src/cross_features/eapt.py

"""EAPT — Edge-Attribution-Patching Topology (Phase 4, idx 11).

Simplified port of EAP-IG (Hanna et al. COLM 2024). Instead of patching
arbitrary edges between MLPs/attention heads at activation-tensor
granularity (which requires a full circuit tracer), we treat each
attention head ``(l, h)`` as a node and score its causal contribution
to the predicted class via integrated-gradients on the attention output:

  score(l, h) = ∫_0^1  (clean − corrupt)_{l,h} · ∂L/∂(activation)_{l,h}  dα

where ``corrupt`` is a per-token-mean baseline. We approximate the
integral with K_STEPS = 4 Riemann steps. Top-TOP_EDGES = 144 head-scores
define a similarity graph; we run Ripser persistence on it plus a small
set of spectral / SCC scalars.

Output dim = 18 + 5 = 23.
"""
from __future__ import annotations

import numpy as np
import torch
import torch.nn.functional as F

from .persistence_summary import ripser_summary_from_distance, column_names as pcol


K_STEPS = 4
L = 12
H = 12


def _columns():
    cols = list(pcol("eapt_pers"))
    cols.extend(["eapt_spec_rad", "eapt_lap_eig1", "eapt_scc_n",
                  "eapt_top10_sum", "eapt_top10_std"])
    return cols


COLUMNS = _columns()
DIM = len(COLUMNS)


def _attention_modules(model):
    if hasattr(model, "roberta"):
        return [l.attention.output.dense for l in model.roberta.encoder.layer]
    if hasattr(model, "electra"):
        return [l.attention.output.dense for l in model.electra.encoder.layer]
    return [l.attention.output.dense for l in model.encoder.layer]


def extract_eapt(model, input_ids, attention_mask, cache, pred_label=None):
    """Return (features (B, 23), columns)."""
    B = input_ids.shape[0]
    feats = np.zeros((B, DIM), dtype=np.float32)
    if pred_label is None:
        with torch.no_grad():
            pred_label = model(input_ids=input_ids, attention_mask=attention_mask).logits.argmax(dim=-1)

    attn_dense_modules = _attention_modules(model)

    for b in range(B):
        ids = input_ids[b:b + 1]
        att = attention_mask[b:b + 1]
        tgt = pred_label[b:b + 1]
        # Clean activation outputs of attention.output.dense (per layer)
        clean_acts = {}
        handles = []
        def make_clean_hook(idx):
            def fn(mod, inputs, output):
                clean_acts[idx] = output.detach().clone()
            return fn
        for l, m in enumerate(attn_dense_modules):
            handles.append(m.register_forward_hook(make_clean_hook(l)))
        with torch.no_grad():
            model(input_ids=ids, attention_mask=att)
        for h in handles: h.remove()

        # Corrupt activation: per-token mean across batch position (here B=1 so just mean across tokens)
        corrupt_acts = {l: clean_acts[l].mean(dim=1, keepdim=True).expand_as(clean_acts[l])
                          for l in clean_acts}

        # IG: K_STEPS Riemann steps, accumulate (clean - corrupt) * grad
        edge_score = np.zeros((L, H), dtype=np.float32)
        head_dim = model.config.hidden_size // H
        for step in range(K_STEPS):
            alpha = (step + 0.5) / K_STEPS
            mix = {l: alpha * clean_acts[l] + (1.0 - alpha) * corrupt_acts[l]
                    for l in clean_acts}
            # Replace each layer's attention.output.dense output with mix via hooks
            patch_handles = []
            def make_patch_hook(idx):
                def fn(mod, inputs, output):
                    return mix[idx]
                return fn
            for l, m in enumerate(attn_dense_modules):
                # Make the hook leaf require grad by replacing the output
                mix[l].requires_grad_(True)
                patch_handles.append(m.register_forward_hook(make_patch_hook(l)))
            model.zero_grad(set_to_none=True)
            out = model(input_ids=ids, attention_mask=att)
            loss = F.cross_entropy(out.logits, tgt)
            grads = torch.autograd.grad(loss, [mix[l] for l in range(L)], allow_unused=True)
            for h in patch_handles: h.remove()
            # score(l, h) = sum over tokens, hidden_dim of head h, of (clean - corrupt) * grad
            for l in range(L):
                if grads[l] is None: continue
                delta = clean_acts[l] - corrupt_acts[l]            # (1, T, D)
                contrib = (delta * grads[l]).sum(dim=1)            # (1, D)
                # Project per head
                contrib_h = contrib.view(1, H, head_dim).sum(dim=-1).cpu().numpy()
                edge_score[l] += contrib_h[0] / K_STEPS

        # Top edges → graph
        # Build (L*H) × (L*H) similarity from outer product of head scores
        scores_flat = edge_score.flatten()
        S = np.outer(np.abs(scores_flat), np.abs(scores_flat))
        N = S.shape[0]
        # Persistence on (1 - normalised S) as a distance
        S_n = S / (S.max() + 1e-12)
        Dmat = 1.0 - S_n
        np.fill_diagonal(Dmat, 0.0)
        pers, _ = ripser_summary_from_distance(Dmat, maxdim=1, prefix="eapt_pers")
        feats[b, 0:18] = pers

        # Spectral radius of S
        try:
            ev = np.linalg.eigvalsh(0.5 * (S + S.T))
            spec_rad = float(np.max(np.abs(ev)))
        except np.linalg.LinAlgError:
            spec_rad = 0.0
        feats[b, 18] = spec_rad

        # Top eigenvalue of normalised Laplacian
        Aabs = np.abs(S_n); np.fill_diagonal(Aabs, 0.0)
        d = Aabs.sum(axis=1); d = np.where(d > 0, d, 1.0)
        Dinv = 1.0 / np.sqrt(d)
        Lap = np.eye(N) - (Dinv[:, None] * Aabs * Dinv[None, :])
        try:
            le = np.linalg.eigvalsh(0.5 * (Lap + Lap.T))
            feats[b, 19] = float(np.sort(le)[::-1][0])
        except np.linalg.LinAlgError:
            pass

        # Number of strongly-connected components of |S| > 0.1 quantile threshold
        thr = np.quantile(Aabs[Aabs > 0], 0.5) if (Aabs > 0).any() else 0.0
        Adj = (Aabs > thr).astype(np.int8)
        # SCC via SciPy
        try:
            from scipy.sparse.csgraph import connected_components
            n_scc, _ = connected_components(Adj, directed=False, connection="strong")
            feats[b, 20] = float(n_scc)
        except Exception:
            feats[b, 20] = 0.0

        # Top-10 statistics of |scores_flat|
        top10 = np.sort(np.abs(scores_flat))[::-1][:10]
        feats[b, 21] = float(top10.sum())
        feats[b, 22] = float(top10.std())
    return feats, COLUMNS