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from abc import ABC, abstractmethod
import torch
from torch.nn import Linear, Module, Parameter
from torch import Tensor
from .decoder import decode
from .types import Stats, TopK, SAEOut
from .SAE_Wrapper import _HookedSAE, _disable_hooks
from .Utils import standardize, unit_norm_decoder
from typing import List, Dict, Tuple, Any
from transformer_lens.hook_points import HookPoint
from dataclasses import dataclass
@dataclass
class SAEConfig:
d_in: int
d_sae: int
hook_name: str
hook_names: List[str]
dead_steps_threshold: int
dead_threshold: float
auxk: int | None
standardize: bool
class SAE_Template(
ABC,
_HookedSAE,
):
last_nonzero: torch.Tensor
"""The number of steps since the latents have activated."""
def __init__(
self,
d_in: int,
d_sae: int,
hook_names: List[str],
dead_steps_threshold: int,
dead_threshold: float = 1e-3,
# TODO: Make this optional and default to a power of 2 close to d_model / 2.
auxk: int | None = 256,
standardize: bool = True,
) -> None:
"""
Args:
d_in (int): The number of inputs.
d_sae (int): The number of latents.
dead_steps_threshold (int): The number of steps after which a latent is
flagged as dead during training.
dead_threshold (float): The threshold for a latent to be considered
activated. Defaults to 1e-3.
auxk (int | None): The number of dead latents with which to model the
reconstruction error. Defaults to 256.
standardize (bool): Whether to standardize the inputs. Defaults to True.
"""
super().__init__()
self.cfg = SAEConfig(
d_in=d_in,
d_sae=d_sae,
hook_name=hook_names[0],
hook_names=hook_names,
auxk=auxk,
dead_steps_threshold=dead_steps_threshold,
dead_threshold=dead_threshold,
standardize=standardize,
)
self.use_error_term = False
self.encoder = Linear(d_in, d_sae, bias=False)
self.decoder = Linear(d_sae, d_in, bias=False)
self.pre_encoder_bias = Parameter(torch.zeros(d_in))
self.register_buffer("last_nonzero", torch.zeros(d_sae, dtype=torch.long))
self.decoder.weight.data = self.encoder.weight.data.T.clone()
self.decoder.weight.data = self.decoder.weight.data.T.contiguous().T
unit_norm_decoder(self.decoder)
# set up hooks
self.hook_sae_input = HookPoint()
self.hook_sae_acts_pre = HookPoint()
self.hook_sae_acts_post = HookPoint()
self.hook_sae_output = HookPoint()
self.hook_sae_recons = HookPoint()
self.hook_sae_error = HookPoint()
super().setup()
@abstractmethod
def encode(self, *args, **kwargs) -> Tuple[Any, ...]:
pass
@abstractmethod
def decode(self, *args, **kwargs) -> torch.Tensor:
pass
@abstractmethod
def forward_training(self, inputs: torch.Tensor) -> SAEOut:
pass
def forward(
self,
x: torch.Tensor,
) -> torch.Tensor:
'''
Modify the forward pass to allow gradient flows through the error term.
'''
latents, _, _, stats, _ = self.encode(x)
sae_out = self.decode(latents, stats)
if self.use_error_term:
with torch.no_grad() if self.detach_error_term else torch.enable_grad():
with _disable_hooks(self):
clead_sae, _, _, clean_stats, _ = self.encode(x)
x_reconstruct_clean = self.decode(clead_sae, clean_stats)
if self.disable_error_grad:
# If disable_error_grad -> gradient will NOT flows through the error hook
# Similar to SAE_LENS forward function
with torch.no_grad():
sae_error = self.hook_sae_error(x - x_reconstruct_clean)
else:
# If not disable_error_grad -> gradient will flows through the error hook
# If detach_error_term -> gradient of error term will not affect upstream components
with torch.no_grad() if self.detach_error_term else torch.enable_grad():
temp_error = (x - x_reconstruct_clean)
temp_error.requires_grad_() # allow gradient flows through the error hook
sae_error = self.hook_sae_error(temp_error)
sae_out = sae_out + sae_error
return self.hook_sae_output(sae_out)
def update_last_nonzero(self, topk: TopK, device: torch.device) -> None:
# Update the number of steps since the latents have activated
last_nonzero = torch.zeros_like(self.last_nonzero, device=device)
last_nonzero.scatter_add_(
dim=0,
index=topk.indices.reshape(-1),
src=(
topk.values > self.cfg.dead_threshold
).to(last_nonzero.dtype).reshape(-1),
)
self.last_nonzero *= 1 - last_nonzero.clamp(max=1)
self.last_nonzero += 1
def compute_dead_latents_and_auxk(self, latents: Tensor) -> tuple[Tensor, TopK | None]:
# Mask the latents flagged as dead during training
dead_mask = self.last_nonzero >= self.cfg.dead_steps_threshold
latents = latents * dead_mask
# Compute the fraction of dead latents
dead = torch.sum(dead_mask, dtype=torch.float32).detach() / self.cfg.d_sae
# If auxk is not None, find the auxk largest dead latents
auxk = None
if self.cfg.auxk is not None:
values_auxk , indices_auxk = torch.topk(
latents,
k=self.cfg.auxk,
sorted=False
)
auxk = TopK(values_auxk , indices_auxk)
return dead, auxk
def on_after_backward(self) -> list[Linear]:
return []
def on_train_end(self) -> list[Tensor]:
return []
@property
def W_dec(self) -> Tensor:
"""The decoder weight matrix. Transpose to match sae_lens"""
return self.decoder.weight.T
@property
def W_enc(self) -> Tensor:
"""The encoder weight matrix. Transpose to match sae_lens"""
return self.encoder.weight.T
@property
def b_dec(self) -> Tensor:
"""The decoder bias vector."""
return self.pre_encoder_bias
@property
def b_enc(self) -> Tensor:
"""The encoder bias vector."""
return -self.pre_encoder_bias
class SAE(SAE_Template):
last_nonzero: torch.Tensor
"""The number of steps since the latents have activated."""
def __init__(
self,
d_in: int,
d_sae: int,
hook_names: List[str],
dead_steps_threshold: int,
dead_threshold: float = 1e-3,
# TODO: Make this optional and default to a power of 2 close to d_model / 2.
auxk: int | None = 256,
standardize: bool = True,
sparsity_coef: float = 1 / 16,
**kwargs,
) -> None:
"""
Args:
d_in (int): The number of inputs.
d_sae (int): The number of latents.
dead_steps_threshold (int): The number of steps after which a latent is
flagged as dead during training.
dead_threshold (float): The threshold for a latent to be considered
activated. Defaults to 1e-3.
auxk (int | None): The number of dead latents with which to model the
reconstruction error. Defaults to 256.
standardize (bool): Whether to standardize the inputs. Defaults to True.
"""
super().__init__(
d_in=d_in,
d_sae=d_sae,
hook_names=hook_names,
auxk=auxk,
dead_steps_threshold=dead_steps_threshold,
dead_threshold=dead_threshold,
standardize=standardize,
)
self.sparsity_coef = sparsity_coef
def encode(
self, inputs: torch.Tensor
) -> tuple[torch.Tensor, TopK, TopK | None, Stats | None, torch.Tensor]:
inputs = self.hook_sae_input(inputs)
stats = None
if self.cfg.standardize:
inputs, stats = standardize(inputs)
hidden_pre = self.hook_sae_acts_pre(self.encoder.forward(inputs - self.pre_encoder_bias))
latents = self.hook_sae_acts_post(torch.relu(hidden_pre))
# Convert to standard output format of TopK(values, indices) - this ensures a consistent API
mask = latents > self.cfg.dead_threshold
max_k = torch.max(torch.sum(mask, dim=-1)).item()
values, indices = torch.topk(
latents,
k=max_k, # type: ignore
sorted=False
)
topk = TopK(values, indices)
self.update_last_nonzero(topk, inputs.device)
dead, auxk = self.compute_dead_latents_and_auxk(hidden_pre)
return latents, topk, auxk, stats, dead
def decode(self, latents: torch.Tensor, stats: Stats | None = None) -> torch.Tensor:
recons = (latents @ self.decoder.weight.T) + self.pre_encoder_bias
if stats is not None:
recons = recons * stats.std + stats.mean
return self.hook_sae_recons(recons)
def forward_training(self, inputs: torch.Tensor) -> SAEOut:
latents, topk, auxk, stats, dead = self.encode(inputs)
recons = self.decode(latents, stats)
sparsity_loss = torch.abs(topk.values).sum(dim=-1).mean() * self.sparsity_coef
auxk_recons = None
if auxk is not None:
auxk_latents = torch.zeros_like(latents)
auxk_latents.scatter_(
dim=-1,
index=auxk.indices,
src=torch.relu(auxk.values),
)
auxk_recons = self.decode(auxk_latents)
recons = self.hook_sae_output(recons)
return SAEOut(topk, recons, auxk, auxk_recons, dead, sparsity_loss) |