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import argparse
import json
from pathlib import Path
import numpy as np
import scipy.sparse as sp
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from experiments.common import ARTIFACT_DIR, DATA_DIR, load_jsonl, set_seed
from featurelens.config import SETTINGS
from featurelens.metrics import reconstruction_metrics
from featurelens.sae import SAEStore, SparseEncoding
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description='Collect residual and Qwen-Scope SAE activations.')
parser.add_argument('--input', type=Path, default=DATA_DIR / 'prompts.jsonl')
parser.add_argument('--output-dir', type=Path, default=ARTIFACT_DIR / 'activations')
parser.add_argument('--batch-size', type=int, default=16)
parser.add_argument('--max-length', type=int, default=192)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--layers', type=int, nargs='+', default=list(SETTINGS.layers))
return parser.parse_args()
def _build_sparse(encodings: list[SparseEncoding], n_rows: int, width: int) -> sp.csr_matrix:
row_ids: list[int] = []
col_ids: list[int] = []
values: list[float] = []
for row, encoding in enumerate(encodings):
idx = encoding.indices.detach().cpu().numpy().reshape(-1)
vals = encoding.values.detach().float().cpu().numpy().reshape(-1)
positive = vals > 0
row_ids.extend([row] * int(positive.sum()))
col_ids.extend(idx[positive].astype(int).tolist())
values.extend(vals[positive].astype(float).tolist())
return sp.csr_matrix((values, (row_ids, col_ids)), shape=(n_rows, width), dtype=np.float32)
def _promptwide_max_encoding(
token_encoding: SparseEncoding,
attention_mask: torch.Tensor,
) -> list[SparseEncoding]:
"""Max-pool sparse feature activations across non-padding tokens for each prompt."""
indices = token_encoding.indices.detach().cpu()
values = token_encoding.values.detach().float().cpu()
mask = attention_mask.detach().bool().cpu()
pooled: list[SparseEncoding] = []
for row_idx in range(indices.shape[0]):
feature_max: dict[int, float] = {}
valid_positions = torch.nonzero(mask[row_idx], as_tuple=False).reshape(-1).tolist()
for token_idx in valid_positions:
token_ids = indices[row_idx, token_idx].reshape(-1).tolist()
token_values = values[row_idx, token_idx].reshape(-1).tolist()
for feature_id, activation in zip(token_ids, token_values, strict=True):
activation = float(activation)
if activation <= 0.0:
continue
feature_id = int(feature_id)
if activation > feature_max.get(feature_id, 0.0):
feature_max[feature_id] = activation
if feature_max:
ordered = sorted(feature_max.items())
pooled.append(
SparseEncoding(
indices=torch.tensor([item[0] for item in ordered], dtype=torch.long),
values=torch.tensor([item[1] for item in ordered], dtype=torch.float32),
)
)
else:
pooled.append(
SparseEncoding(
indices=torch.empty(0, dtype=torch.long),
values=torch.empty(0, dtype=torch.float32),
)
)
return pooled
def _make_capture_hook(
captured: dict[int, torch.Tensor],
layer: int,
):
"""Bind the capture dictionary and layer before registering the hook."""
def hook(_module, _inputs, output):
hidden = output[0] if isinstance(output, tuple) else output
captured[layer] = hidden.detach()
return hook
@torch.inference_mode()
def main() -> None:
args = parse_args()
set_seed(args.seed)
rows = load_jsonl(args.input)
args.output_dir.mkdir(parents=True, exist_ok=True)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_dtype = torch.float16 if device.type == 'cuda' else torch.float32
tokenizer = AutoTokenizer.from_pretrained(SETTINGS.model_id)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained(
SETTINGS.model_id,
torch_dtype=model_dtype,
low_cpu_mem_usage=True,
).to(device)
model.eval()
sae_store = SAEStore(
SETTINGS.sae_repo_id,
layers=args.layers,
device=device,
dtype=torch.float32,
top_k=SETTINGS.sae_top_k,
)
residuals: dict[int, list[np.ndarray]] = {layer: [] for layer in args.layers}
final_encodings: dict[int, list[SparseEncoding]] = {layer: [] for layer in args.layers}
promptwide_encodings: dict[int, list[SparseEncoding]] = {layer: [] for layer in args.layers}
recon_stats: dict[int, list[dict[str, float]]] = {layer: [] for layer in args.layers}
for start in range(0, len(rows), args.batch_size):
batch_rows = rows[start : start + args.batch_size]
texts = [row['text'] for row in batch_rows]
batch = tokenizer(
texts,
return_tensors='pt',
padding=True,
truncation=True,
max_length=args.max_length,
)
batch = {key: value.to(device) for key, value in batch.items()}
captured: dict[int, torch.Tensor] = {}
handles = []
for layer in args.layers:
handles.append(
model.model.layers[layer].register_forward_hook(
_make_capture_hook(captured, layer)
)
)
model(**batch, use_cache=False)
for handle in handles:
handle.remove()
for layer in args.layers:
sae = sae_store.get(layer)
hidden = captured[layer]
final_token_residuals = hidden[:, -1, :]
final_batch_encoding = sae.encode(final_token_residuals)
token_batch_encoding = sae.encode(hidden)
promptwide_batch = _promptwide_max_encoding(
token_batch_encoding,
batch['attention_mask'],
)
for row_idx in range(final_token_residuals.shape[0]):
residual = final_token_residuals[row_idx]
final_encoding = SparseEncoding(
indices=final_batch_encoding.indices[row_idx],
values=final_batch_encoding.values[row_idx],
)
reconstructed = sae.decode_sparse(final_encoding)
residuals[layer].append(
residual.detach().float().cpu().numpy().astype(np.float16)
)
final_encodings[layer].append(final_encoding)
promptwide_encodings[layer].append(promptwide_batch[row_idx])
recon_stats[layer].append(reconstruction_metrics(residual, reconstructed))
print(f'Processed {min(start + args.batch_size, len(rows))}/{len(rows)} prompts', flush=True)
for layer in args.layers:
residual_array = np.stack(residuals[layer], axis=0)
np.save(args.output_dir / f'residuals_layer{layer}.npy', residual_array)
promptwide_sparse = _build_sparse(
promptwide_encodings[layer],
len(rows),
SETTINGS.sae_width,
)
sp.save_npz(
args.output_dir / f'features_layer{layer}.npz',
promptwide_sparse,
compressed=True,
)
final_sparse = _build_sparse(
final_encodings[layer],
len(rows),
SETTINGS.sae_width,
)
sp.save_npz(
args.output_dir / f'features_final_layer{layer}.npz',
final_sparse,
compressed=True,
)
summary = {
'layer': layer,
'n_samples': len(rows),
'mean_cosine': float(np.mean([item['cosine'] for item in recon_stats[layer]])),
'mean_nmse': float(np.mean([item['nmse'] for item in recon_stats[layer]])),
'median_nmse': float(np.median([item['nmse'] for item in recon_stats[layer]])),
'mean_active_features_final_token': float(np.mean(np.diff(final_sparse.indptr))),
'mean_active_features_promptwide': float(np.mean(np.diff(promptwide_sparse.indptr))),
}
# Preserve the legacy key for report compatibility. It refers to final-token TopK activity.
summary['mean_active_features'] = summary['mean_active_features_final_token']
(args.output_dir / f'reconstruction_layer{layer}.json').write_text(
json.dumps(summary, indent=2),
encoding='utf-8',
)
metadata = {
'model_id': SETTINGS.model_id,
'sae_repo_id': SETTINGS.sae_repo_id,
'layers': args.layers,
'top_k': SETTINGS.sae_top_k,
'width': SETTINGS.sae_width,
'n_samples': len(rows),
'feature_pooling': 'prompt-wide max activation across non-padding tokens',
'feature_file_pattern': 'features_layer{layer}.npz',
'final_token_feature_file_pattern': 'features_final_layer{layer}.npz',
'dense_residual_pooling': 'final prompt token',
'rows': rows,
}
(args.output_dir / 'metadata.json').write_text(
json.dumps(metadata, indent=2),
encoding='utf-8',
)
print(f'Activation artifacts written to {args.output_dir}')
if __name__ == '__main__':
main()
|