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, 43 insertions, 0 deletions

diff --git a/ep_run/casc_eq_train.py b/ep_run/casc_eq_train.py
index ed164ce..05c1e4e 100644
--- a/ep_run/casc_eq_train.py
+++ b/ep_run/casc_eq_train.py
@@ -53,6 +53,11 @@ ap.add_argument('--dgain_geo', type=float, default=0.0)   # >0: per-layer geomet
                                                           # gain = geo^l (layer 0 = x1), optionally capped
 ap.add_argument('--dgain_geo_cap', type=float, default=0.0) # >0: cap for the geometric profile
 ap.add_argument('--dgain_rand', type=float, default=0.0)
+ap.add_argument('--gen', type=int, default=0)               # >0: sample text from the resumed checkpoint and exit
+ap.add_argument('--gen_prompts', default='')                # '|'-separated prompts; empty = built-in set
+ap.add_argument('--gen_temp', type=float, default=0.8)
+ap.add_argument('--gen_topk', type=int, default=40)
+ap.add_argument('--gen_new', type=int, default=120)
 ap.add_argument('--probe_dgspec', type=int, default=0)      # >0: M1 spectroscopy, value = n batches; exits before training
 ap.add_argument('--probe_gains', default='1,2,4,8,16,32,64,128,256')
 ap.add_argument('--probe_f64', action='store_true')  # fp64 states+model in the probe: the fp-floor decisive arm  # >1: per-STEP log-uniform dgain_top in
@@ -859,6 +864,44 @@ if args.ddp_grad_test:
     import sys
     sys.exit(0)
 
+if args.gen > 0:
+    # Sampling from a resumed checkpoint. Inference here is an ordinary forward pass through the
+    # blocks, which is the point: EP appears only in training. The same code path loads an EP or a
+    # backprop checkpoint, since both scripts save the same keys, so the two can be sampled side by
+    # side under identical settings.
+    import sys
+    from tokenizers import Tokenizer as _Tok
+    _tk = _Tok.from_file(str(DD / 'tokenizer.json'))
+    prompts = [p for p in args.gen_prompts.split('|') if p] or [
+        'The main difference between a virus and a bacterium is',
+        'To find the area of a circle, you',
+        'In 1815, the eruption of Mount Tambora',
+        'Photosynthesis is the process by which',
+    ]
+    @torch.no_grad()
+    def _sample(prompt, n_new, temp, topk, seed):
+        torch.manual_seed(seed)
+        ids = _tk.encode(prompt).ids[:args.T - n_new - 1]
+        idx = torch.zeros(1, args.T, dtype=torch.long, device=dev)
+        L = len(ids)
+        idx[0, :L] = torch.tensor(ids, device=dev)
+        for _ in range(n_new):
+            if L >= args.T: break
+            z = emb(idx)
+            for b in blocks: z = b(z, mask)
+            lg = readout(z)[0, L - 1].float() / max(temp, 1e-6)
+            v, _i = torch.topk(lg, topk)
+            lg[lg < v[-1]] = -float('inf')
+            nt = torch.multinomial(F.softmax(lg, -1), 1).item()
+            idx[0, L] = nt; L += 1
+        return _tk.decode(idx[0, :L].tolist())
+    print(f'# samples from {args.resume}  (temp {args.gen_temp}, top-k {args.gen_topk})', flush=True)
+    for pi, p in enumerate(prompts):
+        for s in range(args.gen):
+            print(f'\n--- prompt {pi + 1}, sample {s + 1} ---', flush=True)
+            print(_sample(p, args.gen_new, args.gen_temp, args.gen_topk, 1000 * pi + s), flush=True)
+    sys.exit(0)
+
 if args.probe_dgspec > 0:
     # M1 DGAIN SPECTROSCOPY: per-block leak vector vs uniform read-displacement gain.
     # Paired design: L_l(g) = mean_b[gEP_l(g) - gBP_l] on the SAME batch — batch-sampling