File size: 8,619 Bytes
56e82ec | 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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """Convert spectral analysis JSON results to CSV tables for analysis."""
import json
import csv
import sys
import os
import re
from pathlib import Path
def classify_layer(name, model_type):
"""Classify a weight matrix by layer index, component type, and phase."""
if model_type == "prisma":
# mirror_blocks.N.component
m = re.match(r'mirror_blocks\.(\d+)\.', name)
if m:
layer_idx = int(m.group(1))
phase = "mirror"
if 'attn' in name:
comp = 'Q' if 'q_proj' in name else 'K' if 'k_proj' in name else 'V' if 'v_proj' in name else 'O' if 'o_proj' in name else 'attn'
elif 'ffn.w3' in name or 'gate_expand' in name:
comp = 'W3'
elif 'ffn.w4' in name or 'gate_compress' in name:
comp = 'W4'
elif 'ffn.w1' in name:
comp = 'W1'
elif 'w2' in name:
comp = 'W2'
else:
comp = 'other'
return layer_idx, comp, phase
m = re.match(r'middle_blocks\.(\d+)\.', name)
if m:
layer_idx = int(m.group(1))
phase = "middle"
if 'attn' in name:
comp = 'Q' if 'q_proj' in name else 'K' if 'k_proj' in name else 'V' if 'v_proj' in name else 'O' if 'o_proj' in name else 'attn'
elif 'gate' in name:
comp = 'W3'
elif 'ffn.w1' in name:
comp = 'W1'
elif 'ffn.w2' in name:
comp = 'W2'
else:
comp = 'other'
return layer_idx, comp, phase
m = re.match(r'(first|last)_block\.', name)
if m:
phase = m.group(1)
if 'attn' in name:
comp = 'Q' if 'q_proj' in name else 'K' if 'k_proj' in name else 'V' if 'v_proj' in name else 'O' if 'o_proj' in name else 'attn'
elif 'ffn.w3' in name or 'gate' in name:
comp = 'W3'
elif 'ffn.w4' in name:
comp = 'W4'
elif 'ffn.w1' in name:
comp = 'W1'
elif 'ffn.w2' in name:
comp = 'W2'
else:
comp = 'other'
return 0, comp, phase
if 'embed' in name:
return -1, 'embed', 'embed'
if 'head' in name or 'lm_head' in name:
return 99, 'head', 'head'
return -1, 'other', 'other'
else: # GPT-2 style
m = re.match(r'transformer\.h\.(\d+)\.', name)
if m:
layer_idx = int(m.group(1))
if 'c_attn' in name:
comp = 'QKV'
elif 'c_proj' in name and 'mlp' not in name:
comp = 'O'
elif 'c_fc' in name:
comp = 'W1'
elif 'mlp.c_proj' in name:
comp = 'W2'
else:
comp = 'other'
return layer_idx, comp, "layer"
if 'wte' in name:
return -1, 'embed', 'embed'
if 'wpe' in name:
return -1, 'pos_embed', 'embed'
return -1, 'other', 'other'
def json_to_csvs(json_path, output_dir, model_type="prisma"):
with open(json_path) as f:
data = json.load(f)
os.makedirs(output_dir, exist_ok=True)
# 1. Full weight matrix summary
rows = []
for name, info in data.items():
if 'activation' in name or name.startswith('_'):
continue
layer_idx, comp, phase = classify_layer(name, model_type)
rows.append({
'name': name,
'layer_idx': layer_idx,
'component': comp,
'phase': phase,
'shape': 'x'.join(str(s) for s in info['shape']),
'effective_rank': round(info['effective_rank'], 2),
'stable_rank': round(info['stable_rank'], 3),
'spectral_norm': round(info['spectral_norm'], 4),
'frobenius_norm': round(info['frobenius_norm'], 4),
'alpha': round(info['alpha'], 4),
'alpha_r2': round(info['alpha_r2'], 4),
'signal_ratio': round(info['signal_ratio'], 4),
'condition_number': round(info['condition_number'], 2),
'mp_bound': round(info['mp_bound'], 4),
'n_above_mp': info['n_above_mp'],
'n_total': info['n_total'],
'sv_1': round(info['top_10_sv'][0], 4) if info['top_10_sv'] else 0,
'sv_2': round(info['top_10_sv'][1], 4) if len(info['top_10_sv']) > 1 else 0,
'sv_10': round(info['top_10_sv'][9], 4) if len(info['top_10_sv']) > 9 else 0,
'sv1_sv2_ratio': round(info['top_10_sv'][0] / info['top_10_sv'][1], 4) if len(info['top_10_sv']) > 1 and info['top_10_sv'][1] > 0 else 0,
})
with open(os.path.join(output_dir, 'weights_full.csv'), 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=rows[0].keys())
w.writeheader()
w.writerows(sorted(rows, key=lambda r: (r['phase'], r['layer_idx'], r['component'])))
# 2. Layer-level FFN summary (W1 progression = the lens)
ffn_rows = [r for r in rows if r['component'] == 'W1']
with open(os.path.join(output_dir, 'ffn_w1_progression.csv'), 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=['layer_idx', 'phase', 'effective_rank', 'stable_rank', 'alpha', 'alpha_r2', 'signal_ratio', 'condition_number', 'sv1_sv2_ratio'])
w.writeheader()
for r in sorted(ffn_rows, key=lambda r: (r['phase'], r['layer_idx'])):
w.writerow({k: r[k] for k in w.fieldnames})
# 3. Gate comparison (W3 vs W4)
gate_rows = [r for r in rows if r['component'] in ('W3', 'W4') and r['phase'] == 'mirror']
with open(os.path.join(output_dir, 'gate_comparison.csv'), 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=['layer_idx', 'component', 'effective_rank', 'stable_rank', 'alpha', 'alpha_r2', 'signal_ratio', 'sv1_sv2_ratio'])
w.writeheader()
for r in sorted(gate_rows, key=lambda r: (r['layer_idx'], r['component'])):
w.writerow({k: r[k] for k in w.fieldnames})
# 4. Attention head comparison (Q, K, V, O per layer)
attn_rows = [r for r in rows if r['component'] in ('Q', 'K', 'V', 'O', 'QKV')]
with open(os.path.join(output_dir, 'attention_progression.csv'), 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=['layer_idx', 'phase', 'component', 'effective_rank', 'stable_rank', 'alpha', 'signal_ratio', 'condition_number'])
w.writeheader()
for r in sorted(attn_rows, key=lambda r: (r['phase'], r['layer_idx'], r['component'])):
w.writerow({k: r[k] for k in w.fieldnames})
# 5. Summary statistics
alphas = [r['alpha'] for r in rows if r['alpha'] > 0]
eff_ranks = [r['effective_rank'] for r in rows if r['layer_idx'] >= 0]
signal_ratios = [r['signal_ratio'] for r in rows if r['layer_idx'] >= 0]
summary = {
'n_matrices': len(rows),
'mean_alpha': round(sum(alphas) / len(alphas), 4) if alphas else 0,
'min_alpha': round(min(alphas), 4) if alphas else 0,
'max_alpha': round(max(alphas), 4) if alphas else 0,
'mean_effective_rank': round(sum(eff_ranks) / len(eff_ranks), 2) if eff_ranks else 0,
'mean_signal_ratio': round(sum(signal_ratios) / len(signal_ratios), 4) if signal_ratios else 0,
'n_well_trained (alpha<2)': sum(1 for a in alphas if a < 2.0),
'n_total_alpha': len(alphas),
}
with open(os.path.join(output_dir, 'summary.csv'), 'w', newline='') as f:
w = csv.DictWriter(f, fieldnames=summary.keys())
w.writeheader()
w.writerow(summary)
print(f"Wrote CSVs to {output_dir}/")
print(f" weights_full.csv ({len(rows)} matrices)")
print(f" ffn_w1_progression.csv ({len(ffn_rows)} layers)")
print(f" gate_comparison.csv ({len(gate_rows)} entries)")
print(f" attention_progression.csv ({len(attn_rows)} entries)")
print(f" summary.csv")
if __name__ == '__main__':
base = "circuits/scripts/spectral_output/mirrored_300M_mk4_cont"
# Prisma
json_to_csvs(
f"{base}/results.json",
f"{base}/csv_prisma",
model_type="prisma"
)
# GPT-2 medium
if os.path.exists(f"{base}/results_b.json"):
json_to_csvs(
f"{base}/results_b.json",
f"{base}/csv_gpt2",
model_type="gpt2"
)
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