File size: 13,890 Bytes
a2ffd07 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 | # Based on https://github.com/openai/sparse_autoencoder/blob/4965b941e9eb590b00b253a2c406db1e1b193942/sparse_autoencoder/train.py
import torch
from huggingface_hub import PyTorchModelHubMixin
from torch.nn import Linear, Module, Parameter
from torch import Tensor
from .decoder import decode
from .types import CrosscoderOut, Stats, TopK, SAEOut
from .Standard import SAE_Template, SAEConfig
from .SAE_Wrapper import _disable_hooks
from typing import Any, Tuple, Dict, List, Optional
from .Utils import standardize, unit_norm_decoder
from transformer_lens.hook_points import HookedRootModule
from transformer_lens.hook_points import HookPoint
class TopKSAE(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],
k: int,
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,
**kwargs,
) -> None:
"""
Args:
d_in (int): The number of inputs.
d_sae (int): The number of latents.
k (int): The number of largest latents to keep.
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.k = k
def encode(
self, inputs: torch.Tensor
) -> tuple[Tensor, TopK, TopK | None, Stats | None, torch.Tensor]:
inputs = self.hook_sae_input(inputs)
stats = None
if self.cfg.standardize:
inputs, stats = standardize(inputs)
# Keep a reference to the latents before the TopK activation function
hidden_pre = self.hook_sae_acts_pre(self.encoder.forward(inputs - self.pre_encoder_bias))
# Find the k largest latents
values, indices = torch.topk(
hidden_pre,
k=self.k,
sorted=False
)
topk = TopK(torch.relu(values), indices)
latents = torch.zeros_like(hidden_pre)
latents = self.hook_sae_acts_post(latents.scatter_(-1, topk.indices, topk.values))
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: Tensor, stats: Stats | None = None) -> torch.Tensor:
# if self.kernel_speedup:
# # NOTE: We need to convert latents to topk, instead of using the topk from the forward pass
# # because the latents is at the "hook_sae_acts_post", which is after the TopK activation function.
# values, indices = torch.topk(
# latents,
# k=self.k,
# sorted=False
# )
# topk = TopK(values, indices)
# recons = decode(topk, self.decoder.weight) + self.pre_encoder_bias
# else:
# recons = (latents @ self.decoder.weight.T) + self.pre_encoder_bias
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)
auxk_recons = None
if auxk is not None:
auxk_latents = torch.zeros_like(latents)
auxk_latents.scatter_(
-1,
auxk.indices,
torch.relu(auxk.values),
)
auxk_recons = self.decode(auxk_latents)
# recons = self.hook_sae_output(recons)
return SAEOut(topk, recons, auxk, auxk_recons, dead, 0)
class TopKTranscoder(TopKSAE):
def __init__(
self,
d_in: int,
d_sae: int,
hook_names: list[str],
k: int,
dead_steps_threshold: int,
auxk: int | None = 256,
dead_threshold: float = 1e-3,
standardize: bool = True,
**kwargs
):
assert len(hook_names) == 2, "TopKTranscoder requires exactly two hook names."
output_hook, input_hook = hook_names
super().__init__(
d_in=d_in,
d_sae=d_sae,
hook_names=[output_hook, input_hook],
k=k,
auxk=auxk,
dead_steps_threshold=dead_steps_threshold,
dead_threshold=dead_threshold,
standardize=standardize,
)
self.input_hook = input_hook
self.output_hook = output_hook
self.decoder_bias = Parameter(torch.zeros(d_in))
def decode(self, latents: Tensor, stats: Stats | None = None) -> torch.Tensor:
recons = (latents @ self.decoder.weight.T) + self.decoder_bias
return self.hook_sae_recons(recons)
def forward(
self,
x: Any,
) -> torch.Tensor:
'''
Modify the forward pass to allow gradient flows through the error term.
'''
if isinstance(x, torch.Tensor):
inputs = x
outputs = None
elif isinstance(x, Tuple):
inputs, outputs = x
assert isinstance(inputs, torch.Tensor), "Inputs must be a torch.Tensor."
assert isinstance(outputs, torch.Tensor), "Outputs must be a torch.Tensor."
else:
raise TypeError("Input must be a torch.Tensor or a tuple of (inputs, outputs).")
latents, _, _, stats, _ = self.encode(inputs)
sae_out = self.decode(latents, stats)
if self.use_error_term and outputs is not None:
with torch.no_grad() if self.detach_error_term else torch.enable_grad():
with _disable_hooks(self):
clead_sae, _, _, clean_stats, _ = self.encode(inputs)
input_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(outputs - input_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 = (outputs - input_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)
@property
def b_dec(self) -> torch.Tensor:
"""
Returns the decoder bias.
"""
return self.decoder_bias
class TopKCrosscoder(TopKSAE):
def __init__(
self,
d_in: int,
d_sae: int,
input_hook: str,
output_hooks: list[str],
k: int,
dead_steps_threshold: int,
auxk: int | None = 256,
dead_threshold: float = 1e-3,
standardize: bool = True,
**kwargs
):
self.input_hook = input_hook
self.output_hooks = output_hooks
super().__init__(
d_in=d_in,
d_sae=d_sae,
hook_names=output_hooks + [input_hook],
k=k,
auxk=auxk,
dead_steps_threshold=dead_steps_threshold,
dead_threshold=dead_threshold,
standardize=standardize,
)
self.crosscoder_decoders = torch.nn.ModuleList(
[Linear(d_sae, d_in, bias=True) for _ in range(len(output_hooks)-1)]
)
self.decoder_bias = Parameter(torch.zeros(d_in))
def decode(self, latents: Tensor, stats: Stats | None = None) -> torch.Tensor:
recons = (latents @ self.decoder.weight.T) + self.decoder_bias
return self.hook_sae_recons(recons)
def crosscoder_decode(
self,
latents: Tensor,
) -> List[torch.Tensor]:
"""
Decode the latents into a list of tensors, one for each output hook.
"""
recons = []
for decoder in self.crosscoder_decoders:
recons.append((decoder(latents)))
return recons
def _forward(
self,
inputs: torch.Tensor,
outputs: torch.Tensor | None,
pv_cons: torch.Tensor | float,
sae_out: torch.Tensor,
) -> torch.Tensor:
if self.use_error_term and outputs is not None:
with torch.no_grad() if self.detach_error_term else torch.enable_grad():
with _disable_hooks(self):
clead_sae, _, _, clean_stats, _ = self.encode(inputs)
input_reconstruct_clean = self.decode(clead_sae, clean_stats) + pv_cons
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(outputs - input_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 = (outputs - input_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 forward(
self,
x: Any,
) -> torch.Tensor:
inputs, outputs, pv_cons = self.check_input(x)
latents, _, _, stats, _ = self.encode(inputs)
sae_out = self.decode(latents, stats) + pv_cons
return self._forward(inputs, outputs, pv_cons, sae_out)
def forward_crosscoder(
self,
x: Any,
) -> Tuple[Tensor, List[Tensor]]:
inputs, outputs, pv_cons = self.check_input(x)
latents, _, _, stats, _ = self.encode(inputs)
sae_out = self.decode(latents, stats) + pv_cons
output = self._forward(inputs, outputs, pv_cons, sae_out)
cross_outputs = self.crosscoder_decode(latents)
return output, cross_outputs
def forward_training(self, inputs: Any) -> CrosscoderOut: # type: ignore
if isinstance(inputs, Tuple):
inputs, pv_cons = inputs # input and previous layer construction
assert isinstance(inputs, torch.Tensor), "Inputs must be a torch.Tensor."
assert isinstance(pv_cons, torch.Tensor) or isinstance(pv_cons, float), "Previous layer construction must be a torch.Tensor."
else:
raise TypeError("Input must be a tuple of (inputs, previous_layer_construction).")
latents, topk, auxk, stats, dead = self.encode(inputs)
recons = self.decode(latents, stats) + pv_cons
cross_recons = self.crosscoder_decode(latents)
auxk_recons = None
if auxk is not None:
auxk_latents = torch.zeros_like(latents)
auxk_latents.scatter_(
-1,
auxk.indices,
torch.relu(auxk.values),
)
auxk_recons = self.decode(auxk_latents)
# recons = self.hook_sae_output(recons)
return CrosscoderOut(topk, recons, cross_recons, auxk, auxk_recons, dead, 0)
def check_input(
self,
x: Any,
) -> Tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | float]:
"""
Check the input type and return the inputs, outputs, and previous layer construction.
"""
if isinstance(x, Tuple):
if len(x) == 2:
inputs, pv_cons = x
outputs = None
elif len(x) == 3:
inputs, outputs, pv_cons = x
else:
raise ValueError("Input tuple must be of length 2 or 3.")
assert isinstance(inputs, torch.Tensor), "Inputs must be a torch.Tensor."
assert isinstance(outputs, torch.Tensor) or outputs is None, "Outputs must be a torch.Tensor or None."
assert isinstance(pv_cons, torch.Tensor) or isinstance(pv_cons, float), "Previous layer construction must be a torch.Tensor."
return inputs, outputs, pv_cons
else:
raise TypeError("Input must be a tuple of (inputs, outputs, previous_layer_construction).") |