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import torch
from models.nn.feature_factory import FeatureFactory
from models.nn.modules.attn_n_transition import MultiheadAttnAndTransition
from models.nn.modules.pair_update import PairReprUpdate
from models.nn.modules.seq_transition_af3 import Transition
class EncoderTransformer(torch.nn.Module):
"""
Encoder part of the autoencoder. A transformer with pair-biased attention.
"""
def __init__(self, **kwargs):
"""
Initializes the NN. The seqs and pair representations used are just zero in case
no features are required."""
super(EncoderTransformer, self).__init__()
self.nlayers = kwargs["encoder"]["nlayers"]
self.token_dim = kwargs["encoder"]["token_dim"]
self.pair_repr_dim = kwargs["encoder"]["pair_repr_dim"]
self.update_pair_repr = kwargs["encoder"]["update_pair_repr"]
self.update_pair_repr_every_n = kwargs["encoder"]["update_pair_repr_every_n"]
self.use_tri_mult = kwargs["encoder"]["use_tri_mult"]
self.normalize_latent = kwargs["encoder"]["normalize_latent"]
# To form initial representation
self.init_repr_factory = FeatureFactory(
feats=kwargs["encoder"]["feats_seq"],
dim_feats_out=kwargs["encoder"]["token_dim"],
use_ln_out=False,
mode="seq",
**kwargs["encoder"],
)
# To get conditioning variables
self.cond_factory = FeatureFactory(
feats=kwargs["encoder"]["feats_cond_seq"],
dim_feats_out=kwargs["encoder"]["dim_cond"],
use_ln_out=False,
mode="seq",
**kwargs["encoder"],
)
self.transition_c_1 = Transition(
kwargs["encoder"]["dim_cond"], expansion_factor=2
)
self.transition_c_2 = Transition(
kwargs["encoder"]["dim_cond"], expansion_factor=2
)
# To get pair representation
self.pair_rep_factory = FeatureFactory(
feats=kwargs["encoder"]["feats_pair_repr"],
dim_feats_out=kwargs["encoder"]["pair_repr_dim"],
use_ln_out=False,
mode="pair",
**kwargs["encoder"],
)
# Trunk layers
self.transformer_layers = torch.nn.ModuleList(
[
MultiheadAttnAndTransition(
dim_token=kwargs["encoder"]["token_dim"],
dim_pair=kwargs["encoder"]["pair_repr_dim"],
nheads=kwargs["encoder"]["nheads"],
dim_cond=kwargs["encoder"]["dim_cond"],
residual_mha=True,
residual_transition=True,
parallel_mha_transition=False,
use_attn_pair_bias=True,
use_qkln=kwargs["encoder"]["use_qkln"],
)
for _ in range(self.nlayers)
]
)
# To update pair representations if needed
if self.update_pair_repr:
self.pair_update_layers = torch.nn.ModuleList(
[
(
PairReprUpdate(
token_dim=kwargs["encoder"]["token_dim"],
pair_dim=kwargs["encoder"]["pair_repr_dim"],
use_tri_mult=self.use_tri_mult,
)
if i % self.update_pair_repr_every_n == 0
else None
)
for i in range(self.nlayers - 1)
]
)
self.latent_decoder_mean_n_log_scale = torch.nn.Sequential(
torch.nn.LayerNorm(self.token_dim),
torch.nn.Linear(
self.token_dim, int(2 * kwargs["latent_z_dim"]), bias=False
),
)
self.ln_z = torch.nn.Identity()
def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Runs the network.
Args:
batch: batch from dataset
Returns:
Dictionary
...
"""
mask = batch["mask_dict"]["coords"][..., 0, 0] # [b, n] boolean
# Conditioning variables
c = self.cond_factory(batch) # [b, n, dim_cond]
c = self.transition_c_2(self.transition_c_1(c, mask), mask) # [b, n, dim_cond]
# Iinitial sequence representation from features
seq_f_repr = self.init_repr_factory(batch) # [b, n, token_dim]
seqs = seq_f_repr * mask[..., None] # [b, n, token_dim]
pair_rep = self.pair_rep_factory(batch) # [b, n, n, pair_dim]
# Run trunk
for i in range(self.nlayers):
seqs = self.transformer_layers[i](
seqs, pair_rep, c, mask
) # [b, n, token_dim]
if self.update_pair_repr:
if i < self.nlayers - 1:
if self.pair_update_layers[i] is not None:
pair_rep = self.pair_update_layers[i](
seqs, pair_rep, mask
) # [b, n, n, pair_dim]
# Get final coordinates
flat_out = (
self.latent_decoder_mean_n_log_scale(seqs) * mask[..., None]
) # [b, n, 2 * latent_dim]
flat_out = flat_out * mask[..., None]
mean, log_scale = torch.chunk(
flat_out, chunks=2, dim=-1
) # [b, n, latent_dim] each
z = mean + torch.randn_like(log_scale) * torch.exp(
log_scale
) # [b, n, latent_dim]
z_pre_ln = z
z = self.ln_z(z) * mask[..., None] # [b, n, latent_dim]
output = {
"mean": mean,
"log_scale": log_scale,
"z_latent": z,
"z_latent_pre_ln": z_pre_ln,
}
return output
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