File size: 2,143 Bytes
55d72e6 | 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 | """Penultimate-layer CLS embedding extractor.
Faithful port of ``max_probe_fine_tune_seeds/produce_embeddings.py:
extract_features`` (L77) using the same ``src/utils/pooling.py:
CLSTokenPooling(layer_number=-1)`` semantics, but driven by our YAML
config rather than Hydra.
For each split it writes:
outputs/<cfg>/baselines/<cfg>_embeddings_<split>.pkl
a Python pickle with keys:
features : (N, D) float32 penultimate-layer CLS pooled features
logits : (N, C) float32 classifier logits (matches predictions parquet)
targets : (N,) int64 ground-truth labels
idx : (N,) int64 sample indices (aligned with predictions_<split>.parquet["idx"])
This format is designed so all downstream baselines (Mahalanobis × 4,
probes) can read it without modification.
"""
from __future__ import annotations
from pathlib import Path
from typing import List, Tuple
import numpy as np
import pandas as pd
import torch
from tqdm import tqdm
@torch.no_grad()
def extract_penultimate(model, dataloader, device, pooling):
"""Run the model over the dataloader and pool the requested layer.
Uses the seq-classification model head (logits shape (B, C)).
``pooling`` is a callable with the signature
``(hidden_states, input_ids, model) -> (B, D)``.
"""
model.eval()
feats, logits, targets = [], [], []
use_autocast = device.type == "cuda"
for batch in tqdm(dataloader, desc="penultimate-cls"):
ids = batch["input_ids"].to(device)
att = batch["attention_mask"].to(device)
lbl = batch["label"]
with torch.autocast(device_type=device.type, enabled=use_autocast):
out = model(input_ids=ids, attention_mask=att,
output_hidden_states=True, output_attentions=False)
pooled = pooling(out.hidden_states, input_ids=ids, model=model)
feats.append(pooled.float().cpu().numpy())
logits.append(out.logits.float().cpu().numpy())
targets.append(lbl.cpu().numpy())
return (
np.concatenate(feats, axis=0),
np.concatenate(logits, axis=0),
np.concatenate(targets, axis=0),
)
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