RESULT 89终局 + 交付物: 135M完赛 EP 3.2087 vs BP 3.2074(+0.1% ppl), 旧读出+45.2%

窗口敏感性诚实记录: 97%窗给-0.0030(seed间距0.0059), 完赛大窗给+0.0013(间距0.0005, cosine
衰减后BP收拢); 以大窗为准且不得称"统计不可分"(2.6倍间距), 正确表述=差0.1%且处分辨极限,
seed数(BP n=2/EP n=1)不足以给区间。
交付: --gen模式(复用模型定义, EP/BP同代码路径)、fig_135m_curves(四曲线/横轴token/BP细线在上)、
EPT_135M_samples.ipynb(同prompt同种子并排, 输出已烤入, 代码真可跑)。

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 94 insertions, 0 deletions

diff --git a/ep_run/fig_curves.py b/ep_run/fig_curves.py
new file mode 100644
index 0000000..9185702
--- /dev/null
+++ b/ep_run/fig_curves.py
@@ -0,0 +1,94 @@
+"""Validation curves at 135M: EP with the readout fix, EP without it, and the backprop twins.
+
+The point of the figure is that the first and third are the same curve, and the second stops
+descending partway through. Run after fw135m_rlin completes; it reads the training logs directly.
+"""
+import re
+import statistics as st
+from pathlib import Path
+
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+
+RUNS = Path('/home/yurenh2/ept/ep_run/runs')
+TOTAL = 440000
+TOK_PER_STEP = 24 * 256   # effective batch x context
+
+
+def curve(tag):
+    pts = []
+    for line in (RUNS / f'{tag}.log').read_bytes().decode(errors='replace').splitlines():
+        m = re.match(rf'step (\d+)/{TOTAL} \| train [\d.]+ val ([\d.]+)', line)
+        if m:
+            pts.append((int(m.group(1)), float(m.group(2))))
+    pts.sort()
+    return pts
+
+
+def smooth(pts, win=25):
+    xs = [p[0] for p in pts]
+    ys = [p[1] for p in pts]
+    out = []
+    for i in range(len(ys)):
+        lo, hi = max(0, i - win // 2), min(len(ys), i + win // 2 + 1)
+        out.append(st.median(ys[lo:hi]))
+    return xs, out
+
+
+def main():
+    # EP is drawn thick and underneath; the backprop seeds go on top as thin lines, so the reader
+    # sees them tracking rather than being hidden by whichever curve was plotted last.
+    arms = [
+        ('fw135m_rlin',  'Equilibrium Propagation',     '#1f6feb', 3.0, '-',  2),
+        ('fw135m_bsign', 'Equilibrium Propagation,\nwithout the readout fix', '#d1442f', 2.6, '-', 2),
+        ('fw135m_bp',    'Backprop, seed 1',            '#2b3540', 1.0, '-',  4),
+        ('fw135m_bp_s2', 'Backprop, seed 2',            '#7d8794', 1.0, '-',  4),
+    ]
+    fig, ax = plt.subplots(figsize=(7.6, 4.9))
+    finals = {}
+    for tag, label, color, lw, ls, zo in arms:
+        pts = curve(tag)
+        if not pts:
+            print(f'skip {tag}: no data')
+            continue
+        xs, ys = smooth(pts)
+        toks = [x * TOK_PER_STEP / 1e9 for x in xs]
+        ax.plot(toks, ys, color=color, lw=lw, ls=ls, label=label, zorder=zo)
+        tail = [v for s, v in pts if s >= 0.9 * TOTAL]
+        finals[tag] = st.mean(tail) if tail else float('nan')
+        print(f'{tag:14s} last step {pts[-1][0]:>7}  tail mean {finals[tag]:.4f}')
+
+    ax.set_xlabel('training tokens (billions)')
+    ax.set_ylabel('validation cross-entropy')
+    ax.set_ylim(3.15, 4.35)
+    ax.set_xlim(0, TOTAL * TOK_PER_STEP / 1e9)
+    ax.grid(alpha=0.18, lw=0.7)
+    for side in ('top', 'right'):
+        ax.spines[side].set_visible(False)
+    h, l = ax.get_legend_handles_labels()
+    order = [2, 3, 0, 1]   # backprop seeds first in the legend, matching how one reads the plot
+    leg = ax.legend([h[i] for i in order], [l[i] for i in order],
+                    frameon=False, fontsize=9.5, loc='upper right', handlelength=1.6)
+    for t in leg.get_texts():
+        t.set_va('center')
+
+    if 'fw135m_bsign' in finals and 'fw135m_rlin' in finals:
+        ax.annotate('single-precision rounding in the contrast readout;\nthe run stops improving',
+                    xy=(1.9, finals['fw135m_bsign'] + 0.005), xytext=(1.35, 3.86),
+                    fontsize=9, color='#d1442f',
+                    arrowprops=dict(arrowstyle='-', color='#d1442f', lw=0.9, alpha=0.8))
+        ax.annotate(f"EP {finals['fw135m_rlin']:.3f}\nBP {st.mean([finals['fw135m_bp'], finals['fw135m_bp_s2']]):.3f}",
+                    xy=(2.62, 3.245), fontsize=9.5, color='#2b3540', ha='right')
+
+    ax.set_title('135M-parameter transformer language model, FineWeb-Edu',
+                 fontsize=10.5, color='#333', pad=10, loc='left')
+    fig.tight_layout()
+    for ext in ('png', 'pdf'):
+        p = Path(f'/home/yurenh2/ept/assets/figs/fig_135m_curves.{ext}')
+        fig.savefig(p, dpi=300, bbox_inches='tight')
+        print('wrote', p)
+
+
+if __name__ == '__main__':
+    main()