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326e148 | 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 | import torch
import torch.nn as nn
from transformers import PreTrainedModel
from .configuration_emcoder import EmCoderConfig
class EmCoderCore(nn.Module):
"""The core encoder architecture of EmCoder, without the classification head."""
def __init__(self, config: EmCoderConfig):
super().__init__()
self.token_embedding = nn.Embedding(config.vocab_size, config.d_model)
self.pos_embedding = nn.Embedding(config.max_seq_len, config.d_model)
self.embed_norm = nn.LayerNorm(config.d_model)
encoder_layer = nn.TransformerEncoderLayer(
d_model=config.d_model,
nhead=config.n_head,
dim_feedforward=config.d_ffn,
dropout=config.dropout,
activation="gelu",
norm_first=True,
batch_first=True,
)
self.encoder = nn.TransformerEncoder(
encoder_layer=encoder_layer, num_layers=config.n_layers
)
self.final_norm = nn.LayerNorm(config.d_model)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Standard forward pass through the encoder."""
seq_len = x.size(1)
pos_ids = torch.arange(seq_len, device=x.device).unsqueeze(0)
x = self.token_embedding(x) + self.pos_embedding(pos_ids)
x = self.embed_norm(x)
x = self.dropout(x)
padding_mask = mask == 0
encoded = self.encoder(x, src_key_padding_mask=padding_mask)
return self.final_norm(encoded)
class EmCoder(PreTrainedModel):
"""The full EmCoder model, including the classification head."""
config_class = EmCoderConfig
def __init__(self, config: EmCoderConfig):
super().__init__(config)
self.encoder = EmCoderCore(config)
self.classifier = nn.Sequential(
nn.Linear(config.d_model, config.d_model),
nn.GELU(),
nn.Dropout(config.dropout),
nn.Linear(config.d_model, config.num_labels),
)
self.post_init()
def _set_mc_dropout(self, active: bool = True):
for m in self.modules():
if isinstance(m, nn.Dropout) or isinstance(m, nn.MultiheadAttention):
m.train(active)
@staticmethod
def _masked_mean_pooling(
features: torch.Tensor, mask: torch.Tensor
) -> torch.Tensor:
mask = mask.unsqueeze(-1) # (B, S, 1)
masked_features = features * mask # (B, S, D)
sum_masked_features = masked_features.sum(dim=1) # (B, D)
count_tokens = torch.clamp(mask.sum(dim=1), min=1e-9) # (B, 1)
return sum_masked_features / count_tokens # (B, D)
def mc_forward(
self,
x: torch.Tensor,
mask: torch.Tensor,
n_samples: int,
max_batch_size: int | None = None,
) -> torch.Tensor:
"""
Performs Monte Carlo Dropout inference to quantify epistemic uncertainty.
Args:
x: Input token IDs of shape (B, S).
mask: Attention mask of shape (B, S).
n_samples: Total number of Monte Carlo samples.
max_batch_size: Maximum number of samples in one forward pass.
Returns:
Logits of shape (n_samples, B, num_labels).
"""
if max_batch_size is None:
max_batch_size = n_samples
B, S = x.shape
num_labels = self.classifier[-1].out_features
all_logits = torch.empty((n_samples, B, num_labels), device=x.device)
is_training = self.training
self._set_mc_dropout(active=True)
try:
for i in range(0, n_samples, max_batch_size):
batch_samples = min(max_batch_size, n_samples - i)
x_stacked = x.repeat(batch_samples, 1) # (batch_samples * B, S)
mask_stacked = mask.repeat(batch_samples, 1) # (batch_samples * B, S)
features = self.encoder(
x_stacked, mask_stacked
) # (batch_samples * B, S, D)
pooled = self._masked_mean_pooling(features, mask_stacked)
logits = self.classifier(pooled) # (n_samples * B, num_labels)
all_logits[i : i + batch_samples] = logits.view(batch_samples, B, -1)
finally:
self._set_mc_dropout(active=is_training)
return all_logits
def forward(self, x: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Standard forward pass without MC Dropout."""
features = self.encoder(x, mask)
pooled = self._masked_mean_pooling(features, mask)
return self.classifier(pooled)
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