| """Shared with set4_goldfish.py: cross-lingual sentence-level activations.""" |
| import sys |
| sys.path.insert(0,"/root/compose-audit") |
| from common import * |
|
|
| @torch.no_grad() |
| def sent_acts(model, tok, lines, dev, bs=16, maxlen=128): |
| """Mean-pooled per-sentence residual activations, {layer: (n_sent, d)} -- rows are matched |
| ACROSS LANGUAGES by FLORES sentence id, which is what makes a cross-lingual basis map fittable.""" |
| outs = None |
| for i in range(0, len(lines), bs): |
| enc = tok(lines[i:i + bs], return_tensors="pt", padding=True, truncation=True, max_length=maxlen) |
| ids = enc["input_ids"].to(dev); am = enc["attention_mask"].to(dev).float() |
| hs = model(ids, attention_mask=enc["attention_mask"].to(dev), output_hidden_states=True).hidden_states |
| if outs is None: |
| outs = [[] for _ in hs] |
| w = am / am.sum(1, keepdim=True).clamp(min=1) |
| for j, h in enumerate(hs): |
| outs[j].append((h.float() * w.unsqueeze(-1)).sum(1).cpu()) |
| return {j: torch.cat(o).numpy().astype(np.float64) for j, o in enumerate(outs)} |
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