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truthx.py
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import torch
from torch import nn
import torch.nn.functional as F
from abc import abstractmethod
from torch import tensor as Tensor
from typing import List, Any
class BaseVAE(nn.Module):
def __init__(self) -> None:
super(BaseVAE, self).__init__()
def encode(self, input: Tensor) -> List[Tensor]:
raise NotImplementedError
def decode(self, input: Tensor) -> Any:
raise NotImplementedError
def sample(self, batch_size: int, current_device: int, **kwargs) -> Tensor:
raise NotImplementedError
def generate(self, x: Tensor, **kwargs) -> Tensor:
raise NotImplementedError
@abstractmethod
def forward(self, *inputs: Tensor) -> Tensor:
pass
@abstractmethod
def loss_function(self, *inputs: Any, **kwargs) -> Tensor:
pass
class MLPAE(BaseVAE):
def __init__(
self,
in_channels: int,
semantic_latent_dim: int,
truthful_latent_dim: int,
semantic_hidden_dims: List = None,
truthful_hidden_dims: List = None,
decoder_hidden_dims: List = None,
**kwargs
) -> None:
super(MLPAE, self).__init__()
self.semantic_latent_dim = semantic_latent_dim
if semantic_hidden_dims is None:
semantic_hidden_dims = []
# Build Semantic Encoder
semantic_encoder_modules = []
flat_size = in_channels
for h_dim in semantic_hidden_dims:
semantic_encoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, h_dim), nn.LayerNorm(h_dim), nn.LeakyReLU()
)
)
flat_size = h_dim
semantic_encoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, semantic_latent_dim),
nn.LayerNorm(semantic_latent_dim),
nn.LeakyReLU(),
)
)
self.semantic_encoder = nn.Sequential(*semantic_encoder_modules)
if truthful_hidden_dims is None:
truthful_hidden_dims = []
# Build Truthful Encoder
truthful_encoder_modules = []
flat_size = in_channels
for h_dim in truthful_hidden_dims:
truthful_encoder_modules.append(
nn.Sequential(
(
nn.Linear(flat_size, h_dim)
if flat_size != h_dim
else nn.Identity()
),
nn.LayerNorm(h_dim),
nn.LeakyReLU(),
)
)
flat_size = h_dim
truthful_encoder_modules.append(
nn.Sequential(
(
nn.Linear(flat_size, truthful_latent_dim)
if flat_size != truthful_latent_dim
else nn.Identity()
),
nn.LayerNorm(truthful_latent_dim),
nn.LeakyReLU(),
)
)
self.truthful_encoder = nn.Sequential(*truthful_encoder_modules)
# Cross-Attention Module
self.num_heads = 1
self.cross_attention = nn.MultiheadAttention(
embed_dim=semantic_latent_dim, num_heads=self.num_heads
)
self.proj = None
if semantic_latent_dim != truthful_latent_dim:
self.proj = nn.Linear(truthful_latent_dim, semantic_latent_dim, bias=False)
# Build Decoder
decoder_modules = []
if len(decoder_hidden_dims) > 0:
flat_size = semantic_latent_dim
for h_dim in decoder_hidden_dims:
decoder_modules.append(
nn.Sequential(
nn.Linear(flat_size, h_dim), nn.LayerNorm(h_dim), nn.LeakyReLU()
)
)
flat_size = h_dim
flat_size = decoder_hidden_dims[-1]
self.decoder = nn.Sequential(*decoder_modules)
else:
self.decoder_input = None
self.decoder = None
flat_size = semantic_latent_dim
self.final_layer = nn.Sequential(nn.Linear(flat_size, in_channels))
def encode_semantic(self, input: Tensor) -> List[Tensor]:
semantic_latent_rep = self.semantic_encoder(input)
return semantic_latent_rep
def encode_truthful(self, input: Tensor) -> List[Tensor]:
truthful_latent_rep = self.truthful_encoder(input)
truthful_latent_rep = F.normalize(truthful_latent_rep, p=2, dim=-1)
return truthful_latent_rep
def attention(self, query: Tensor, key: Tensor, value: Tensor) -> Tensor:
if self.proj is not None and query.size(-1) != key.size(-1):
key = self.proj(key)
value = self.proj(value)
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
output, attention_weights = self.cross_attention(query, key, value)
return output[0]
def decode(self, z: Tensor) -> Tensor:
result = z
if self.decoder is not None:
result = self.decoder(result)
result = self.final_layer(result)
return result
def forward(
self, input: Tensor, truthful_latent_rep=None, **kwargs
) -> List[Tensor]:
semantic_latent_rep = self.encode_semantic(input)
if truthful_latent_rep is None:
truthful_latent_rep = self.encode_truthful(input)
truthful_latent_rep = truthful_latent_rep.reshape(
-1, truthful_latent_rep.size(-1)
)
z = semantic_latent_rep + self.attention(
semantic_latent_rep,
truthful_latent_rep.contiguous(),
truthful_latent_rep.contiguous(),
)
output = self.decode(z)
return [output, input, semantic_latent_rep, truthful_latent_rep]
def forward_decoder(self, input, semantic_latent_rep, truthful_latent_rep):
z = semantic_latent_rep + self.attention(
semantic_latent_rep, truthful_latent_rep, truthful_latent_rep
)
output = self.decode(z)
return [output, input, semantic_latent_rep, truthful_latent_rep]
def get_semantic_latent_rep(self, input: Tensor, **kwargs) -> List[Tensor]:
semantic_latent_rep = self.encode_semantic(input)
return semantic_latent_rep
def get_truthful_latent_rep(self, input: Tensor, **kwargs) -> List[Tensor]:
truthful_latent_rep = self.encode_truthful(input)
return truthful_latent_rep
def loss_function(self, *args, **kwargs) -> dict:
recons = args[0]
input = args[1]
recons_loss = F.mse_loss(recons, input)
loss = recons_loss
return {"loss": loss, "Reconstruction_Loss": recons_loss.detach()}
class TruthX:
def __init__(self, model_path, hidden_size, edit_strength=1.0, top_layers=10):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(model_path)
args = checkpoint["args"]
semantic_latent_dim = args.semantic_latent_dim # Adjust as needed
truthful_latent_dim = args.truthful_latent_dim
semantic_hidden_dims = (
[int(_) for _ in args.semantic_hidden_dims.split(",")]
if args.semantic_hidden_dims != ""
else []
)
truthful_hidden_dims = (
[int(_) for _ in args.truthful_hidden_dims.split(",")]
if args.truthful_hidden_dims != ""
else []
)
decoder_hidden_dims = (
[int(_) for _ in args.decoder_hidden_dims.split(",")]
if args.decoder_hidden_dims != ""
else []
)
ae_model = MLPAE(
in_channels=hidden_size,
semantic_latent_dim=semantic_latent_dim,
truthful_latent_dim=truthful_latent_dim,
semantic_hidden_dims=semantic_hidden_dims,
truthful_hidden_dims=truthful_hidden_dims,
decoder_hidden_dims=decoder_hidden_dims,
).to(device)
ae_model.load_state_dict(checkpoint["state_dict"])
ae_model.pos_center = ((checkpoint["pos_center"])).to(device)
ae_model.neg_center = ((checkpoint["neg_center"])).to(device)
ae_model.eval()
ae_model.to(device)
self.ae_model = ae_model
# checkpoint['accuracy'][-1]=1.0
self.rank = checkpoint["rank"]
self.top_layers = top_layers
self.edit_strength = edit_strength
self.cur_layer_id = 0
self.prompt_length = None
self.mc = False
@torch.inference_mode()
def edit(self, X):
layer_id = int(self.cur_layer_id.split(".")[0])
if self.cur_layer_id.endswith("attn"):
layer_id = 2 * layer_id
else:
layer_id = 2 * layer_id + 1
if self.rank[layer_id] > self.top_layers:
return X
bsz, s_len, d = X.size()
x = (
X.contiguous()
.view(-1, d)
.type_as(self.ae_model.semantic_encoder[0][0].weight)
)
x_truthful = self.ae_model.get_truthful_latent_rep(
X.type_as(self.ae_model.semantic_encoder[0][0].weight)
)
pos_center = self.ae_model.pos_center[layer_id].unsqueeze(0)
neg_center = self.ae_model.neg_center[layer_id].unsqueeze(0)
delta = (pos_center - neg_center).unsqueeze(0)
recon_x_pos = (
self.ae_model(
x,
truthful_latent_rep=F.normalize(
x_truthful + delta, p=2, dim=-1
).type_as(x),
)[0]
.contiguous()
.view(bsz, s_len, d)
)
recon_x_neg = (
self.ae_model(
x,
truthful_latent_rep=F.normalize(
x_truthful - delta, p=2, dim=-1
).type_as(x),
)[0]
.contiguous()
.view(bsz, s_len, d)
)
Delta = recon_x_pos - recon_x_neg
Delta = Delta.contiguous().to(X.dtype)
Delta = F.normalize(Delta, p=2, dim=-1).type_as(X) * torch.norm(
X, p=2, dim=-1
).unsqueeze(2)
mask = torch.ones((bsz, s_len), device=Delta.device)
if self.mc:
# multiple-choice, only edit the tokens in answer
mask[:, : self.prompt_length + 1] = 0
# probing those untruthful position
probing = (
torch.nn.functional.cosine_similarity(
x_truthful, neg_center.unsqueeze(1), dim=-1
)
- torch.nn.functional.cosine_similarity(
x_truthful, pos_center.unsqueeze(1), dim=-1
)
).clamp(0, 999)
mask = mask * probing
else:
# open-ended generation, only edit the generated token (i.e., last token)
mask[:, :-1] = 0
mask[:, -1:] = 1
new_X = X + (Delta.type_as(X)) * self.edit_strength * mask.unsqueeze(2).type_as(X)
return new_X