File size: 45,218 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 | from tracemalloc import start
from typing import Literal
from .Feature_Graph_Trans import *
from transformer_lens.utils import get_act_name
def _hook_name(sae_name: str, name: str | None):
return sae_name + "." + str(name)
class Feature_Graph_Cross(Feature_Graph_Trans):
def __init__(
self,
model: HookedSAETransformer,
saes: Dict[int, List[Tuple[str, Any]]], # {layer: list[{hook_position: HookedSAE}]}, can define granularity here
use_error_term: bool = False,
):
super().__init__(model, saes, use_error_term)
def process_transcoder(self):
self.input_hooks = []
self.output_hooks = []
self.crosscoders = []
self.non_crosscoders = []
check_mlp_out = False
self.check_attn_out = False
self.check_resid_pre = False
for sae in self.dict_saes.values():
sae = sae.to(self.device)
if getattr(sae, 'input_hook', False) and getattr(sae, 'output_hooks', False):
self.input_hooks.append(sae.input_hook)
self.output_hooks.append(sae.output_hooks[0])
self.crosscoders.append(sae)
if "mlp_out" in sae.output_hooks[0]:
check_mlp_out = True
else:
self.non_crosscoders.append(sae)
if "attn_out" in sae.cfg.hook_name:
self.check_attn_out = True
if "resid_pre" in sae.cfg.hook_name:
self.check_resid_pre = True
assert check_mlp_out, "Crosscoder needs to be provided at the mlp_out hook."
def _gradient_wrt_nodes_ig(
self,
clean_token: Tensor,
corrupt_cache: ActivationCache | Dict[str, Tensor],
metric: Callable[[Tensor], Tensor],
retain_graph: bool = False,
verbose: bool = False,
**kwargs,
) -> Tuple[
Dict[Tuple[Node, Index], SparseAct], # node effects
Dict[str, SparseAct]
]:
steps = kwargs.get("steps", 10)
self._check_graph()
self.model_setup()
self.model.reset_hooks()
for _, sae in self.dict_saes.items():
sae.reset_hooks()
fwd_cache = {}
bwd_cache = {}
with t.set_grad_enabled(True):
with self._detach_error_term(True):
for target_name in self.dict_saes.keys():
for step in range(steps):
frac = step / steps
with self._setup_fwd_bwd_grad_sae_hook_ig(
target_name=target_name,
frac=frac,
fwd_cache=fwd_cache,
bwd_cache=bwd_cache,
corrupt_cache=corrupt_cache,
):
with self._setup_forward_model_hook(transfer_grad=kwargs.get("transfer_grad", True)):
metric(self.model(clean_token)).backward(retain_graph=retain_graph)
# average the gradients
for key in bwd_cache.keys():
bwd_cache[key] /= steps
node_grads = {}
for node, index in self.nodes.keys():
node_grads[(node, index)] = cache_to_sparseact(
bwd_cache,
sae_hook_name(node.name),
error_term_name(node.name) if self.use_error_term else None,
)
cache = {}
for sae_name in self.dict_saes.keys():
cache[sae_name] = cache_to_sparseact(
fwd_cache,
sae_hook_name(sae_name),
error_term_name(sae_name) if self.use_error_term else None,
)
self.model.reset_hooks()
for sae in self.dict_saes.values():
sae.reset_hooks()
return node_grads, cache
def _gradient_wrt_nodes_vw_ig(
self,
clean_token: Tensor,
corrupt_cache: ActivationCache | Dict[str, Tensor],
metric: Callable[[Tensor], Tensor],
retain_graph: bool = False,
verbose: bool = False,
**kwargs,
) -> Tuple[
Dict[Tuple[Node, Index], SparseAct], # node effects
Dict[str, SparseAct]
]:
'''
Using virtual weight to compute node grad
'''
steps = kwargs.get("steps", 10)
self._check_graph()
self.model_setup()
self.model.reset_hooks()
sink_hook_name = get_act_name("resid_post", self.n_layers-1) # last layer resid post hook
sink_node_cache: Dict[str, Tensor] = {}
def hook_bwd(tens: Tensor, hook: HookPoint):
sink_node_cache[hook.name] = tens.detach() # type: ignore
all_fwd_cache = {}
all_bwd_cache = {}
for target_name in self.dict_saes.keys():
for step in range(steps):
frac = step / steps
fwd_cache = {}
with t.set_grad_enabled(True):
with self._detach_error_term(True):
with self.model.hooks(
bwd_hooks=[(sink_hook_name, hook_bwd)]
):
with self._setup_fwd_bwd_grad_sae_hook_ig(
target_name=target_name,
frac=frac,
fwd_cache=fwd_cache,
bwd_cache={},
corrupt_cache=corrupt_cache,
):
with self._setup_forward_model_hook(transfer_grad=False): # save memory, we don't need transfer_grad here
metric(self.model(clean_token)).backward(retain_graph=retain_graph)
# run the model on interpolate intervention
with self._setup_forward_model_hook(transfer_grad=False): # save memory, we don't need transfer_grad here
with self._setup_virtual_weight_sae_hook_ig(
target_name=target_name,
frac=frac,
corrupt_cache=corrupt_cache,
):
_, unpatch_clean_cache = self.model.run_with_cache(clean_token)
current_grad = sink_node_cache[sink_hook_name] # (b, seq, d_model)
bwd_cache = self._TE_using_virtual_weight(
current_grad,
fwd_cache=fwd_cache,
unpatch_clean_cache=unpatch_clean_cache,
)
if step == 0:
add_cache(all_fwd_cache, fwd_cache)
add_cache(all_bwd_cache, bwd_cache)
# average the gradients
for key in all_bwd_cache.keys():
all_bwd_cache[key] /= steps
node_grads = {}
for node, index in self.nodes.keys():
node_grads[(node, index)] = all_bwd_cache[node.name]
cache = {}
for sae_name in self.dict_saes.keys():
cache[sae_name] = cache_to_sparseact(
all_fwd_cache,
sae_hook_name(sae_name),
error_term_name(sae_name) if self.use_error_term else None,
)
self.model.reset_hooks()
for sae in self.dict_saes.values():
sae.reset_hooks()
return node_grads, cache
def _TE_using_virtual_weight(
self,
current_grad: Tensor,
fwd_cache: ActivationCache | Dict[str, Tensor],
unpatch_clean_cache: ActivationCache | Dict[str, Tensor],
) -> Dict[str, Tensor]:
bwd_cache = {}
resid_grad_cache = {}
for layer in reversed(range(self.n_layers)):
mlp_name = get_act_name("mlp_out", layer)
attn_name = get_act_name("attn_out", layer)
resid_pre_name = get_act_name("resid_pre", layer)
resid_grad_cache[mlp_name] = current_grad # (b, seq, d_model)
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
bwd_cache[mlp_name] = SparseAct(
act=current_grad @ self.dict_saes[mlp_name].W_dec.T,
res = current_grad if self.use_error_term else None,
)
for upper_layer in range(layer+1, self.n_layers):
upper_mlp_name = get_act_name("mlp_out", upper_layer)
bwd_cache[mlp_name] += SparseAct(
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
act = resid_grad_cache[upper_mlp_name] @ self.dict_saes[mlp_name].crosscoder_decoders[upper_layer-layer-1].weight,
res = 0 if self.use_error_term else None, # type: ignore
)
grad_through_mlp = gradient_with_mlp(
end_feature_vec=current_grad, # (b, seq, d_model)
start_feature_vec=None,
transcoder_enc=self.dict_saes[mlp_name].W_enc, # (d_model, d_sae)
transcoder_dec=self.dict_saes[mlp_name].W_dec, # (d_sae, d_model)
transcoder_act=fwd_cache[sae_hook_name(mlp_name)], # (b, seq, d_sae)
layer_end=layer,
pos_end=None,
seq_length=self.seq_length, # type: ignore
batch_size=current_grad.shape[0],
use_error_term=self.use_error_term,
cache=unpatch_clean_cache,
device=self.device # type: ignore
).act # (b, seq, d_model)
current_grad = current_grad + grad_through_mlp # resid_mid_grad
if self.check_attn_out:
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
bwd_cache[attn_name] = SparseAct(
act=current_grad @ self.dict_saes[attn_name].W_dec.T,
res = current_grad if self.use_error_term else None,
)
grad_through_attn = gradient_with_attn(
model=self.model,
end_feature_vec=current_grad, # (b, seq, d_model)
start_feature_vec=None,
layer_end=layer,
pos_end=None,
use_error_term=self.use_error_term,
cache=unpatch_clean_cache,
device=self.device # type: ignore
).act # (b, seq, d_model)
current_grad = current_grad + grad_through_attn # resid_pre_grad
if self.check_resid_pre:
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
bwd_cache[resid_pre_name] = SparseAct(
act=current_grad @ self.dict_saes[resid_pre_name].W_dec.T,
res = current_grad if self.use_error_term else None,
)
return bwd_cache
def _edge_attribution_trans(
self,
unpatched_clean_cache: ActivationCache | Dict[str, Tensor],
hook_position_end: Tuple[Node, Index],
hook_position_start: Tuple[Node, Index],
leftvec: SparseAct,
rightvec: SparseAct,
**kwargs,
) -> Tensor:
d_sae_end = self.dict_saes[hook_position_end[0].name].cfg.d_sae
d_sae_start = self.dict_saes[hook_position_start[0].name].cfg.d_sae
start_layer = int(hook_position_start[0].name.split(".")[1])
end_layer = int(hook_position_end[0].name.split(".")[1])
aggregate_dim = [0] if self.token_wise else [0, 1]
edge_effect = {}
all_error = []
for end_node, end_index in self.active_nodes(*hook_position_end):
if isinstance(end_index, ErrorIndex):
all_error.append((end_node, end_index))
elif isinstance(end_index, FeatureIndex):
feat_id = end_index.idx[-1]
pos_end = end_index.idx[-2]
index = t.tensor(list(end_index.idx), device=self.device)
end_node_grad = leftvec.act[:, pos_end, feat_id].unsqueeze(-1).unsqueeze(-1) # (b, 1, 1)
end_feature_vec = self.dict_saes[end_node.name].W_enc[:, feat_id].unsqueeze(0).unsqueeze(0) # (1, 1, d_model)
if "mlp_out" in hook_position_start[0].name:
list_grad_dot_leftvec_tensor = self._DE_using_virtual_weight_cross( # (b, seq, d_model)
grad=end_feature_vec,
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
)
for i in range(len(list_grad_dot_leftvec_tensor)):
list_grad_dot_leftvec_tensor[i] *= end_node_grad # (b, seq, d_model)
grad_dot_leftvec = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=list_grad_dot_leftvec_tensor[-1] @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=list_grad_dot_leftvec_tensor[-1] if self.use_error_term else None
)
for i, upper_layer in enumerate(reversed(range(start_layer+1, end_layer))): # (start+1 -> end_layer-1)
rel_id = upper_layer-start_layer-1
grad_dot_leftvec += SparseAct(
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
act = (
list_grad_dot_leftvec_tensor[i] @
self.dict_saes[hook_position_start[0].name].crosscoder_decoders[rel_id].weight
),
res = 0 if self.use_error_term else None, # type: ignore
)
else:
grad_dot_leftvec_tensor = self._DE_using_virtual_weight( # (b, seq, d_model)
grad=end_feature_vec,
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
) * end_node_grad
grad_dot_leftvec = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=grad_dot_leftvec_tensor @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=grad_dot_leftvec_tensor if self.use_error_term else None
)
'''
edge_effect shape (seq, d_sae+1, seq, d_sae+1) or (d_sae+1, d_sae+1) in sparse_coo tensor
the sparse_coo will have the shape:
--> indices of shape (2, num_active) or (1, num_active)
--> values of shape (num_active, seq, d_sae+1) or (num_active, d_sae+1)
'''
edge_effect[index] = ( # (seq, d_sae+1) || (d_sae+1)
grad_dot_leftvec @ rightvec
).sum(aggregate_dim).to_tensor()
else:
raise ValueError(f"end_index of type {type(end_index)} is not supported.")
'''
The gradient of error node to upstream node is 1 - gradient of sum end_feature_node
We multiply error grad so that we only have to backward once (Jacobian vector product)
The "end_feature_dependent" sums all of the gradient of end_feature_node.
'''
if self.use_error_term:
all_end_node_grad: Tensor = leftvec.res # type: ignore
feature_coef_to_cal_error_edge = einops.einsum(
all_end_node_grad, self.dict_saes[hook_position_end[0].name].W_dec,
"b seq d_model, d_sae_end d_model -> b seq d_sae_end",
)
end_feature_dependent = einops.einsum(
feature_coef_to_cal_error_edge, self.dict_saes[hook_position_end[0].name].W_enc,
"b seq d_sae_end, d_model d_sae_end -> b seq d_model"
)
for end_error_node, end_error_index in all_error:
pos_end = end_error_index.idx[0]
index = t.tensor(list(end_error_index.idx + (d_sae_end,)), device=self.device)
end_node_grad = leftvec.res[:, pos_end].unsqueeze(1) # (b, 1, d_model) # type: ignore
end_error_grad_tensor = self._DE_using_virtual_weight( # (b, seq, d_model)
grad=end_node_grad - end_feature_dependent[:, pos_end].unsqueeze(1), # (b, 1, d_model)
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
)
end_error_grad = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=end_error_grad_tensor @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=end_error_grad_tensor if self.use_error_term else None
)
edge_effect[index] = ( # (seq, d_sae_start+1) | (d_sae_start+1)
end_error_grad @ rightvec
).sum(aggregate_dim).to_tensor()
seq = int(self.seq_length) # type: ignore
num_end = d_sae_end
num_start = d_sae_start
if self.use_error_term:
num_end += 1
num_start += 1
if len(edge_effect.keys()) != 0:
indices = t.stack(list(edge_effect.keys()), dim=0).T # shape (2, num_active) or (1, num_active)
values = t.stack([value for value in edge_effect.values()], dim=0) # shape (num_active, seq, d_sae+1) or (num_active, d_sae+1)
# if no active nodes, return empty tensor
else:
indices = t.empty((2, 0) if self.token_wise else (1, 0), dtype=t.long).to(self.device)
values = t.empty((0, seq, num_start) if self.token_wise else (0, num_start), dtype=t.float).to(self.device)
if self.token_wise:
return t.sparse_coo_tensor(indices, values, size=(seq, num_end, seq, num_start)).coalesce()
else:
return t.sparse_coo_tensor(indices, values, size=(num_end, num_start)).coalesce()
def _DE_using_virtual_weight_cross(
self,
grad: Tensor, # (1, 1, d_model) or (b, 1, d_model)
pos_end: int,
batch_size: int,
unpatched_clean_cache: ActivationCache | Dict[str, Tensor],
hook_position_end: Tuple[Node, Index],
hook_position_start: Tuple[Node, Index],
) -> List[Tensor]:
start_layer = int(hook_position_start[0].name.split(".")[1])
end_layer = int(hook_position_end[0].name.split(".")[1])
path = [] # path attention for gradient, if we have attention circuit then the path is empty
if not self.check_attn_out:
if not "resid_pre" in hook_position_end[0].name:
path.append(("attn", end_layer)) # attn or mlp at end hook -> goes through attn at end_layer
for layer in reversed(range(start_layer+1, end_layer)):
path.append(("attn", layer))
if "resid_pre" in hook_position_start[0].name:
path.append(("attn", start_layer)) # resid_pre at start hook -> goes through attn at start_layer
else:
for layer in reversed(range(start_layer+1, end_layer)):
path.append(("no_attn_grad", layer))
if "mlp_out" in hook_position_end[0].name:
ln = "ln2"
elif "attn_out" in hook_position_end[0].name:
ln = "ln1"
else:
ln = None
if self.check_attn_out and "attn_out" in hook_position_end[0].name:
current_grad = gradient_with_attn(
model=self.model,
end_feature_vec=grad, # (b, seq, d_model)
start_feature_vec=None,
layer_end=end_layer,
pos_end=pos_end,
use_error_term=self.use_error_term,
cache=unpatched_clean_cache,
device=self.device # type: ignore
).act # (b, seq, d_model)
else:
current_grad = gradient_ln_only( # (b, seq, d_model)
end_feature_vec=grad,
start_feature_vec=None,
layer_end=end_layer,
pos_end=pos_end,
seq_length=self.seq_length, # type: ignore
batch_size=batch_size,
use_error_term=self.use_error_term,
cache=unpatched_clean_cache,
device=self.device, # type: ignore
ln=ln,
).act
all_resid_grad = [] # we don't need to cache the end_layer grad
for name, layer in path:
if name == "attn":
grad_through_attn = gradient_with_attn(
model=self.model,
end_feature_vec=current_grad, # (b, seq, d_model)
start_feature_vec=None,
layer_end=layer,
pos_end=None,
use_error_term=self.use_error_term,
cache=unpatched_clean_cache,
device=self.device # type: ignore
).act # (b, seq, d_model)
current_grad = current_grad + grad_through_attn
if layer in range(start_layer+1, end_layer):
all_resid_grad.append(current_grad) # cache the gradient of each intermediate layer
all_resid_grad.append(current_grad) # cache the gradient of the start layer
return all_resid_grad
@contextmanager
def _setup_forward_model_hook(self, use_error_term: bool | None = None, transfer_grad: bool = True):
cross_cache = {}
# Hook function at transcoder input: caches the activations before transcoder
def hook_crosscoder_input(activations: Tensor, hook: HookPoint, crosscoder_idx: int):
cross_cache[crosscoder_idx] = activations.clone()
all_cross_recons = [0.0 for _ in range(len(self.crosscoders))]
def hook_crosscoder_output(activations: Tensor, hook: HookPoint, crosscoder_idx: int):
recons, cross_recons = self.crosscoders[crosscoder_idx].forward_crosscoder(
(cross_cache[crosscoder_idx], activations, all_cross_recons[crosscoder_idx])
)
for j in range(crosscoder_idx+1, self.n_layers):
all_cross_recons[j] += cross_recons[j-crosscoder_idx-1]
if transfer_grad:
return recons + (activations - activations.detach())
else:
return recons
fwd_hooks = []
for i in range(len(self.crosscoders)):
fwd_hooks.append((self.input_hooks[i], partial(hook_crosscoder_input, crosscoder_idx=i)))
fwd_hooks.append((self.output_hooks[i], partial(hook_crosscoder_output, crosscoder_idx=i)))
use_error_term = use_error_term if use_error_term is not None else self.use_error_term
try:
for sae in self._saes_to_list():
self.model.add_sae(sae, use_error_term)
for hook, func in fwd_hooks:
self.model.add_hook(hook, func, dir="fwd")
yield
finally:
self.model.reset_saes()
self.model.reset_hooks()
def _saes_to_list(self) -> List[Any]:
return self.non_crosscoders
class ESAE_FG_Cross(
ESAE_FG_Trans,
Feature_Graph_Cross,
):
def __init__(
self,
model: HookedSAETransformer,
saes: Dict[int, List[Tuple[str, Any]]],
esaes: Dict[int, List[Tuple[str, Any]]],
use_esae_error_term: bool = False,
) -> None:
super().__init__(model, saes, esaes, use_esae_error_term)
def process_transcoder(self):
Feature_Graph_Cross.process_transcoder(self)
def forward(
self,
clean_token: Tensor,
corrupt_cache: ActivationCache | Dict[str, Tensor] | None,
patch_deleted_comp: bool = False,
**kwargs,
) -> Tuple[Tensor, Dict[str, SparseAct]]:
'''
Forward pass of the graph with clean tokens, if the edge exists, replace the activation with corrupted activation
'''
self._check_graph()
self.model.reset_hooks()
self.model_setup()
fwd_cache = {}
with t.no_grad():
with self._setup_forward_model_hook(transfer_grad=False):
with self._setup_fwd_sae_hook(
fwd_cache=fwd_cache, corrupt_cache=corrupt_cache, patch_deleted_comp=patch_deleted_comp
):
logits = self.model(clean_token)
cache = {}
for sae_name in self.dict_saes.keys():
cache[sae_name] = cache_to_sparseact(
fwd_cache,
sae_hook_name(sae_name),
sae_hook_name(error_term_name(sae_name)) if self.use_error_term else None,
error_term_name(error_term_name(sae_name)) if self.use_esae_error_term else None,
)
for sae in self.dict_saes.values():
sae.reset_hooks()
self.model.reset_hooks()
return logits, cache
def _gradient_wrt_nodes_ig(
self,
clean_token: Tensor,
corrupt_cache: ActivationCache | Dict[str, Tensor],
metric: Callable[[Tensor], Tensor],
retain_graph: bool = False,
verbose: bool = False,
**kwargs,
) -> Tuple[
Dict[Tuple[Node, Index], SparseAct], # node effects
Dict[str, SparseAct]
]:
steps = kwargs.get("steps", 10)
self._check_graph()
self.model_setup()
self.model.reset_hooks()
for _, sae in self.dict_saes.items():
sae.reset_hooks()
fwd_cache = {}
bwd_cache = {}
with t.set_grad_enabled(True):
with self._detach_error_term(True):
for target_name in self.dict_saes.keys():
for step in range(steps):
frac = step / steps
with self._setup_fwd_bwd_grad_sae_hook_ig(
target_name=target_name,
frac=frac,
fwd_cache=fwd_cache,
bwd_cache=bwd_cache,
corrupt_cache=corrupt_cache,
):
with self._setup_forward_model_hook(transfer_grad=kwargs.get("transfer_grad", True)):
metric(self.model(clean_token)).backward(retain_graph=retain_graph)
# average the gradients
for key in bwd_cache.keys():
bwd_cache[key] /= steps
node_grads = {}
for node, index in self.nodes.keys():
node_grads[(node, index)] = cache_to_sparseact(
bwd_cache,
sae_hook_name(node.name),
sae_hook_name(error_term_name(node.name)) if self.use_error_term else None,
error_term_name(error_term_name(node.name)) if self.use_esae_error_term else None,
)
cache = {}
for sae_name in self.dict_saes.keys():
cache[sae_name] = cache_to_sparseact(
fwd_cache,
sae_hook_name(sae_name),
sae_hook_name(error_term_name(sae_name)) if self.use_error_term else None,
error_term_name(error_term_name(sae_name)) if self.use_esae_error_term else None,
)
self.model.reset_hooks()
for sae in self.dict_saes.values():
sae.reset_hooks()
return node_grads, cache
def _TE_using_virtual_weight(
self,
current_grad: Tensor,
fwd_cache: ActivationCache | Dict[str, Tensor],
unpatch_clean_cache: ActivationCache | Dict[str, Tensor],
) -> Dict[str, Tensor]:
return Feature_Graph_Cross._TE_using_virtual_weight(
self,
current_grad=current_grad,
fwd_cache=fwd_cache,
unpatch_clean_cache=unpatch_clean_cache,
)
def _edge_attribution_trans(
self,
unpatched_clean_cache: ActivationCache | Dict[str, Tensor],
hook_position_end: Tuple[Node, Index],
hook_position_start: Tuple[Node, Index],
leftvec: SparseAct,
rightvec: SparseAct,
**kwargs,
) -> Tensor:
d_sae_end = self.dict_saes[hook_position_end[0].name].cfg.d_sae
d_sae_start = self.dict_saes[hook_position_start[0].name].cfg.d_sae
d_esae_end = self.dict_esaes[hook_position_end[0].name].cfg.d_sae
d_esae_start = self.dict_esaes[hook_position_start[0].name].cfg.d_sae
start_layer = int(hook_position_start[0].name.split(".")[1])
end_layer = int(hook_position_end[0].name.split(".")[1])
aggregate_dim = [0] if self.token_wise else [0, 1]
edge_effect = {}
all_error = []
all_feature_error = []
for end_node, end_index in self.active_nodes(*hook_position_end):
if isinstance(end_index, ErrorIndex):
all_error.append((end_node, end_index))
elif isinstance(end_index, FeatureErrorIndex):
all_feature_error.append((end_node, end_index))
elif isinstance(end_index, FeatureIndex):
feat_id = end_index.idx[-1]
pos_end = end_index.idx[-2]
index = t.tensor(list(end_index.idx), device=self.device)
end_node_grad = leftvec.act[:, pos_end, feat_id].unsqueeze(-1).unsqueeze(-1) # (b, 1, 1)
end_feature_vec = self.dict_saes[end_node.name].W_enc[:, feat_id].unsqueeze(0).unsqueeze(0) # (1, 1, d_model)
if "mlp_out" in hook_position_start[0].name:
list_grad_dot_leftvec_tensor = self._DE_using_virtual_weight_cross( # (b, seq, d_model)
grad=end_feature_vec,
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
)
for i in range(len(list_grad_dot_leftvec_tensor)):
list_grad_dot_leftvec_tensor[i] *= end_node_grad # (b, seq, d_model)
grad_dot_leftvec = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=list_grad_dot_leftvec_tensor[-1] @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=list_grad_dot_leftvec_tensor[-1] @ self.dict_esaes[hook_position_start[0].name].W_dec.T if self.use_error_term else None,
resc=list_grad_dot_leftvec_tensor[-1] if self.use_esae_error_term else None
)
for i, upper_layer in enumerate(reversed(range(start_layer+1, end_layer))): # (start+1 -> end_layer-1)
rel_id = upper_layer-start_layer-1
grad_dot_leftvec += SparseAct(
# (b, seq, d_model) @ (d_model, d_sae) -> (b, seq, d_sae)
act = (
list_grad_dot_leftvec_tensor[i] @
self.dict_saes[hook_position_start[0].name].crosscoder_decoders[rel_id].weight
),
res = 0 if self.use_error_term else None, # type: ignore
resc = 0 if self.use_esae_error_term else None, # type: ignore
)
else:
grad_dot_leftvec_tensor = self._DE_using_virtual_weight( # (b, seq, d_model)
grad=end_feature_vec,
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
) * end_node_grad
grad_dot_leftvec = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=grad_dot_leftvec_tensor @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=grad_dot_leftvec_tensor @ self.dict_esaes[hook_position_start[0].name].W_dec.T if self.use_error_term else None,
resc=grad_dot_leftvec_tensor if self.use_esae_error_term else None,
)
'''
edge_effect shape (seq, d_sae+1, seq, d_sae+1) or (d_sae+1, d_sae+1) in sparse_coo tensor
the sparse_coo will have the shape:
--> indices of shape (2, num_active) or (1, num_active)
--> values of shape (num_active, seq, d_sae+1) or (num_active, d_sae+1)
'''
effect = (
grad_dot_leftvec * rightvec
).sum(aggregate_dim)
if self.use_esae_error_term:
effect.contract()
edge_effect[index] = effect.to_tensor() # (seq, d_sae_start+d_esae_start+1) | (d_sae_start+d_esae_start+1)
else:
raise ValueError(f"end_index of type {type(end_index)} is not supported.")
'''
The gradient of feature error node to upstream node is f_esae_enc - sum gradient of end_feature_node
The "grad_through_end_feat_error" computes the jacobian of f_esae_enc going through f_sae_dec and f_sae_enc
'''
if self.use_error_term:
grad_through_end_feat_error = einops.einsum(
self.dict_saes[hook_position_end[0].name].W_dec.T, self.dict_saes[hook_position_end[0].name].W_enc.T,
"d_model1 d_sae_end, d_sae_end d_model2 -> d_model1 d_model2"
)
grad_through_end_feat_error = einops.einsum( # gradient through sae_feature
self.dict_esaes[hook_position_end[0].name].W_enc.T, grad_through_end_feat_error,
"d_esae_end d_model1, d_model1 d_model2 -> d_esae_end d_model2"
)
for end_feature_error_node, end_feature_error_index in all_feature_error:
pos_end = end_feature_error_index.idx[0]
feat_id = end_feature_error_index.idx[-1]
revised_index = list(end_feature_error_index.idx)
revised_index[-1] += d_sae_end
index = t.tensor(revised_index, device=self.device)
end_node_grad = leftvec.res[:, pos_end, feat_id].unsqueeze(-1).unsqueeze(-1) # (b, 1, 1) # type: ignore
end_feature_vec = self.dict_esaes[end_feature_error_node.name].W_enc[:, feat_id].unsqueeze(0).unsqueeze(0) # (1, 1, d_model)
end_feature_error_grad_tensor = self._DE_using_virtual_weight( # (b, seq, d_model)
grad=end_feature_vec - grad_through_end_feat_error[feat_id].unsqueeze(0).unsqueeze(0), # (1, 1, d_model)
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
)
end_feature_error_grad = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=end_feature_error_grad_tensor @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=end_feature_error_grad_tensor @ self.dict_esaes[hook_position_start[0].name].W_dec.T,
resc=end_feature_error_grad_tensor if self.use_esae_error_term else None,
)
effect = (
end_feature_error_grad * rightvec
).sum(aggregate_dim)
if self.use_esae_error_term:
effect.contract()
edge_effect[index] = effect.to_tensor() # (seq, d_sae_start+d_esae_start+1) | (d_sae_start+d_esae_start+1)
'''
The gradient of feature error node to upstream node is:
f_esae_enc - sum gradient of end_feature_node - sum gradient of end_feaeture_error_node
The gradient of feature error node to upstream node is f_esae_enc - sum gradient of end_feature_node (see above)
The "grad_through_end_feat_error" computes the jacobian of f_esae_enc going through f_sae_dec and f_sae_enc
The "feature_error_coef_to_cal_error_edge" calculates the leftvec (metric gradient) at the esae_error | esae_feature
We can then have the contribution of feature_error_node by:
The contribution of feature_end_node is the gradient of f_esae_enc - sum grad_through_end_feat_error * feature_error_coef_to_cal_error_edge
The "end_feature_dependent" sums all of the gradient of end_feature_node.
'''
if self.use_esae_error_term and self.use_error_term:
all_end_node_grad: Tensor = leftvec.resc # type: ignore
'''
End feauture contribution
'''
feature_coef_to_cal_error_edge = einops.einsum(
all_end_node_grad, self.dict_saes[hook_position_end[0].name].W_dec,
"b seq d_model, d_sae_end d_model -> b seq d_sae_end",
)
end_feature_dependent = einops.einsum(
feature_coef_to_cal_error_edge, self.dict_saes[hook_position_end[0].name].W_enc,
"b seq d_sae_end, d_model d_sae_end -> b seq d_model"
)
'''
End feature error contribution
'''
grad_through_end_feat_error = einops.einsum(
self.dict_saes[hook_position_end[0].name].W_dec.T, self.dict_saes[hook_position_end[0].name].W_enc.T,
"d_model1 d_sae_end, d_sae_end d_model2 -> d_model1 d_model2"
)
grad_through_end_feat_error = einops.einsum( # gradient through sae_feature
self.dict_esaes[hook_position_end[0].name].W_enc.T, grad_through_end_feat_error,
"d_esae_end d_model1, d_model1 d_model2 -> d_esae_end d_model2"
)
feature_error_coef_to_cal_error_edge = einops.einsum(
all_end_node_grad, self.dict_esaes[hook_position_end[0].name].W_dec,
"b seq d_model, d_esae_end d_model -> b seq d_esae_end",
)
feature_error_dependent = einops.einsum(
self.dict_esaes[hook_position_end[0].name].W_enc.T - grad_through_end_feat_error,
feature_error_coef_to_cal_error_edge,
"d_esae_end d_model, b seq d_esae_end -> b seq d_model",
)
for end_error_node, end_error_index in all_error:
pos_end = end_error_index.idx[0]
index = t.tensor(list(end_error_index.idx + (d_sae_end+d_esae_end,)), device=self.device)
end_node_grad = leftvec.resc[:, pos_end].unsqueeze(1) # (b, 1, d_model) # type: ignore
end_error_grad_tensor = self._DE_using_virtual_weight( # (b, seq, d_model)
# (b, 1, d_model)
grad= end_node_grad - end_feature_dependent[:, pos_end].unsqueeze(1) - feature_error_dependent[:, pos_end].unsqueeze(1),
pos_end=pos_end,
batch_size=leftvec.act.shape[0],
unpatched_clean_cache=unpatched_clean_cache,
hook_position_end=hook_position_end,
hook_position_start=hook_position_start,
)
end_error_grad = SparseAct(
# (b, seq, d_model) @ (d_model, d_sae)
act=end_error_grad_tensor @ self.dict_saes[hook_position_start[0].name].W_dec.T,
res=end_error_grad_tensor @ self.dict_esaes[hook_position_start[0].name].W_dec.T,
resc=end_error_grad_tensor,
)
effect = (
end_error_grad * rightvec
).sum(aggregate_dim)
if self.use_esae_error_term:
effect.contract()
edge_effect[index] = effect.to_tensor() # (seq, d_sae_start+d_esae_start+1) | (d_sae_start+d_esae_start+1)
seq = int(self.seq_length) # type: ignore
num_end = d_sae_end
num_start = d_sae_start
if self.use_error_term:
num_end += d_esae_end
num_start += d_esae_start
if self.use_esae_error_term:
num_end += 1
num_start += 1
if len(edge_effect.keys()) != 0:
indices = t.stack(list(edge_effect.keys()), dim=0).T # shape (2, num_active) or (1, num_active)
values = t.stack([value for value in edge_effect.values()], dim=0) # shape (num_active, seq, d_sae+d_esae+1) or (num_active, d_sae+d_esae+1)
# if no active nodes, return empty tensor
else:
indices = t.empty((2, 0) if self.token_wise else (1, 0), dtype=t.long).to(self.device)
values = t.empty((0, seq, num_start) if self.token_wise else (0, num_start), dtype=t.float).to(self.device)
if self.token_wise:
return t.sparse_coo_tensor(indices, values, size=(seq, num_end, seq, num_start)).coalesce()
else:
return t.sparse_coo_tensor(indices, values, size=(num_end, num_start)).coalesce()
@contextmanager
def _setup_forward_model_hook(self, use_error_term: bool | None = None, transfer_grad: bool = True):
cross_cache = {}
# Hook function at transcoder input: caches the activations before transcoder
def hook_crosscoder_input(activations: Tensor, hook: HookPoint, crosscoder_idx: int):
cross_cache[crosscoder_idx] = activations.clone()
all_cross_recons = [0.0 for _ in range(len(self.crosscoders))]
def hook_crosscoder_output(activations: Tensor, hook: HookPoint, crosscoder_idx: int):
recons, cross_recons = self.crosscoders[crosscoder_idx].forward_crosscoder(
(cross_cache[crosscoder_idx], activations, all_cross_recons[crosscoder_idx])
)
for j in range(crosscoder_idx+1, self.n_layers):
all_cross_recons[j] += cross_recons[j-crosscoder_idx-1]
if transfer_grad:
return recons + (activations - activations.detach())
else:
return recons
fwd_hooks = []
for i in range(len(self.crosscoders)):
fwd_hooks.append((self.input_hooks[i], partial(hook_crosscoder_input, crosscoder_idx=i)))
fwd_hooks.append((self.output_hooks[i], partial(hook_crosscoder_output, crosscoder_idx=i)))
use_error_term = use_error_term if use_error_term is not None else self.use_error_term
try:
for sae in self._saes_to_list():
self.model.add_sae(sae, use_error_term)
for hook, func in fwd_hooks:
self.model.add_hook(hook, func, dir="fwd")
yield
finally:
self.model.reset_saes()
self.model.reset_hooks()
def _saes_to_list(self) -> List[Any]:
return Feature_Graph_Cross._saes_to_list(self) |