Upload code/screen_sweep.py with huggingface_hub

code/screen_sweep.py

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+"""Broad ecosystem screen: how many RELEASED Llama-3.1-8B derivatives have actually left the base
+model's coordinate frame? Streams one model at a time (download -> screen -> delete) so the disk
+footprint stays at one checkpoint."""
+import os, sys, json, time, gc, shutil, traceback
+sys.path.insert(0, "/root/merge-accuracy")
+import numpy as np, torch
+from huggingface_hub import snapshot_download, hf_hub_download
+import ma_common as C, gmap
+from cheap_screen import screen
+from mergeschool.core import alignment as AL
+
+OUT = "/root/merge-accuracy/results/ecosystem_screen.json"
+CACHE = "/root/hf_cache_mergeacc"
+BASE = "meta-llama/Llama-3.1-8B"
+CAND = [
+    ("NousResearch/Hermes-3-Llama-3.1-8B", "instruct post-training", "Nous Research"),
+    ("allenai/Llama-3.1-Tulu-3-8B-SFT", "instruct SFT", "AI2"),
+    ("dphn/Dolphin3.0-Llama3.1-8B", "instruct post-training", "Dolphin"),
+    ("meta-llama/Llama-Guard-3-8B", "safety classifier", "Meta"),
+    ("fdtn-ai/Foundation-Sec-8B", "domain CPT (security)", "Foundation AI"),
+    ("OpenSciLM/Llama-3.1_OpenScholar-8B", "domain CPT (science)", "OpenSciLM"),
+    ("nvidia/OpenMath2-Llama3.1-8B", "domain SFT (maths)", "NVIDIA"),
+    ("NCSOFT/Llama-VARCO-8B-Instruct", "language CPT (Korean)", "NCSOFT"),
+    ("McGill-NLP/AfriqueLlama-8B", "language CPT (African)", "McGill NLP"),
+    ("Yiddish-NLP/MameLoshnLM", "language CPT (Yiddish)", "Yiddish-NLP"),
+    ("deepcogito/cogito-v1-preview-llama-8B", "instruct post-training", "Deep Cogito"),
+    ("tokyotech-llm/Llama-3.1-Swallow-8B-v0.2", "language CPT (Japanese)", "TokyoTech"),
+    ("aisingapore/Llama-SEA-LION-v3-8B", "language CPT (SEA)", "AI Singapore"),
+    ("microsoft/UserLM-8b", "role post-training", "Microsoft"),
+]
+res = json.load(open(OUT)) if os.path.exists(OUT) else {}
+
+mb = C.load_model(BASE, dev="cpu", dtype=torch.float32)
+sd_base = C.sd_np(mb); cfg = mb.config
+HID, NH, NKV, VOC = cfg.hidden_size, cfg.num_attention_heads, cfg.num_key_value_heads, cfg.vocab_size
+del mb; gc.collect()
+print(f"base ready HID={HID} NH={NH} NKV={NKV}", flush=True)
+
+for repo, kind, group in CAND:
+    if repo in res: continue
+    d = None
+    try:
+        cfp = hf_hub_download(repo, "config.json", cache_dir=CACHE)
+        c = json.load(open(cfp))
+        if (c.get("hidden_size") != HID or c.get("num_attention_heads") != NH
+                or c.get("num_key_value_heads") != NKV or c.get("vocab_size") != VOC
+                or c.get("num_hidden_layers") != cfg.num_hidden_layers):
+            res[repo] = {"kind": kind, "group": group, "status": "shape mismatch",
+                         "config": {k: c.get(k) for k in ("hidden_size", "num_attention_heads",
+                                                          "num_key_value_heads", "vocab_size",
+                                                          "num_hidden_layers")}}
+            print(f"SKIP {repo}: shape mismatch", flush=True)
+            json.dump(res, open(OUT, "w"), indent=1); continue
+        t0 = time.time()
+        d = snapshot_download(repo, allow_patterns=["*.safetensors", "*.json", "tokenizer*"],
+                              cache_dir=CACHE, max_workers=8)
+        dl = time.time() - t0
+        m = C.load_model(repo, dev="cpu", dtype=torch.float32)
+        sd = C.sd_np(m); del m; gc.collect()
+        t = time.time(); f_id, pres = screen(sd, sd_base, HID); dt = time.time() - t
+        keys = C.shared_keys(sd_base, sd)
+        a = np.concatenate([sd_base[k].ravel() for k in keys])
+        b = np.concatenate([sd[k].ravel() for k in keys])
+        wc = float(a @ b / (np.linalg.norm(a) * np.linalg.norm(b)))
+        rd = float(np.linalg.norm(a - b) / np.linalg.norm(a))
+        del a, b, sd; gc.collect()
+        res[repo] = {"kind": kind, "group": group, "status": "screened",
+                     "identity_fraction_worst_layer": f_id, "screen_seconds": dt,
+                     "download_seconds": dl, "layers_screened": len(pres),
+                     "weight_cosine_vs_base": wc, "rel_drift": rd,
+                     "frame_has_drifted": bool(f_id < 0.95)}
+        print(f"{repo:48s} id_frac={f_id:.4f} wcos={wc:.4f} drift={rd:.4f} "
+              f"-> {'DRIFTED' if f_id < 0.95 else 'same frame'}  ({dt:.0f}s)", flush=True)
+        json.dump(res, open(OUT, "w"), indent=1)
+    except Exception:
+        res[repo] = {"kind": kind, "group": group, "status": "error",
+                     "error": traceback.format_exc()[-400:]}
+        print(f"ERR {repo}: {traceback.format_exc()[-250:]}", flush=True)
+        json.dump(res, open(OUT, "w"), indent=1)
+    finally:
+        # stream and delete: keep at most one candidate checkpoint on disk
+        keep = ("Llama-3.1-8B", "Llama-3.1-8B-Instruct", "Swallow-8B-v0.1",
+                "typhoon2", "sea-lionv3-base")
+        if not any(k in repo for k in keep):
+            p = f"{CACHE}/models--" + repo.replace("/", "--")
+            if os.path.isdir(p):
+                shutil.rmtree(p, ignore_errors=True)
+        gc.collect()
+print("SWEEP_DONE", flush=True)