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import torch
import gc
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple
from .pooling import Attention1dPoolingHead, MeanPoolingHead, LightAttentionPoolingHead
from .pooling import MeanPooling, MeanPoolingProjection
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):
cos = cos[:, :, : x.shape[-2], :]
sin = sin[:, :, : x.shape[-2], :]
return (x * cos) + (rotate_half(x) * sin)
class RotaryEmbedding(nn.Module):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int):
super().__init__()
# Generate and save the inverse frequency buffer (non trainable)
inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim))
inv_freq = inv_freq
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=2):
seq_len = x.shape[seq_dimension]
# Reset the tables if the sequence length has changed,
# or if we're on a new device (possibly due to tracing for instance)
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.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self._cos_cached = emb.cos()[None, None, :, :]
self._sin_cached = emb.sin()[None, 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, seq_dimension=-2)
return (
apply_rotary_pos_emb(q, self._cos_cached, self._sin_cached),
apply_rotary_pos_emb(k, self._cos_cached, self._sin_cached),
)
class CrossModalAttention(nn.Module):
def __init__(self, args):
super().__init__()
self.attention_head_size = args.hidden_size // args.num_attention_head
assert (
self.attention_head_size * args.num_attention_head == args.hidden_size
), "Embed size needs to be divisible by num heads"
self.num_attention_head = args.num_attention_head
self.hidden_size = args.hidden_size
self.query_proj = nn.Linear(args.hidden_size, args.hidden_size)
self.key_proj = nn.Linear(args.hidden_size, args.hidden_size)
self.value_proj = nn.Linear(args.hidden_size, args.hidden_size)
self.dropout = nn.Dropout(args.attention_probs_dropout)
self.out_proj = nn.Linear(args.hidden_size, args.hidden_size)
self.rotary_embeddings = RotaryEmbedding(dim=self.attention_head_size)
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_head, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, query, key, value, attention_mask=None, output_attentions=False):
key_layer = self.transpose_for_scores(self.key_proj(key))
value_layer = self.transpose_for_scores(self.value_proj(value))
query_layer = self.transpose_for_scores(self.query_proj(query))
query_layer = query_layer * self.attention_head_size**-0.5
query_layer, key_layer = self.rotary_embeddings(query_layer, key_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if attention_mask is not None:
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_scores = attention_scores.masked_fill(attention_mask == 0, float('-inf'))
attention_probs = F.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)
context_layer = context_layer.view(new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else context_layer
return outputs
class AdapterModel(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
if 'foldseek_seq' in args.structure_seq:
self.foldseek_embedding = nn.Embedding(args.vocab_size, args.hidden_size)
self.cross_attention_foldseek = CrossModalAttention(args)
if 'ss8_seq' in args.structure_seq:
self.ss_embedding = nn.Embedding(args.vocab_size, args.hidden_size)
self.cross_attention_ss = CrossModalAttention(args)
if 'esm3_structure_seq' in args.structure_seq:
self.esm3_structure_embedding = nn.Embedding(args.vocab_size, args.hidden_size)
self.cross_attention_esm3_structure = CrossModalAttention(args)
self.layer_norm = nn.LayerNorm(args.hidden_size)
if args.pooling_method == 'attention1d':
self.classifier = Attention1dPoolingHead(args.hidden_size, args.num_labels, args.pooling_dropout)
elif args.pooling_method == 'mean':
if "PPI" in args.dataset:
self.pooling = MeanPooling()
self.projection = MeanPoolingProjection(args.hidden_size, args.num_labels, args.pooling_dropout)
else:
self.classifier = MeanPoolingHead(args.hidden_size, args.num_labels, args.pooling_dropout)
elif args.pooling_method == 'light_attention':
self.classifier = LightAttentionPoolingHead(args.hidden_size, args.num_labels, args.pooling_dropout)
else:
raise ValueError(f"classifier method {args.pooling_method} not supported")
def plm_embedding(self, plm_model, aa_seq, attention_mask, structure_tokens=None):
with torch.no_grad():
if "ProSST" in self.args.plm_model:
outputs = plm_model(input_ids=aa_seq, attention_mask=attention_mask, ss_input_ids=structure_tokens, output_hidden_states=True)
elif "Prime" in self.args.plm_model or "deep" in self.args.plm_model:
outputs = plm_model(input_ids=aa_seq, attention_mask=attention_mask, output_hidden_states=True)
elif self.training and hasattr(self, 'args') and self.args.training_method == 'full':
outputs = plm_model(input_ids=aa_seq, attention_mask=attention_mask)
else:
outputs = plm_model(input_ids=aa_seq, attention_mask=attention_mask)
if "ProSST" in self.args.plm_model or "Prime" in self.args.plm_model:
seq_embeds = outputs.hidden_states[-1]
else:
seq_embeds = outputs.last_hidden_state
gc.collect()
torch.cuda.empty_cache()
return seq_embeds
def forward(self, plm_model, batch):
if "ProSST" in self.args.plm_model:
aa_seq, attention_mask, stru_tokens = batch['aa_seq_input_ids'], batch['aa_seq_attention_mask'], batch['aa_seq_stru_tokens']
seq_embeds = self.plm_embedding(plm_model, aa_seq, attention_mask, stru_tokens)
else:
aa_seq, attention_mask = batch['aa_seq_input_ids'], batch['aa_seq_attention_mask']
seq_embeds = self.plm_embedding(plm_model, aa_seq, attention_mask)
if 'foldseek_seq' in self.args.structure_seq:
foldseek_seq = batch['foldseek_seq_input_ids']
foldseek_embeds = self.foldseek_embedding(foldseek_seq)
foldseek_embeds = self.cross_attention_foldseek(foldseek_embeds, seq_embeds, seq_embeds, attention_mask)
embeds = seq_embeds + foldseek_embeds
embeds = self.layer_norm(embeds)
if 'ss8_seq' in self.args.structure_seq:
ss_seq = batch['ss8_seq_input_ids']
ss_embeds = self.ss_embedding(ss_seq)
if 'foldseek_seq' in self.args.structure_seq:
# cross attention with foldseek
ss_embeds = self.cross_attention_ss(ss_embeds, embeds, embeds, attention_mask)
embeds = ss_embeds + embeds
else:
# cross attention with sequence
ss_embeds = self.cross_attention_ss(ss_embeds, seq_embeds, seq_embeds, attention_mask)
embeds = ss_embeds + seq_embeds
embeds = self.layer_norm(embeds)
if 'esm3_structure_seq' in self.args.structure_seq:
esm3_structure_seq = batch['esm3_structure_seq_input_ids']
esm3_structure_embeds = self.esm3_structure_embedding(esm3_structure_seq)
if 'foldseek_seq' in self.args.structure_seq:
# cross attention with foldseek
esm3_structure_embeds = self.cross_attention_esm3_structure(esm3_structure_embeds, embeds, embeds, attention_mask)
embeds = esm3_structure_embeds + embeds
elif 'ss8_seq' in self.args.structure_seq:
# cross attention with ss8
esm3_structure_embeds = self.cross_attention_esm3_structure(esm3_structure_embeds, ss_embeds, ss_embeds, attention_mask)
embeds = esm3_structure_embeds + ss_embeds
else:
# cross attention with sequence
esm3_structure_embeds = self.cross_attention_esm3_structure(esm3_structure_embeds, seq_embeds, seq_embeds, attention_mask)
embeds = esm3_structure_embeds + seq_embeds
embeds = self.layer_norm(embeds)
if self.args.structure_seq:
logits = self.classifier(embeds, attention_mask)
else:
logits = self.classifier(seq_embeds, attention_mask)
return logits
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