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wassname
2b1d2b7493
mean dilutes the few incoherent positions that carry the collapse: #101's token loops had mean per-position kl_rev ~0.38, under the tau=0.5 hinge, so the barrier never fired (journal h/i). Incoherence is outlier-driven, so rmse/p95/max are sensitive to it (scripts/diag_kl_agg.py synthetic: same loop = rmse 1.5 / p95 3.8 / max 8.1 vs coherent ~0.03; sep ratio grows 21x->58x->77x->85x from mean to max). rmse default for the new arm (smooth dense gradient). eps inside the sqrt: B=0 LoRA init zeros every kl_pos at step 0 and bare sqrt(0) has inf grad -> 0*nan. mean stays the config default = no change to existing runs. Queued as the next loop arm (kl_rev rmse, ref=base, tau=1.0). Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
55 lines
2.5 KiB
Python
55 lines
2.5 KiB
Python
"""Why mean-KL is blind to the coherence collapse, and rmse/p95 are not (journal-supporting).
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No GPU, no model: synthetic next-token distributions (ml-debug Part 3 loss-surface check).
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A coherent-trait student shifts a little mass toward a base-PLAUSIBLE token at every position;
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an incoherent student is base everywhere except a few positions that spike on a base-IMPROBABLE
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token (a token loop). We aggregate the per-position KL the way the heal barrier does and show
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that mean dilutes the loop under the hinge threshold while outlier aggregates catch it.
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"""
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import numpy as np
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from tabulate import tabulate
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rng = np.random.default_rng(0)
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V, T = 200, 60 # vocab, positions in a completion
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def softmax(z):
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z = z - z.max(-1, keepdims=True)
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e = np.exp(z)
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return e / e.sum(-1, keepdims=True)
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base_logits = rng.standard_normal((T, V))
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p_ref = softmax(base_logits)
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order = np.argsort(p_ref.mean(0))
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trait_tok = order[len(order) // 2] # mid-prob = base-PLAUSIBLE (where coherent trait lands)
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loop_tok = order[3] # near-lowest = base-IMPROBABLE (where a loop lands)
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tl = base_logits.copy(); tl[:, trait_tok] += 1.6 # broad small shift, EVERY position
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p_trait = softmax(tl)
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ll = base_logits.copy()
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for t in (12, 13, 14, 15): # 4 spiked positions out of 60
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ll[t] = -10; ll[t, loop_tok] = 12.0
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p_loop = softmax(ll)
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def kl_pos(p, q): # per-position KL(student || base), vocab summed (as in heal._kl_per_pos)
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return (p * (np.log(np.clip(p, 1e-9, 1)) - np.log(np.clip(q, 1e-9, 1)))).sum(-1)
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AGGS = {"mean_t": lambda k: k.mean(),
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"rmse_t": lambda k: np.sqrt((k ** 2).mean()),
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"p95_t": lambda k: np.percentile(k, 95),
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"max_t": lambda k: k.max()}
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rows = []
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for name, p in [("coherent trait", p_trait), ("incoherent loop", p_loop)]:
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k = kl_pos(p, p_ref)
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rows.append([name] + [f"{f(k):.3f}" for f in AGGS.values()])
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rows.append(["sep ratio (loop/trait)"] +
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[f"{f(kl_pos(p_loop, p_ref)) / f(kl_pos(p_trait, p_ref)):.1f}x" for f in AGGS.values()])
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print(tabulate(rows, headers=["student (60 positions)", *AGGS], tablefmt="github"))
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print("\nSHOULD: incoherent-loop mean_t KL ~0.38 sits UNDER a tau=0.5 hinge, so relu(mean-tau)=0 and the")
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print("barrier never fires (the #101 collapse). The SAME loop has rmse_t ~1.5 / p95_t ~3.8, well over tau,")
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print("so an outlier-aggregated barrier fires on it. If mean_t separated loop from trait as well as rmse_t,")
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print("the outlier aggregation would buy nothing -- the point is the sep ratio GROWS from mean to rmse/p95.")
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