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from collections.abc import Sequence
from pathlib import Path
from typing import Callable
import numpy as np
import pandas as pd
import torch
from sae_lens import SAE
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from torch import nn
from tqdm.autonotebook import tqdm
from transformer_lens import HookedTransformer
from transformer_lens.hook_points import HookedRootModule
from functools import partial
# from sae_bench.evals.absorption.common import (
# PROBES_DIR,
# RESULTS_DIR,
# get_or_make_dir,
# load_df_or_run,
# load_dfs_or_run,
# load_or_train_probe,
# load_probe_data_split_or_train,
# )
from .probing import LinearProbe, train_multi_probe
from .util import batchify, DEFAULT_DEVICE
from .util import get_sae_acts
EPS = 1e-6
SPARSE_PROBING_EXPERIMENT_NAME = "k_sparse_probing"
class KSparseProbe(nn.Module):
weight: torch.Tensor # shape (k)
bias: torch.Tensor # scalar
feature_ids: torch.Tensor # shape (k)
def __init__(
self, weight: torch.Tensor, bias: torch.Tensor, feature_ids: torch.Tensor
):
super().__init__()
self.weight = weight
self.bias = bias
self.feature_ids = feature_ids
@property
def k(self) -> int:
return self.weight.shape[0]
def forward(self, x: torch.Tensor) -> torch.Tensor:
filtered_acts = x[..., self.feature_ids]
return filtered_acts @ self.weight + self.bias
def to(self, device: torch.device | str):
self.weight = self.weight.to(device)
self.bias = self.bias.to(device)
self.feature_ids = self.feature_ids.to(device)
def train_sparse_multi_probe(
x_train: torch.Tensor, # tensor of shape (num_samples, input_dim)
y_train: torch.Tensor, # tensor of shape (num_samples, num_probes), with values in [0, 1]
device: torch.device,
l1_decay: float = 0.01, # l1 regularization strength
num_probes: int | None = None, # inferred from y_train if None
batch_size: int = 4096,
num_epochs: int = 50,
lr: float = 0.01,
end_lr: float = 1e-5,
l2_decay: float = 1e-6,
show_progress: bool = True,
verbose: bool = False,
map_acts: Callable[[torch.Tensor], torch.Tensor] | None = None,
probe_dim: int | None = None,
) -> LinearProbe:
"""
Train a multi-probe with L1 regularization on the weights.
"""
return train_multi_probe(
x_train,
y_train,
num_probes=num_probes,
batch_size=batch_size,
num_epochs=num_epochs,
lr=lr,
end_lr=end_lr,
weight_decay=l2_decay,
show_progress=show_progress,
verbose=verbose,
device=device,
extra_loss_fn=lambda probe, _x, _y: l1_decay * probe.weights.abs().sum(dim=-1).mean(),
map_acts=map_acts,
probe_dim=probe_dim,
)
def train_k_sparse_probes(
sae: HookedRootModule,
train_labels: list[tuple[str, int]], # list of (token, letter number) pairs
train_activations: torch.Tensor, # n_vocab X d_model
ks: Sequence[int],
map_acts: Callable[[torch.Tensor], torch.Tensor] | None = None,
l1_decay: float = 0.01,
batch_size_sae: int = 128,
batch_size_lr: int = 4096,
num_epochs: int = 50,
device: torch.device | str = "cpu",
) -> dict[int, dict[int, KSparseProbe]]: # dict[k, dict[letter_id, probe]]
"""
Train k-sparse probes for each k in ks.
Returns a dict of dicts, where the outer dict is indexed by k and the inner dict is the label.
"""
results: dict[int, dict[int, KSparseProbe]] = defaultdict(dict)
with torch.no_grad():
labels = {label for _, label in train_labels}
sparse_train_y = torch.nn.functional.one_hot(
torch.tensor([idx for _, idx in train_labels])
)
if map_acts is None:
map_acts = partial(
get_sae_acts, sae=sae, batch_size=batch_size_lr, device=device, verbose=False, convert_to_cpu=False
)
l1_probe = (
train_sparse_multi_probe(
train_activations,
sparse_train_y,
l1_decay=l1_decay,
num_epochs=num_epochs,
batch_size=batch_size_lr,
device=device,
map_acts=map_acts,
probe_dim=sae.cfg.d_sae,
)
.float()
.cpu()
)
feature_acts = get_sae_acts(
train_activations, sae, batch_size_sae, device, verbose=True, convert_to_cpu=True
).numpy()
with torch.no_grad():
train_k_y = np.array([idx for _, idx in train_labels])
with tqdm(total=len(ks) * len(labels), desc="training k-probes") as pbar:
for k in ks:
for label in labels:
# using topk and not abs() because we only want features that directly predict the label
sparse_feat_ids = l1_probe.weights[label].topk(k).indices
train_k_x = feature_acts[..., sparse_feat_ids]
if k==1:
train_k_x = train_k_x.reshape(-1, 1)
# Use SKLearn here because it's much faster than torch if the data is small
sk_probe = LogisticRegression(
max_iter=500, class_weight="balanced"
).fit(train_k_x, (train_k_y == label).astype(np.int64))
probe = KSparseProbe(
weight=torch.tensor(sk_probe.coef_[0]).float(),
bias=torch.tensor(sk_probe.intercept_[0]).float(), # type: ignore
feature_ids=sparse_feat_ids,
)
results[k][label] = probe
pbar.update(1)
return results
def train_l1_probe(
sae: HookedRootModule,
y_train: torch.Tensor,
x_train: torch.Tensor,
map_acts: Callable[[torch.Tensor], torch.Tensor] | None = None,
l1_decay: float = 0.01,
batch_size_lr: int = 4096,
num_epochs: int = 50,
device: torch.device | str = DEFAULT_DEVICE,
) -> LinearProbe:
if map_acts is None:
map_acts = partial(
get_sae_acts, sae=sae, batch_size=batch_size_lr, device=device, verbose=False, convert_to_cpu=False
)
l1_probe = (
train_sparse_multi_probe(
x_train,
y_train,
l1_decay=l1_decay,
num_epochs=num_epochs,
batch_size=batch_size_lr,
device=device,
map_acts=map_acts,
probe_dim=sae.cfg.d_sae,
)
.float()
.cpu()
)
return l1_probe
def get_probe_predictions(
probe: LinearProbe | KSparseProbe,
x: torch.Tensor,
batch_size_lr: int = 4096,
device: torch.device | str = DEFAULT_DEVICE,
) -> torch.Tensor:
probe.eval()
probe.to(device)
predictions = []
with torch.no_grad():
for batch_x in batchify(x, batch_size_lr, show_progress=False):
batch_x = batch_x.to(device)
preds: torch.Tensor = probe(batch_x)
predictions.append((torch.sigmoid(preds) > 0.5).cpu())
return torch.cat(predictions, dim=0) # shape (num_samples) |