.esm_model.encoder.layer[-i-1].parameters():
param.requires_grad = True
self.lm_head = RobertaLMHead(embed_dim = 1280, output_dim=33, weight=self.esm_model.embeddings.word_embeddings.weight)
self.attn_head = PositionalAttentionHead(input_dim, n_heads)
self.attn_ln = nn.LayerNorm(input_dim)
self.attn_skip = nn.Linear(input_dim, input_dim)
self.linear_layers = nn.ModuleList()
for _ in range(self.hparams.num_linear_layers):
self.linear_layers.append(nn.Linear(input_dim, input_dim))
self.reduction_layers = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
GELU(),
nn.Linear(hidden_dim, output_dim)
)
self.clf_ln = nn.LayerNorm(input_dim)
self.class_weights = class_weights
self.classification_thresholds = nn.Parameter(torch.tensor([0.5]*output_dim))
self.historical_memory = 0.9
self.training_step_outputs = []
self.training_step_true_labels = []
self.testing_step_predictions = []
self.testing_step_labels = []
self.test_dataloader = None
self.tokenizer = AutoTokenizer.from_pretrained(str(BACKBONE), local_files_only=True)
self.corruption_epoch_threshold = 5
def setup(self, stage=None):
self.__build_loss()
def __build_loss(self):
self.contrastive_loss = AdaptiveMarginTripletLoss()
focal_gamma = torch.tensor(self.hparams.focal_gamma).to(self.device)
self.class_weights = self.class_weights.to(self.device)
self.focal_loss = FocalLoss(gamma=focal_gamma)
self.f1_loss = F1Loss()
self.reconstruction_loss = nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id)
def forward(self, input_ids, attention_masks=None):
outputs = self.esm_model(input_ids=input_ids, attention_mask=attention_masks, output_hidden_states=True)
embeddings = outputs.last_hidden_state
attention_masks = attention_masks
x_pool, x_attns = self.attn_head(embeddings, attention_masks)
x_pool = self.attn_ln(x_pool + self.attn_skip(x_pool))
for linear_layer in self.linear_layers:
residue = x_pool
x_pool = linear_layer(x_pool)
x_pool = F.silu(x_pool)
x_pool = x_pool + residue
x_weighted = torch.einsum('bhlk,bld->bhld', x_attns, x_pool)
x_combined = x_weighted.mean(dim=1)
x_combined = self.clf_ln(x_combined)
mlm_logits = self.lm_head(x_combined)
attention_masks = attention_masks.unsqueeze(-1).float()
attention_sum = attention_masks.sum(dim=1, keepdim=True)
x_combined_masked = (x_combined * attention_masks).sum(dim=1) / attention_sum.squeeze(1)
x_pred = self.reduction_layers(x_combined_masked)
gc.collect()
torch.cuda.empty_cache()
return x_pred, x_attns, x_combined_masked, mlm_logits
def forward_from_embeds(self, inputs_embeds, attention_masks=None):
esm = self.esm_model
# Disable token dropout for embedding input.
if getattr(esm.embeddings, "token_dropout", False):
esm.embeddings.token_dropout = False
outs = esm(
inputs_embeds=inputs_embeds,
attention_mask=attention_masks.to(torch.long),
output_hidden_states=True,
)
embeddings = outs.last_hidden_state
x_pool, x_attns = self.attn_head(embeddings, attention_masks)
x_pool = self.attn_ln(x_pool + self.attn_skip(x_pool))
for linear_layer in self.linear_layers:
residue = x_pool
x_pool = F.silu(linear_layer(x_pool))
x_pool = x_pool + residue
x_weighted = torch.einsum('bhlk,bld->bhld', x_attns, x_pool).mean(dim=1)
x_weighted = self.clf_ln(x_weighted)
mask_f = attention_masks.unsqueeze(-1).float()
x_combined_masked = (x_weighted * mask_f).sum(dim=1) / mask_f.sum(dim=1).clamp_min(1e-6)
x_pred = self.reduction_layers(x_combined_masked)
return x_pred, x_attns, x_combined_masked, None
def predict(self, input_ids, attention_masks=None):
x_pred, _, _, _ = self.forward(input_ids, attention_masks)
classification_output = torch.sigmoid(x_pred)
predictions = (classification_output >= self.classification_thresholds).float()
for i, pred in enumerate(predictions):
if pred.sum() == 0:
weighted_probs = classification_output[i]
max_class = torch.argmax(weighted_probs)
predictions[i, max_class] = 1.0
confidence_scores = torch.abs(classification_output - self.classification_thresholds) / self.classification_thresholds
max_confidence_scores = confidence_scores.max(dim=1).values
return classification_output, predictions, max_confidence_scores
def training_step(self, batch, batch_idx):
anchor_input_ids, anchor_attention_masks = batch['anchor_input_ids'], batch['anchor_attention_mask']
positive_input_ids, positive_attention_masks = batch['positive_input_ids'], batch['positive_attention_mask']
negative_input_ids, negative_attention_masks = batch['negative_input_ids'], batch['negative_attention_mask']
anchor_labels = batch['anchor_labels']
positive_labels = batch['positive_labels']
negative_labels = batch['negative_labels']
anchor_x_pred, _, anchor_x_combined, mlm_logits = self.forward(anchor_input_ids, anchor_attention_masks)
positive_x_pred, _, positive_x_combined, _ = self.forward(positive_input_ids, positive_attention_masks)
negative_x_pred, _, negative_x_combined, _ = self.forward(negative_input_ids, negative_attention_masks)
reconstruction_loss = self.reconstruction_loss(mlm_logits.view(-1, mlm_logits.size(-1)), anchor_input_ids.view(-1))
anchor_focal_loss = self.focal_loss(anchor_x_pred, anchor_labels, self.class_weights)
positive_focal_loss = self.focal_loss(positive_x_pred, positive_labels, self.class_weights)
semi_hard_negative_ce_loss = F.binary_cross_entropy_with_logits(input=negative_x_pred, target=negative_labels)
focal_loss = (anchor_focal_loss + positive_focal_loss) / 2
anchor_f1_loss = self.f1_loss(anchor_x_pred, anchor_labels)
positive_f1_loss = self.f1_loss(positive_x_pred, positive_labels)
semi_hard_f1_loss = self.f1_loss(negative_x_pred, negative_labels)
semi_hard_triplet_loss = self.contrastive_loss(anchor_x_combined, positive_x_combined, negative_x_combined)
if self.current_epoch > self.corruption_epoch_threshold:
hard_negative_input_ids, hard_negative_attention_masks = batch['hard_negative_input_ids'], batch['hard_negative_attention_mask']
hard_negative_labels = batch['hard_negative_labels']
if self.current_epoch < 15:
epoch_ratio = min(1.0, (self.current_epoch - self.corruption_epoch_threshold) / (self.hparams.epochs - self.corruption_epoch_threshold))
hard_weight = epoch_ratio
semi_hard_weight = 1 - hard_weight
else:
semi_hard_weight = 0.0
hard_weight = 1.0
hard_negative_x_pred, _, hard_negative_x_combined, _ = self.forward(hard_negative_input_ids, hard_negative_attention_masks)
hard_negative_ce_loss = F.binary_cross_entropy_with_logits(input=hard_negative_x_pred, target=hard_negative_labels)
hard_triplet_loss = self.contrastive_loss(anchor_x_combined, positive_x_combined, hard_negative_x_combined)
hard_f1_loss = self.f1_loss(hard_negative_x_pred, hard_negative_labels)
negative_ce_loss = semi_hard_weight * semi_hard_negative_ce_loss + hard_weight * hard_negative_ce_loss
triplet_loss = semi_hard_weight * semi_hard_triplet_loss + hard_weight * hard_triplet_loss
f1_loss = anchor_f1_loss + positive_f1_loss + semi_hard_weight * semi_hard_triplet_loss + hard_weight * hard_f1_loss
else:
negative_ce_loss = semi_hard_negative_ce_loss
triplet_loss = semi_hard_triplet_loss
f1_loss = anchor_f1_loss + positive_f1_loss + semi_hard_f1_loss
loss = focal_loss * 2 + negative_ce_loss + f1_loss + triplet_loss * 2 + reconstruction_loss
self.training_step_outputs.append(anchor_x_pred)
self.training_step_outputs.append(positive_x_pred)
self.training_step_outputs.append(negative_x_pred)
self.training_step_true_labels.append(anchor_labels)
self.training_step_true_labels.append(positive_labels)
self.training_step_true_labels.append(negative_labels)
macro_f1_val = macro_f1(anchor_labels, torch.sigmoid(anchor_x_pred), self.classification_thresholds)
self.log('train_macro_f1', macro_f1_val, on_step=True, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
gc.collect()
torch.cuda.empty_cache()
gc.collect()
torch.cuda.empty_cache()
self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
return loss
def on_train_epoch_end(self):
epoch_outputs = torch.cat(self.training_step_outputs, dim=0)
epoch_true_labels = torch.cat(self.training_step_true_labels, dim=0)
epoch_outputs = torch.sigmoid(epoch_outputs)
self.update_class_thresholds(epoch_outputs, epoch_true_labels)
threshold_dict = {f"threshold_{i}": self.classification_thresholds[i].item() for i in range(15)}
self.log_dict(threshold_dict, on_epoch=True)
self.training_step_outputs.clear()
self.training_step_true_labels.clear()
gc.collect()
torch.cuda.empty_cache()
def validation_step(self, batch, batch_idx):
anchor_input_ids, anchor_attention_masks = batch['anchor_input_ids'], batch['anchor_attention_mask']
positive_input_ids, positive_attention_masks = batch['positive_input_ids'], batch['positive_attention_mask']
negative_input_ids, negative_attention_masks = batch['negative_input_ids'], batch['negative_attention_mask']
anchor_labels = batch['anchor_labels']
positive_labels = batch['positive_labels']
negative_labels = batch['negative_labels']
anchor_x_pred, _, anchor_x_combined, mlm_logits = self.forward(anchor_input_ids, anchor_attention_masks)
positive_x_pred, _, positive_x_combined, _ = self.forward(positive_input_ids, positive_attention_masks)
negative_x_pred, _, negative_x_combined, _ = self.forward(negative_input_ids, negative_attention_masks)
reconstruction_loss = self.reconstruction_loss(mlm_logits.view(-1, mlm_logits.size(-1)), anchor_input_ids.view(-1))
anchor_focal_loss = self.focal_loss(anchor_x_pred, anchor_labels , self.class_weights)
positive_focal_loss = self.focal_loss(positive_x_pred, positive_labels, self.class_weights)
negative_ce_loss = F.binary_cross_entropy_with_logits(input=negative_x_pred, target=negative_labels)
focal_loss = (anchor_focal_loss + positive_focal_loss) / 2
f1_loss = self.f1_loss(anchor_x_pred, anchor_labels)
triplet_loss = self.contrastive_loss(anchor_x_combined, positive_x_combined, negative_x_combined)
loss = focal_loss * 2 + negative_ce_loss + f1_loss + triplet_loss * 2 + reconstruction_loss
macro_f1_val = macro_f1(anchor_labels, torch.sigmoid(anchor_x_pred), self.classification_thresholds)
self.log('val_macro_f1', macro_f1_val, on_step=True, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
gc.collect()
torch.cuda.empty_cache()
self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True, sync_dist=True)
def test_step(self, batch, batch_idx):
input_ids, labels, attention_masks = batch['input_ids'], batch['labels'], batch['attention_masks']
x_pred, _, _ = self.forward_test(input_ids, attention_masks)
predictions = torch.sigmoid(x_pred)
self.testing_step_predictions.append(predictions)
self.testing_step_labels.append(labels)
def on_test_epoch_end(self,):
predictions = torch.cat(self.testing_step_predictions, dim=0)
labels = torch.cat(self.testing_step_labels, dim=0)
predictions_binary = (predictions >= self.classification_thresholds).float().cpu().numpy()
labels_binary = labels.cpu().numpy()
f1 = f1_score(labels_binary, predictions_binary, average='macro')
roc_auc = roc_auc_score(labels_binary, predictions.cpu().to(torch.float32).numpy(), average='micro')
accuracy = accuracy_score(labels_binary.flatten(), predictions_binary.flatten())
mcc = matthews_corrcoef(labels_binary.flatten(), predictions_binary.flatten())
precision = precision_score(labels_binary, predictions_binary, average='macro')
recall = recall_score(labels_binary, predictions_binary, average='macro')
results = {
'test_F1_macro': f1,
'test_AUCROC': roc_auc,
'test_Accuracy': accuracy,
'test_MCC': mcc,
'test_Precision': precision,
'test_Recall': recall,
}
self.log_dict(results)
self.testing_step_predictions.clear()
self.testing_step_labels.clear()
gc.collect()
torch.cuda.empty_cache()
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.hparams.lr, betas=(0.9, 0.95), weight_decay=1e-5)
schedulers = CosineAnnealingWithWarmup(optimizer, warmup_steps=100, eta_ratio=0.1, total_steps=9000)
lr_schedulers = {
"scheduler": schedulers,
"name": 'learning_rate_logs',
"interval": 'step',
'frequency': 1
}
return [optimizer], [lr_schedulers]
def update_class_thresholds(self, inputs, targets):
with torch.no_grad():
min_threshold_value = 0.001
thresholds = torch.linspace(0.0, 1.0, steps=1000, device=inputs.device)
num_classes = targets.size(1)
best_f1_scores = torch.zeros(num_classes, device=inputs.device)
best_thresholds = torch.full((num_classes,), min_threshold_value, device=inputs.device)
for threshold in thresholds:
binary_predictions = (inputs >= threshold).float()
tp = (binary_predictions * targets).sum(dim=0)
fp = (binary_predictions * (1 - targets)).sum(dim=0)
fn = ((1 - binary_predictions) * targets).sum(dim=0)
precision = tp / (tp + fp + 1e-7)
recall = tp / (tp + fn + 1e-7)
f1_scores = 2 * precision * recall / (precision + recall + 1e-7)
is_better = f1_scores > best_f1_scores
best_f1_scores = torch.where(is_better, f1_scores, best_f1_scores)
best_thresholds = torch.where(is_better, threshold.expand_as(best_f1_scores), best_thresholds)
updated_thresholds = self.historical_memory * self.classification_thresholds.data + (1 - self.historical_memory) * best_thresholds
self.classification_thresholds = nn.Parameter(torch.clamp(updated_thresholds, min=min_threshold_value))
gc.collect()
torch.cuda.empty_cache()
class ResidueEncoder(MultitaskProteinModel):
"""Return per-residue clf_ln outputs for diffusion training."""
def forward(self, input_ids, attention_masks=None):
captured = []
hook = self.clf_ln.register_forward_hook(lambda module, inputs, output: captured.append(output))
try:
logits, attention, pooled, mlm_logits = super().forward(input_ids, attention_masks)
finally:
hook.remove()
return logits, attention, captured[0], mlm_logits
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