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"b9b4841f-1878-44a4-899b-1e67e4804474" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco_test expert=image\n", "[data] pulling 6 shard(s) of AbstractPhil/bertenstein-v1/image_coco_test (~497MB each) ...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00000-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 494MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "fe40852ff06840bcb8ddf64158e8805e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00000-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "089e0c0a29b9440cb5abb18375fce6a0" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 0: 833 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00001-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 494MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5bd479cce61d417c95e4f1f3d693e971" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00001-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "78033a4992c34fddacf267159b2cdf99" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 1: 833 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00002-of-00049.arro(…): reconstructing 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0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "893c97f0d81545d5be411f59c0e32943" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 3: 833 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00004-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 494MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "42e52b4026284a56a299f5c4b4c8e22a" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00004-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "30b4a3b146d0439bb0e99b2e9c9c3de9" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 4: 833 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00005-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 494MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "550202de00db4e70a3340c4a90201039" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00005-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "1a849e98c67247e596830ac4abbaa9dd" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 5: 833 rows\n", "[data] 4998 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4998 rows available; raise cfg.n_shards for the full 5000\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "checkpoints/final/model.safetensors: reconstructing file: 0%| | 0.00B / 230MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "499b537f0bf547079c5e05383dd65c23" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "checkpoints/final/model.safetensors: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f0d3eefc48ec45d9855e71691ba14052" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1961 erank=20.5/20.4\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0000)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0001 R@5=0.0007 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.3156 erank=1.0/20.6\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0004001600609626621, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0002 R@5=0.0008 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.4199 erank=1.0/20.6\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=6.71e-08\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0006002400914439932, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0015 cos_match=-0.0177 cos_rand=-0.01890 chance=0.00020 CV=0.2661 erank=41.8/44.3\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0002 R@5=0.0011 cos_match=-0.0060 cos_rand=-0.00601 chance=0.00020 CV=0.1381 erank=1.1/51.8\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1721 erank=20.5/20.4 (accidental fixed points: 1)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.0002 R@5=0.0010 cos_match=+0.0079 cos_rand=+0.00788 chance=0.00020\n", " published cos_after for image = 0.4107 (the honest shared-structure ceiling)\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1961\n", " measured A0 effective rank (text/expert): 20.5 / 20.4 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0156 0.0156 0.0234\n", " 128 1.0000 0.0078 0.0078 0.0117\n", " 256 1.0000 0.0039 0.0039 0.0000\n", " 512 1.0000 0.0020 0.0020 0.0010\n", " 1024 1.0000 0.0010 0.0010 0.0010\n", " 2048 1.0000 0.0005 0.0005 0.0007\n", " 4096 1.0000 0.0002 0.0002 0.0004\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 70.2s\n", "[done] wrote bertenstein_audit_results.json\n" ] } ], "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# BERTENSTEIN R@1 AUDIT — single Colab cell\n", "#\n", "# Question under test: is GEOLIP-Bertenstein's R@1 = 1.0000 a measurement of\n", "# cross-modal alignment, or a measurement of the evaluation protocol?\n", "#\n", "# The published eval (cell2_prototype_model_trainer_v1.py :748 eval_direct)\n", "# encodes text AND the expert modality in ONE joint forward pass through an\n", "# UNMASKED bidirectional fusion layer, reads both special tokens out\n", "# of that single sequence, and calls the cosine between them \"retrieval\".\n", "# Every diagonal entry sim[i,i] is therefore two readout heads of the same\n", "# fused encoding of item i. The off-diagonal sim[i,j] compares readouts from\n", "# two forward passes that never saw each other.\n", "#\n", "# This bed holds everything else constant and cuts exactly one wire at a time.\n", "#\n", "# ARMS (all on the shipped checkpoints/final weights, image<->text,\n", "# image_coco_test = the exact split that produced R@1 = 1.0000)\n", "# A0 PUBLISHED joint sequence, full bidirectional attention. Reproduce.\n", "# A1 BLOCKED identical sequence + positions; cross-modal attention MASKED.\n", "# The single-variable cut. Nothing else changes.\n", "# A2 SPLIT two fully independent forward passes. What \"retrieval\" means.\n", "# A3 UNTRAINED random init, PUBLISHED protocol. Does the metric need weights?\n", "# A3b UNTRAINED random init, SPLIT protocol. The floor.\n", "# A4 DERANGED joint pass, text[i] fused with image[perm[i]]. Ground truth\n", "# stays the FORWARD-PASS index. If R@1 holds, the diagonal is\n", "# pass-identity and carries zero semantic content.\n", "# A5 PROCRUSTES pooled frozen features through the shipped aligner, no fusion.\n", "# The honest ceiling (results.json reports cos_after 0.4107).\n", "# SWEEP gallery size N. A leak is N-invariant; retrieval decays ~log N.\n", "# FRAME FIT fp64 orthogonal Procrustes post-fit on the collapsed arms\n", "# before verdicting — the standing dist-campaign law, so we do\n", "# not publish a false floor the way affinity_kl nearly did.\n", "# CV NULL pentachoron CV of untrained normalized randn, printed beside\n", "# the model's CV, because the \"0.20 universal band\" claim needs\n", "# its null on the same line.\n", "#\n", "# Colab-cell-safe: no argparse, no __file__, paste-ahead-safe, globals() guarded.\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "import os, sys, json, math, time, subprocess\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Tuple\n", "\n", "# ── deps (idempotent; quiet) ─────────────────────────────────────────────────\n", "for _pkg, _imp in [(\"datasets\", \"datasets\"), (\"safetensors\", \"safetensors\"),\n", " (\"huggingface_hub\", \"huggingface_hub\")]:\n", " try:\n", " __import__(_imp)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _pkg], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from datasets import Dataset as HFDataset, concatenate_datasets\n", "from huggingface_hub import hf_hub_download\n", "from safetensors.torch import load_file as safetensors_load\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class AuditConfig:\n", " # sources\n", " model_repo: str = \"AbstractPhil/geolip-bertenstein\"\n", " cache_repo: str = \"AbstractPhil/bertenstein-v1\"\n", " ckpt_path: str = \"checkpoints/final/model.safetensors\"\n", " split_dir: str = \"image_coco_test\" # the split behind the R@1 1.0000 test row\n", " n_shards: int = 6 # ~860 rows/shard, ~497MB/shard -> ~5000 rows\n", " expert: str = \"image\"\n", " expert_col: str = \"image_hidden\"\n", " expert_mask_col: Optional[str] = None # image_coco_test ships no image mask\n", "\n", " # architecture (must reproduce checkpoint keys exactly)\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", " text_tokens: int = 32 # text expert: needs_pooling=False, n_pooled=32\n", " image_pooled: int = 16 # image expert: needs_pooling=True, n_pooled=16\n", " image_input_dim: int = 1024 # DINOv2-large, 1024->1024 direct (no projection)\n", "\n", " # protocol\n", " batch_size: int = 128\n", " eval_n: int = 5000 # published n for image_coco_test\n", " sweep_n: Tuple[int, ...] = (64, 128, 256, 512, 1024, 2048, 4096)\n", " frame_fit_pairs: int = 2500 # standing dist-campaign rider\n", " cv_samples: int = 200\n", " cv_null_dims: Tuple[int, ...] = (16, 64, 256, 1024)\n", " seed: int = 0\n", " device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", " # published reference values (bertenstein_results.json -> test.image_coco_test)\n", " ref_r1: float = 1.0\n", " ref_cos_match: float = 0.9735139608383179\n", " ref_cos_rand: float = 0.05161707841568314\n", " ref_cv_joint: float = 0.2007410876349909\n", " ref_procrustes_cos_after: float = 0.4106820225715637\n", " reproduce_tol: float = 0.02 # A0 gate; wider than fp noise, tight enough\n", "\n", "\n", "CFG = AuditConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# MODEL — key-compatible re-declaration of the shipped classes,\n", "# extended ONLY with an optional attn_mask pathway (arm A1).\n", "# State-dict keys are unchanged: layers.0.*, experts..*, pos, out_norm.*\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class FusionConfig:\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", "\n", "\n", "class ProcrustesAligner(nn.Module):\n", " \"\"\"Buffer-only module. Shapes taken from the checkpoint; values loaded from it.\"\"\"\n", "\n", " def __init__(self, d_expert: int, d_text: int, has_projection: bool,\n", " has_expert_whitener: bool, has_text_unwhitener: bool):\n", " super().__init__()\n", " self.register_buffer(\"expert_mean\", torch.zeros(d_expert))\n", " self.register_buffer(\"rotation\", torch.eye(d_text))\n", " if has_projection:\n", " self.register_buffer(\"projection\", torch.zeros(d_expert, d_text))\n", " else:\n", " self.projection = None\n", " if has_expert_whitener:\n", " self.register_buffer(\"expert_whitener\", torch.eye(d_text))\n", " else:\n", " self.expert_whitener = None\n", " if has_text_unwhitener:\n", " self.register_buffer(\"text_unwhitener\", torch.eye(d_text))\n", " else:\n", " self.text_unwhitener = None\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " x = x - self.expert_mean\n", " if self.projection is not None:\n", " x = x @ self.projection\n", " if self.expert_whitener is not None:\n", " x = x @ self.expert_whitener\n", " x = x @ self.rotation.T\n", " if self.text_unwhitener is not None:\n", " x = x @ self.text_unwhitener\n", " return x\n", "\n", "\n", "class FusionLayer(nn.Module):\n", " def __init__(self, c: FusionConfig):\n", " super().__init__()\n", " self.attn = nn.MultiheadAttention(c.d_model, c.n_heads, dropout=c.dropout,\n", " batch_first=True)\n", " self.ff = nn.Sequential(\n", " nn.Linear(c.d_model, c.d_ff),\n", " nn.GELU(),\n", " nn.Linear(c.d_ff, c.d_model),\n", " nn.Dropout(c.dropout),\n", " )\n", " self.n1 = nn.LayerNorm(c.d_model)\n", " self.n2 = nn.LayerNorm(c.d_model)\n", " self.drop = nn.Dropout(c.dropout)\n", "\n", " def forward(self, x, kpm=None, attn_mask=None):\n", " h = self.n1(x)\n", " a, _ = self.attn(h, h, h, key_padding_mask=kpm, attn_mask=attn_mask)\n", " x = x + self.drop(a)\n", " x = x + self.ff(self.n2(x))\n", " return x\n", "\n", "\n", "class ExpertModule(nn.Module):\n", " def __init__(self, name, input_dim, n_pooled, d_model=1024, n_heads=16,\n", " needs_pooling=True, aligner=None):\n", " super().__init__()\n", " self.name = name\n", " self.n_pooled = n_pooled\n", " self.needs_pooling = needs_pooling\n", " self.aligner = aligner\n", " proj_input_dim = d_model if aligner is not None else input_dim\n", " self.input_proj = nn.Linear(proj_input_dim, d_model)\n", " if needs_pooling:\n", " self.pool_queries = nn.Parameter(torch.randn(1, n_pooled, d_model) * 0.02)\n", " self.pool_attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True)\n", " self.pool_norm = nn.LayerNorm(d_model)\n", " self.special_token = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.modality_emb = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.output_head = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.Linear(d_model, d_model),\n", " )\n", "\n", " def prepare_tokens(self, hidden, mask=None):\n", " B = hidden.shape[0]\n", " if self.aligner is not None:\n", " hidden = self.aligner(hidden)\n", " proj = self.input_proj(hidden)\n", " if self.needs_pooling:\n", " q = self.pool_queries.expand(B, -1, -1)\n", " kpm = (~mask.bool()) if mask is not None else None\n", " p, _ = self.pool_attn(q, proj, proj, key_padding_mask=kpm)\n", " tokens = self.pool_norm(p + q)\n", " else:\n", " tokens = proj[:, : self.n_pooled]\n", " return tokens + self.modality_emb\n", "\n", " def get_special(self, B):\n", " return self.special_token.expand(B, -1, -1)\n", "\n", " def extract(self, h):\n", " return F.normalize(self.output_head(h), dim=-1)\n", "\n", "\n", "class BertensteinFusion(nn.Module):\n", " \"\"\"\n", " Verbatim-compatible with the shipped model, plus `block_cross`:\n", " block_cross=False -> published behaviour (unmasked bidirectional attention)\n", " block_cross=True -> identical sequence, identical positions, cross-modal\n", " attention forbidden. Arm A1.\n", " `layout_positions` reproduces the JOINT absolute positions even when only one\n", " modality is present, so arm A2 is not confounded by positional-encoding shift.\n", " \"\"\"\n", "\n", " def __init__(self, fusion_cfg: FusionConfig, experts_dict: Dict[str, ExpertModule]):\n", " super().__init__()\n", " self.cfg = fusion_cfg\n", " self.expert_order = list(experts_dict.keys())\n", " self.experts = nn.ModuleDict(experts_dict)\n", " self.pos = nn.Parameter(torch.randn(1, fusion_cfg.max_seq_len, fusion_cfg.d_model) * 0.02)\n", " self.layers = nn.ModuleList([FusionLayer(fusion_cfg) for _ in range(fusion_cfg.n_layers)])\n", " self.out_norm = nn.LayerNorm(fusion_cfg.d_model)\n", "\n", " def forward(self, expert_hidden, expert_masks=None, block_cross=False,\n", " layout_positions: Optional[Dict[str, int]] = None):\n", " active = [n for n in self.expert_order if n in expert_hidden]\n", " B = next(iter(expert_hidden.values())).shape[0]\n", " expert_masks = expert_masks or {}\n", "\n", " seq_parts, mask_parts, special_pos, spans = [], [], {}, {}\n", " pos = 0\n", " for name in active:\n", " exp = self.experts[name]\n", " h = expert_hidden[name]\n", " m = expert_masks.get(name, None)\n", " start = pos\n", " seq_parts.append(exp.get_special(B))\n", " mask_parts.append(torch.zeros(B, 1, device=h.device, dtype=torch.bool))\n", " special_pos[name] = pos\n", " pos += 1\n", " tokens = exp.prepare_tokens(h, mask=m)\n", " nt = tokens.shape[1]\n", " seq_parts.append(tokens)\n", " mask_parts.append(torch.zeros(B, nt, device=h.device, dtype=torch.bool))\n", " pos += nt\n", " spans[name] = (start, pos)\n", "\n", " seq = torch.cat(seq_parts, 1)\n", " kpm = torch.cat(mask_parts, 1)\n", " L = seq.shape[1]\n", "\n", " # positional encoding: joint-layout absolute positions when requested\n", " if layout_positions is not None:\n", " idx = torch.empty(L, dtype=torch.long, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " base = layout_positions[name]\n", " idx[s:e] = torch.arange(base, base + (e - s), device=seq.device)\n", " seq = seq + self.pos[0, idx].unsqueeze(0)\n", " else:\n", " seq = seq + self.pos[:, :L]\n", "\n", " attn_mask = None\n", " if block_cross and len(active) > 1:\n", " attn_mask = torch.ones(L, L, dtype=torch.bool, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " attn_mask[s:e, s:e] = False # within-modality allowed, cross forbidden\n", "\n", " for layer in self.layers:\n", " seq = layer(seq, kpm=kpm, attn_mask=attn_mask)\n", "\n", " seq = self.out_norm(seq)\n", " return {n: self.experts[n].extract(seq[:, special_pos[n]]) for n in active}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# METRICS\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float64)\n", " cm[:, 0, 1:] = 1\n", " cm[:, 1:, 0] = 1\n", " cm[:, 1:, 1:] = d2.double()\n", " s = (-1.0) ** V\n", " f = math.factorial(V - 1)\n", " return s / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "def pentachoron_cv(emb, n=200, seed=0):\n", " N = emb.shape[0]\n", " if N < 5:\n", " return 0.0\n", " g = torch.Generator().manual_seed(seed)\n", " vs = []\n", " for _ in range(n):\n", " idx = torch.randperm(N, generator=g)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = float(torch.sqrt(F.relu(v2[0])).item())\n", " if v > 0:\n", " vs.append(v)\n", " if len(vs) < 10:\n", " return 0.0\n", " a = np.array(vs, dtype=np.float64)\n", " return float(a.std() / max(a.mean(), 1e-12))\n", "\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " \"\"\"\n", " Participation ratio of the singular-value spectrum: (sum s^2)^2 / sum s^4.\n", " CV is dimension-dependent and therefore a bad standalone gauge (see CV NULL);\n", " erank says directly how many directions the embedding actually uses.\n", " \"\"\"\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s = torch.linalg.svdvals(xc)\n", " s2 = s ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "def retrieval_metrics(ea: torch.Tensor, eb: torch.Tensor, gt: Optional[torch.Tensor] = None,\n", " cv: bool = True, cv_seed: int = 0) -> Dict[str, float]:\n", " \"\"\"Published _metrics, plus explicit ground-truth override for the deranged arm.\"\"\"\n", " N = ea.shape[0]\n", " sim = ea @ eb.T\n", " gt = torch.arange(N) if gt is None else gt\n", "\n", " def r_at_k(k, dim):\n", " topk = sim.topk(min(k, N), dim=dim).indices\n", " if dim == 1:\n", " return (topk == gt.unsqueeze(1)).any(1).float().mean().item()\n", " return (topk == gt.unsqueeze(0)).any(0).float().mean().item()\n", "\n", " diag = sim[torch.arange(N), gt]\n", " out = {\n", " \"r1\": (r_at_k(1, 1) + r_at_k(1, 0)) / 2,\n", " \"r5\": (r_at_k(5, 1) + r_at_k(5, 0)) / 2,\n", " \"cos_match\": diag.mean().item(),\n", " \"cos_rand\": (sim.sum() - diag.sum()).item() / max(N * N - N, 1),\n", " \"n\": N,\n", " \"chance_r1\": 1.0 / N,\n", " }\n", " if cv:\n", " out[\"cv_joint\"] = pentachoron_cv(torch.cat([ea, eb]), n=CFG.cv_samples, seed=cv_seed)\n", " out[\"erank_a\"] = effective_rank(ea)\n", " out[\"erank_b\"] = effective_rank(eb)\n", " return out\n", "\n", "\n", "def procrustes_frame_fit(ea: torch.Tensor, eb: torch.Tensor, n_pairs: int,\n", " seed: int = 0) -> Dict[str, float]:\n", " \"\"\"\n", " fp64 orthogonal Procrustes post-fit on a disjoint pair subset, applied to the\n", " query side, then re-scored on the REMAINDER. Standing dist-campaign rider:\n", " relational/unanchored objectives read as false floors under absolute gauges.\n", " \"\"\"\n", " N = ea.shape[0]\n", " k = min(n_pairs, N // 2)\n", " if k < 32:\n", " return {\"skipped\": True}\n", " g = torch.Generator().manual_seed(seed)\n", " perm = torch.randperm(N, generator=g)\n", " fit_idx, hold_idx = perm[:k], perm[k:]\n", " A = ea[fit_idx].double()\n", " B = eb[fit_idx].double()\n", " U, _, Vt = torch.linalg.svd(A.T @ B, full_matrices=False)\n", " R = (U @ Vt)\n", " ea_r = F.normalize((ea.double() @ R).float(), dim=-1)\n", " eb_n = F.normalize(eb, dim=-1)\n", " m_all = retrieval_metrics(ea_r, eb_n, cv=False)\n", " m_hold = retrieval_metrics(ea_r[hold_idx], eb_n[hold_idx], cv=False)\n", " return {\n", " \"fit_pairs\": k,\n", " \"r1_all_rotated\": m_all[\"r1\"],\n", " \"cos_match_all_rotated\": m_all[\"cos_match\"],\n", " \"r1_heldout_rotated\": m_hold[\"r1\"],\n", " \"cos_match_heldout_rotated\": m_hold[\"cos_match\"],\n", " \"n_heldout\": m_hold[\"n\"],\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# DATA + BUILD\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def fetch_split(cfg: AuditConfig):\n", " print(f\"[data] pulling {cfg.n_shards} shard(s) of {cfg.cache_repo}/{cfg.split_dir} \"\n", " f\"(~497MB each) ...\")\n", " parts = []\n", " for i in range(cfg.n_shards):\n", " fn = f\"{cfg.split_dir}/data-{i:05d}-of-00049.arrow\"\n", " p = hf_hub_download(cfg.cache_repo, fn, repo_type=\"dataset\")\n", " parts.append(HFDataset.from_file(p))\n", " print(f\" shard {i}: {len(parts[-1])} rows\")\n", " ds = concatenate_datasets(parts).with_format(\"torch\")\n", " print(f\"[data] {len(ds)} rows | columns {ds.column_names}\")\n", " return ds\n", "\n", "\n", "def build_model(cfg: AuditConfig, random_init: bool = False):\n", " torch.manual_seed(cfg.seed)\n", " fcfg = FusionConfig(d_model=cfg.d_model, n_heads=cfg.n_heads, n_layers=cfg.n_layers,\n", " d_ff=cfg.d_ff, dropout=cfg.dropout, max_seq_len=cfg.max_seq_len)\n", " experts = {\n", " \"text\": ExpertModule(\"text\", cfg.d_model, cfg.text_tokens, cfg.d_model,\n", " cfg.n_heads, needs_pooling=False),\n", " cfg.expert: ExpertModule(cfg.expert, cfg.image_input_dim, cfg.image_pooled,\n", " cfg.d_model, cfg.n_heads, needs_pooling=True,\n", " aligner=ProcrustesAligner(cfg.image_input_dim, cfg.d_model,\n", " has_projection=False,\n", " has_expert_whitener=True,\n", " has_text_unwhitener=True)),\n", " }\n", " model = BertensteinFusion(fcfg, experts)\n", "\n", " if not random_init:\n", " p = hf_hub_download(cfg.model_repo, cfg.ckpt_path)\n", " state = safetensors_load(p)\n", " keep = {k: v for k, v in state.items()\n", " if k.startswith(\"experts.text.\") or k.startswith(f\"experts.{cfg.expert}.\")\n", " or k.startswith(\"layers.\") or k in (\"pos\", \"out_norm.weight\", \"out_norm.bias\")}\n", " missing, unexpected = model.load_state_dict(keep, strict=False)\n", " assert not missing, f\"MISSING checkpoint keys (model would be partly random): {missing}\"\n", " print(f\"[model] loaded {len(keep)} tensors | unexpected={list(unexpected)}\")\n", " else:\n", " print(\"[model] RANDOM INIT (control arm)\")\n", "\n", " return model.to(cfg.device).eval()\n", "\n", "\n", "# ── layouts: joint absolute positions, so the split arm is position-matched ──\n", "def joint_layout(cfg: AuditConfig) -> Dict[str, int]:\n", " return {\"text\": 0, cfg.expert: 1 + cfg.text_tokens}\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, ds, cfg: AuditConfig, N: int, mode: str,\n", " perm: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " mode:\n", " 'joint' -> published: one pass, unmasked bidirectional attention (A0)\n", " 'blocked' -> one pass, same positions, cross-modal attention masked (A1)\n", " 'split' -> two independent passes, joint-layout positions (A2)\n", " perm: index permutation applied to the EXPERT side inside the joint pass (A4)\n", " \"\"\"\n", " lay = joint_layout(cfg)\n", " te_all, ee_all = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device)\n", " if perm is None:\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " else:\n", " pi = perm[i:j]\n", " eh = ds[pi.tolist()][cfg.expert_col].to(cfg.device).float()\n", "\n", " if mode in (\"joint\", \"blocked\"):\n", " out = model({\"text\": th, cfg.expert: eh}, {\"text\": tm},\n", " block_cross=(mode == \"blocked\"))\n", " te_all.append(out[\"text\"].float().cpu())\n", " ee_all.append(out[cfg.expert].float().cpu())\n", " elif mode == \"split\":\n", " o_t = model({\"text\": th}, {\"text\": tm}, layout_positions=lay)\n", " o_e = model({cfg.expert: eh}, {}, layout_positions=lay)\n", " te_all.append(o_t[\"text\"].float().cpu())\n", " ee_all.append(o_e[cfg.expert].float().cpu())\n", " else:\n", " raise ValueError(mode)\n", " return torch.cat(te_all)[:N], torch.cat(ee_all)[:N]\n", "\n", "\n", "@torch.no_grad()\n", "def procrustes_reference(model, ds, cfg: AuditConfig, N: int) -> Dict[str, float]:\n", " \"\"\"A5: pooled frozen features through the shipped aligner. No fusion layer.\"\"\"\n", " aligner = model.experts[cfg.expert].aligner\n", " tv, ev = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device).float()\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " denom = tm.sum(1, keepdim=True).clamp(min=1.0)\n", " tv.append(((th * tm.unsqueeze(-1)).sum(1) / denom).cpu())\n", " ev.append(aligner(eh).mean(1).cpu())\n", " tv = F.normalize(torch.cat(tv)[:N], dim=-1)\n", " ev = F.normalize(torch.cat(ev)[:N], dim=-1)\n", " m = retrieval_metrics(tv, ev, cv=False)\n", " m[\"note\"] = \"frozen features + shipped aligner, no fusion layer\"\n", " return m\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def line(t=\"\"):\n", " print(\"─\" * 78 if not t else f\"── {t} \" + \"─\" * max(0, 74 - len(t)))\n", "\n", "\n", "def fmt(m: Dict[str, Any]) -> str:\n", " s = (f\"R@1={m['r1']:.4f} R@5={m['r5']:.4f} cos_match={m['cos_match']:+.4f} \"\n", " f\"cos_rand={m['cos_rand']:+.5f} chance={m['chance_r1']:.5f}\")\n", " if \"cv_joint\" in m:\n", " s += f\" CV={m['cv_joint']:.4f} erank={m['erank_a']:.1f}/{m['erank_b']:.1f}\"\n", " return s\n", "\n", "\n", "def run(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " t0 = time.time()\n", " print(\"=\" * 78)\n", " print(\"BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\")\n", " print(\"=\" * 78)\n", " print(f\"device={cfg.device} split={cfg.split_dir} expert={cfg.expert}\")\n", "\n", " ds = fetch_split(cfg)\n", " N = min(cfg.eval_n, len(ds))\n", " if N < cfg.eval_n:\n", " print(f\"[warn] only {N} rows available; raise cfg.n_shards for the full {cfg.eval_n}\")\n", "\n", " model = build_model(cfg, random_init=False)\n", " rnd = build_model(cfg, random_init=True)\n", " R: Dict[str, Any] = {\"n\": N, \"config\": {\"n_shards\": cfg.n_shards, \"seed\": cfg.seed}}\n", "\n", " # ── A0 PUBLISHED ─────────────────────────────────────────────────────────\n", " line(\"A0 PUBLISHED — joint pass, unmasked bidirectional attention\")\n", " te, ee = encode(model, ds, cfg, N, \"joint\")\n", " R[\"A0_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A0_published\"]))\n", " d_r1 = abs(R[\"A0_published\"][\"r1\"] - cfg.ref_r1)\n", " d_cm = abs(R[\"A0_published\"][\"cos_match\"] - cfg.ref_cos_match)\n", " ok = (d_r1 <= cfg.reproduce_tol) and (d_cm <= cfg.reproduce_tol)\n", " R[\"A0_reproduction_gate\"] = {\"pass\": bool(ok), \"d_r1\": d_r1, \"d_cos_match\": d_cm,\n", " \"ref_r1\": cfg.ref_r1, \"ref_cos_match\": cfg.ref_cos_match}\n", " print(f\" GATE reproduce-published: {'PASS' if ok else 'FAIL'} \"\n", " f\"(Δr1={d_r1:.4f}, Δcos={d_cm:.4f})\")\n", " if not ok:\n", " print(\" !! A0 did not reproduce. Downstream arms are uninterpretable until it does.\")\n", "\n", " # ── A1 BLOCKED ───────────────────────────────────────────────────────────\n", " line(\"A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED\")\n", " te, ee = encode(model, ds, cfg, N, \"blocked\")\n", " R[\"A1_blocked\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A1_blocked\"]))\n", " R[\"A1_blocked_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A1_blocked_framefit\"]))\n", "\n", " # ── A2 SPLIT ─────────────────────────────────────────────────────────────\n", " line(\"A2 SPLIT — two independent forward passes (what retrieval means)\")\n", " te2, ee2 = encode(model, ds, cfg, N, \"split\")\n", " R[\"A2_split\"] = retrieval_metrics(te2, ee2, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A2_split\"]))\n", "\n", " # correctness invariant found at smoke: with n_layers=1, block-diagonal masking\n", " # and position-matched independent encoding are the SAME computation. If these\n", " # diverge, the mask or the layout offset is wrong and A1/A2 are uninterpretable.\n", " d_inv = max((te - te2).abs().max().item(), (ee - ee2).abs().max().item())\n", " R[\"A1_A2_identity_gate\"] = {\"pass\": bool(d_inv < 1e-4), \"max_abs_diff\": d_inv}\n", " print(f\" GATE A1==A2 (must hold at n_layers=1): \"\n", " f\"{'PASS' if d_inv < 1e-4 else 'FAIL'} max|Δ|={d_inv:.2e}\")\n", " te, ee = te2, ee2\n", " R[\"A2_split_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A2_split_framefit\"]))\n", "\n", " # ── A3 UNTRAINED ─────────────────────────────────────────────────────────\n", " line(\"A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"joint\")\n", " R[\"A3_untrained_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3_untrained_published\"]))\n", "\n", " line(\"A3b UNTRAINED — random init, SPLIT protocol (the floor)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"split\")\n", " R[\"A3b_untrained_split\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3b_untrained_split\"]))\n", "\n", " # ── A4 DERANGED ──────────────────────────────────────────────────────────\n", " line(\"A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index\")\n", " g = torch.Generator().manual_seed(cfg.seed + 991)\n", " perm = torch.randperm(N, generator=g)\n", " fixed = int((perm == torch.arange(N)).sum())\n", " te, ee = encode(model, ds, cfg, N, \"joint\", perm=perm)\n", " R[\"A4_deranged\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " R[\"A4_deranged\"][\"accidental_fixed_points\"] = fixed\n", " print(\" \", fmt(R[\"A4_deranged\"]), f\" (accidental fixed points: {fixed})\")\n", " print(\" If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\")\n", "\n", " # ── A5 PROCRUSTES REFERENCE ──────────────────────────────────────────────\n", " line(\"A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion\")\n", " R[\"A5_procrustes_reference\"] = procrustes_reference(model, ds, cfg, N)\n", " print(\" \", fmt(R[\"A5_procrustes_reference\"]))\n", " print(f\" published cos_after for image = {cfg.ref_procrustes_cos_after:.4f} \"\n", " f\"(the honest shared-structure ceiling)\")\n", "\n", " # ── CV NULL ──────────────────────────────────────────────────────────────\n", " line(\"CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null\")\n", " g2 = torch.Generator().manual_seed(cfg.seed + 7)\n", " nulls = {}\n", " for d in cfg.cv_null_dims:\n", " z = F.normalize(torch.randn(2 * min(N, 2048), d, generator=g2), dim=-1)\n", " nulls[d] = pentachoron_cv(z, n=cfg.cv_samples, seed=cfg.seed)\n", " print(f\" normalized randn, ZERO training, d={d:<5d}: CV = {nulls[d]:.4f}\")\n", " R[\"cv_null_by_dim\"] = nulls\n", " print(f\" published model CV (image_coco_test) : {cfg.ref_cv_joint:.4f}\")\n", " print(f\" measured A0 CV : \"\n", " f\"{R['A0_published'].get('cv_joint', float('nan')):.4f}\")\n", " print(f\" measured A0 effective rank (text/expert): \"\n", " f\"{R['A0_published'].get('erank_a', float('nan')):.1f} / \"\n", " f\"{R['A0_published'].get('erank_b', float('nan')):.1f} of nominal {cfg.d_model}\")\n", " print(\" READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\")\n", " print(\" claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\")\n", " print(\" if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\")\n", " print(\" clothes, not convergence to a universal constant.\")\n", "\n", " # ── SWEEP ────────────────────────────────────────────────────────────────\n", " line(\"SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N\")\n", " sweep = {}\n", " for mode, tag, mdl in ((\"joint\", \"A0_published\", model),\n", " (\"blocked\", \"A1_blocked\", model),\n", " (\"split\", \"A2_split\", model),\n", " (\"joint\", \"A3_untrained\", rnd)):\n", " rows = []\n", " for n in cfg.sweep_n:\n", " if n > N:\n", " continue\n", " a, b = encode(mdl, ds, cfg, n, mode)\n", " m = retrieval_metrics(a, b, cv=False)\n", " rows.append({\"N\": n, \"r1\": m[\"r1\"], \"cos_match\": m[\"cos_match\"],\n", " \"chance\": m[\"chance_r1\"]})\n", " sweep[tag] = rows\n", " R[\"sweep\"] = sweep\n", " hdr = \" N \" + \"\".join(f\"{t:>22s}\" for t in sweep.keys())\n", " print(hdr)\n", " for k, n in enumerate(cfg.sweep_n):\n", " if n > N:\n", " continue\n", " cells = []\n", " for tag in sweep:\n", " row = next((r for r in sweep[tag] if r[\"N\"] == n), None)\n", " cells.append(f\"{row['r1']:>22.4f}\" if row else f\"{'-':>22s}\")\n", " print(f\" {n:<7d}\" + \"\".join(cells))\n", "\n", " # ── VERDICT ──────────────────────────────────────────────────────────────\n", " line(\"VERDICT\")\n", " a0, a1, a2, a3, a4 = (R[\"A0_published\"][\"r1\"], R[\"A1_blocked\"][\"r1\"],\n", " R[\"A2_split\"][\"r1\"], R[\"A3_untrained_published\"][\"r1\"],\n", " R[\"A4_deranged\"][\"r1\"])\n", " leak_signals = {\n", " \"untrained_reproduces_headline (A3 >= 0.9*A0)\": bool(a3 >= 0.9 * a0),\n", " \"deranged_pairs_still_retrieve (A4 >= 0.9*A0)\": bool(a4 >= 0.9 * a0),\n", " \"blocking_cross_attention_collapses (A1 < 0.5*A0)\": bool(a1 < 0.5 * a0),\n", " \"independent_encoding_collapses (A2 < 0.5*A0)\": bool(a2 < 0.5 * a0),\n", " }\n", " R[\"leak_signals\"] = leak_signals\n", " for k, v in leak_signals.items():\n", " print(f\" [{'X' if v else ' '}] {k}\")\n", " n_leak = sum(leak_signals.values())\n", " R[\"n_leak_signals\"] = n_leak\n", " if n_leak >= 3:\n", " R[\"verdict\"] = \"PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\"\n", " elif n_leak == 0:\n", " R[\"verdict\"] = \"NO LEAK DETECTED — the number survives every cut; escalate to seeds + a second split\"\n", " else:\n", " R[\"verdict\"] = f\"MIXED ({n_leak}/4 leak signals) — read the arms individually, do not summarize\"\n", " print(f\"\\n => {R['verdict']}\")\n", " print(f\"\\n[done] {time.time() - t0:.1f}s\")\n", "\n", " with open(\"bertenstein_audit_results.json\", \"w\") as f:\n", " json.dump(R, f, indent=2, default=float)\n", " print(\"[done] wrote bertenstein_audit_results.json\")\n", " return R\n", "\n", "\n", "# ── activation ───────────────────────────────────────────────────────────────\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run(CFG)" ] }, { "cell_type": "code", "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# BERTENSTEIN R@1 AUDIT — single Colab cell\n", "#\n", "# Question under test: is GEOLIP-Bertenstein's R@1 = 1.0000 a measurement of\n", "# cross-modal alignment, or a measurement of the evaluation protocol?\n", "#\n", "# The published eval (cell2_prototype_model_trainer_v1.py :748 eval_direct)\n", "# encodes text AND the expert modality in ONE joint forward pass through an\n", "# UNMASKED bidirectional fusion layer, reads both special tokens out\n", "# of that single sequence, and calls the cosine between them \"retrieval\".\n", "# Every diagonal entry sim[i,i] is therefore two readout heads of the same\n", "# fused encoding of item i. The off-diagonal sim[i,j] compares readouts from\n", "# two forward passes that never saw each other.\n", "#\n", "# This bed holds everything else constant and cuts exactly one wire at a time.\n", "#\n", "# ARMS (all on the shipped checkpoints/final weights, image<->text,\n", "# image_coco_test = the exact split that produced R@1 = 1.0000)\n", "# A0 PUBLISHED joint sequence, full bidirectional attention. Reproduce.\n", "# A1 BLOCKED identical sequence + positions; cross-modal attention MASKED.\n", "# The single-variable cut. Nothing else changes.\n", "# A2 SPLIT two fully independent forward passes. What \"retrieval\" means.\n", "# A3 UNTRAINED random init, PUBLISHED protocol. Does the metric need weights?\n", "# A3b UNTRAINED random init, SPLIT protocol. The floor.\n", "# A4 DERANGED joint pass, text[i] fused with image[perm[i]]. Ground truth\n", "# stays the FORWARD-PASS index. If R@1 holds, the diagonal is\n", "# pass-identity and carries zero semantic content.\n", "# A5 PROCRUSTES pooled frozen features through the shipped aligner, no fusion.\n", "# The honest ceiling (results.json reports cos_after 0.4107).\n", "# SWEEP gallery size N. A leak is N-invariant; retrieval decays ~log N.\n", "# FRAME FIT fp64 orthogonal Procrustes post-fit on the collapsed arms\n", "# before verdicting — the standing dist-campaign law, so we do\n", "# not publish a false floor the way affinity_kl nearly did.\n", "# CV NULL pentachoron CV of untrained normalized randn, printed beside\n", "# the model's CV, because the \"0.20 universal band\" claim needs\n", "# its null on the same line.\n", "#\n", "# Colab-cell-safe: no argparse, no __file__, paste-ahead-safe, globals() guarded.\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "import os, sys, json, math, time, subprocess\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Tuple\n", "\n", "# ── deps (idempotent; quiet) ─────────────────────────────────────────────────\n", "for _pkg, _imp in [(\"datasets\", \"datasets\"), (\"safetensors\", \"safetensors\"),\n", " (\"huggingface_hub\", \"huggingface_hub\")]:\n", " try:\n", " __import__(_imp)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _pkg], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from datasets import Dataset as HFDataset, concatenate_datasets\n", "from huggingface_hub import hf_hub_download\n", "from safetensors.torch import load_file as safetensors_load\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class AuditConfig:\n", " # sources\n", " model_repo: str = \"AbstractPhil/geolip-bertenstein\"\n", " cache_repo: str = \"AbstractPhil/bertenstein-v1\"\n", " ckpt_path: str = \"checkpoints/final/model.safetensors\"\n", " # image_coco = COCO-Caption \"val\" -> REAL captions (10-28 tokens, erank 37)\n", " # NOTE: the model TRAINED on 85% of this (train_test_split seed 42),\n", " # so numbers here are a contaminated UPPER BOUND. Say so in any writeup.\n", " # image_coco_test = COCO-Caption \"test\" -> COCO 2014 test annotations are WITHHELD.\n", " # extract_first_text() fell through to a constant: every row is the\n", " # SAME 3-token sequence, pooled-text erank 1.00, pairwise cos 1.0000.\n", " # The flagship \"40K test / R@1 1.0000\" row was scored on a split with\n", " # NO TEXT MODALITY. Kept as the default because that is what the card\n", " # claims, and the degeneracy gate below now says so out loud.\n", " split_dir: str = \"image_coco_test\"\n", " n_shards: int = 6 # ~830 rows/shard, ~494MB/shard\n", " shard_total: int = 49 # both splits are data-NNNNN-of-00049.arrow\n", " expert: str = \"image\"\n", " expert_col: str = \"image_hidden\"\n", " expert_mask_col: Optional[str] = None # image_coco_test ships no image mask\n", "\n", " # architecture (must reproduce checkpoint keys exactly)\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", " text_tokens: int = 32 # text expert: needs_pooling=False, n_pooled=32\n", " image_pooled: int = 16 # image expert: needs_pooling=True, n_pooled=16\n", " image_input_dim: int = 1024 # DINOv2-large, 1024->1024 direct (no projection)\n", "\n", " # protocol\n", " batch_size: int = 128\n", " eval_n: int = 5000 # published n for image_coco_test\n", " sweep_n: Tuple[int, ...] = (64, 128, 256, 512, 1024, 2048, 4096)\n", " frame_fit_pairs: int = 2500 # standing dist-campaign rider\n", " cv_samples: int = 200\n", " cv_null_dims: Tuple[int, ...] = (16, 64, 256, 1024)\n", " seed: int = 0\n", " device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", " # published reference values (bertenstein_results.json -> test.image_coco_test)\n", " ref_r1: float = 1.0\n", " ref_cos_match: float = 0.9735139608383179\n", " ref_cos_rand: float = 0.05161707841568314\n", " ref_cv_joint: float = 0.2007410876349909\n", " ref_procrustes_cos_after: float = 0.4106820225715637\n", " reproduce_tol: float = 0.02 # A0 gate; wider than fp noise, tight enough\n", "\n", "\n", "CFG = AuditConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# MODEL — key-compatible re-declaration of the shipped classes,\n", "# extended ONLY with an optional attn_mask pathway (arm A1).\n", "# State-dict keys are unchanged: layers.0.*, experts..*, pos, out_norm.*\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class FusionConfig:\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", "\n", "\n", "class ProcrustesAligner(nn.Module):\n", " \"\"\"Buffer-only module. Shapes taken from the checkpoint; values loaded from it.\"\"\"\n", "\n", " def __init__(self, d_expert: int, d_text: int, has_projection: bool,\n", " has_expert_whitener: bool, has_text_unwhitener: bool):\n", " super().__init__()\n", " self.register_buffer(\"expert_mean\", torch.zeros(d_expert))\n", " self.register_buffer(\"rotation\", torch.eye(d_text))\n", " if has_projection:\n", " self.register_buffer(\"projection\", torch.zeros(d_expert, d_text))\n", " else:\n", " self.projection = None\n", " if has_expert_whitener:\n", " self.register_buffer(\"expert_whitener\", torch.eye(d_text))\n", " else:\n", " self.expert_whitener = None\n", " if has_text_unwhitener:\n", " self.register_buffer(\"text_unwhitener\", torch.eye(d_text))\n", " else:\n", " self.text_unwhitener = None\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " x = x - self.expert_mean\n", " if self.projection is not None:\n", " x = x @ self.projection\n", " if self.expert_whitener is not None:\n", " x = x @ self.expert_whitener\n", " x = x @ self.rotation.T\n", " if self.text_unwhitener is not None:\n", " x = x @ self.text_unwhitener\n", " return x\n", "\n", "\n", "class FusionLayer(nn.Module):\n", " def __init__(self, c: FusionConfig):\n", " super().__init__()\n", " self.attn = nn.MultiheadAttention(c.d_model, c.n_heads, dropout=c.dropout,\n", " batch_first=True)\n", " self.ff = nn.Sequential(\n", " nn.Linear(c.d_model, c.d_ff),\n", " nn.GELU(),\n", " nn.Linear(c.d_ff, c.d_model),\n", " nn.Dropout(c.dropout),\n", " )\n", " self.n1 = nn.LayerNorm(c.d_model)\n", " self.n2 = nn.LayerNorm(c.d_model)\n", " self.drop = nn.Dropout(c.dropout)\n", "\n", " def forward(self, x, kpm=None, attn_mask=None):\n", " h = self.n1(x)\n", " a, _ = self.attn(h, h, h, key_padding_mask=kpm, attn_mask=attn_mask)\n", " x = x + self.drop(a)\n", " x = x + self.ff(self.n2(x))\n", " return x\n", "\n", "\n", "class ExpertModule(nn.Module):\n", " def __init__(self, name, input_dim, n_pooled, d_model=1024, n_heads=16,\n", " needs_pooling=True, aligner=None):\n", " super().__init__()\n", " self.name = name\n", " self.n_pooled = n_pooled\n", " self.needs_pooling = needs_pooling\n", " self.aligner = aligner\n", " proj_input_dim = d_model if aligner is not None else input_dim\n", " self.input_proj = nn.Linear(proj_input_dim, d_model)\n", " if needs_pooling:\n", " self.pool_queries = nn.Parameter(torch.randn(1, n_pooled, d_model) * 0.02)\n", " self.pool_attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True)\n", " self.pool_norm = nn.LayerNorm(d_model)\n", " self.special_token = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.modality_emb = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.output_head = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.Linear(d_model, d_model),\n", " )\n", "\n", " def prepare_tokens(self, hidden, mask=None):\n", " B = hidden.shape[0]\n", " if self.aligner is not None:\n", " hidden = self.aligner(hidden)\n", " proj = self.input_proj(hidden)\n", " if self.needs_pooling:\n", " q = self.pool_queries.expand(B, -1, -1)\n", " kpm = (~mask.bool()) if mask is not None else None\n", " p, _ = self.pool_attn(q, proj, proj, key_padding_mask=kpm)\n", " tokens = self.pool_norm(p + q)\n", " else:\n", " tokens = proj[:, : self.n_pooled]\n", " return tokens + self.modality_emb\n", "\n", " def get_special(self, B):\n", " return self.special_token.expand(B, -1, -1)\n", "\n", " def extract(self, h):\n", " return F.normalize(self.output_head(h), dim=-1)\n", "\n", "\n", "class BertensteinFusion(nn.Module):\n", " \"\"\"\n", " Verbatim-compatible with the shipped model, plus `block_cross`:\n", " block_cross=False -> published behaviour (unmasked bidirectional attention)\n", " block_cross=True -> identical sequence, identical positions, cross-modal\n", " attention forbidden. Arm A1.\n", " `layout_positions` reproduces the JOINT absolute positions even when only one\n", " modality is present, so arm A2 is not confounded by positional-encoding shift.\n", " \"\"\"\n", "\n", " def __init__(self, fusion_cfg: FusionConfig, experts_dict: Dict[str, ExpertModule]):\n", " super().__init__()\n", " self.cfg = fusion_cfg\n", " self.expert_order = list(experts_dict.keys())\n", " self.experts = nn.ModuleDict(experts_dict)\n", " self.pos = nn.Parameter(torch.randn(1, fusion_cfg.max_seq_len, fusion_cfg.d_model) * 0.02)\n", " self.layers = nn.ModuleList([FusionLayer(fusion_cfg) for _ in range(fusion_cfg.n_layers)])\n", " self.out_norm = nn.LayerNorm(fusion_cfg.d_model)\n", "\n", " def forward(self, expert_hidden, expert_masks=None, block_cross=False,\n", " layout_positions: Optional[Dict[str, int]] = None):\n", " active = [n for n in self.expert_order if n in expert_hidden]\n", " B = next(iter(expert_hidden.values())).shape[0]\n", " expert_masks = expert_masks or {}\n", "\n", " seq_parts, mask_parts, special_pos, spans = [], [], {}, {}\n", " pos = 0\n", " for name in active:\n", " exp = self.experts[name]\n", " h = expert_hidden[name]\n", " m = expert_masks.get(name, None)\n", " start = pos\n", " seq_parts.append(exp.get_special(B))\n", " mask_parts.append(torch.zeros(B, 1, device=h.device, dtype=torch.bool))\n", " special_pos[name] = pos\n", " pos += 1\n", " tokens = exp.prepare_tokens(h, mask=m)\n", " nt = tokens.shape[1]\n", " seq_parts.append(tokens)\n", " mask_parts.append(torch.zeros(B, nt, device=h.device, dtype=torch.bool))\n", " pos += nt\n", " spans[name] = (start, pos)\n", "\n", " seq = torch.cat(seq_parts, 1)\n", " kpm = torch.cat(mask_parts, 1)\n", " L = seq.shape[1]\n", "\n", " # positional encoding: joint-layout absolute positions when requested\n", " if layout_positions is not None:\n", " idx = torch.empty(L, dtype=torch.long, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " base = layout_positions[name]\n", " idx[s:e] = torch.arange(base, base + (e - s), device=seq.device)\n", " seq = seq + self.pos[0, idx].unsqueeze(0)\n", " else:\n", " seq = seq + self.pos[:, :L]\n", "\n", " attn_mask = None\n", " if block_cross and len(active) > 1:\n", " attn_mask = torch.ones(L, L, dtype=torch.bool, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " attn_mask[s:e, s:e] = False # within-modality allowed, cross forbidden\n", "\n", " for layer in self.layers:\n", " seq = layer(seq, kpm=kpm, attn_mask=attn_mask)\n", "\n", " seq = self.out_norm(seq)\n", " return {n: self.experts[n].extract(seq[:, special_pos[n]]) for n in active}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# METRICS\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float64)\n", " cm[:, 0, 1:] = 1\n", " cm[:, 1:, 0] = 1\n", " cm[:, 1:, 1:] = d2.double()\n", " s = (-1.0) ** V\n", " f = math.factorial(V - 1)\n", " return s / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "def pentachoron_cv(emb, n=200, seed=0):\n", " N = emb.shape[0]\n", " if N < 5:\n", " return 0.0\n", " g = torch.Generator().manual_seed(seed)\n", " vs = []\n", " for _ in range(n):\n", " idx = torch.randperm(N, generator=g)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = float(torch.sqrt(F.relu(v2[0])).item())\n", " if v > 0:\n", " vs.append(v)\n", " if len(vs) < 10:\n", " return 0.0\n", " a = np.array(vs, dtype=np.float64)\n", " return float(a.std() / max(a.mean(), 1e-12))\n", "\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " \"\"\"\n", " Participation ratio of the singular-value spectrum: (sum s^2)^2 / sum s^4.\n", " CV is dimension-dependent and therefore a bad standalone gauge (see CV NULL);\n", " erank says directly how many directions the embedding actually uses.\n", " \"\"\"\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s = torch.linalg.svdvals(xc)\n", " s2 = s ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def query_degeneracy_gate(ds, cfg: AuditConfig, N: int) -> Dict[str, Any]:\n", " \"\"\"\n", " RUNS BEFORE ANY RETRIEVAL ARM. Measures whether the query set actually varies.\n", "\n", " A retrieval score is meaningless if the queries are identical: a constant query\n", " cannot rank a gallery, so R@1 cannot exceed chance for ANY honest model. Scoring\n", " high with degenerate queries is therefore proof that the score is not coming from\n", " the query modality at all.\n", "\n", " This gate exists because the shipped flagship row (test.image_coco_test, R@1\n", " 1.0000, 'text<->image 40K test') was scored on COCO-Caption's *test* split, whose\n", " caption annotations are withheld. cell1_prepare_data.extract_first_text() returns\n", " \"\" / a constant when no caption field is present, masked_text_tokenize pads it to\n", " max_length, and every row ends up the same 3-token sequence.\n", " \"\"\"\n", " n = min(N, 512)\n", " sl = ds[0:n]\n", " th = sl[\"text_hidden\"].float()\n", " tm = sl[\"text_mask\"]\n", " tok_counts = tm.sum(1)\n", " m = tm.float()\n", " den = m.sum(1, keepdim=True).clamp(min=1.0)\n", " pv = (th * m.unsqueeze(-1)).sum(1) / den\n", " pvn = F.normalize(pv, dim=-1)\n", " sim = pvn @ pvn.T\n", " off = sim[~torch.eye(n, dtype=torch.bool)]\n", " er = effective_rank(pv)\n", "\n", " ih = sl[cfg.expert_col].float().mean(1)\n", " er_img = effective_rank(ih)\n", "\n", " degenerate = bool(er < 2.0 or off.mean().item() > 0.999)\n", " out = {\n", " \"n_probed\": n,\n", " \"tokens_per_row_min\": int(tok_counts.min()),\n", " \"tokens_per_row_max\": int(tok_counts.max()),\n", " \"distinct_token_counts\": int(torch.unique(tok_counts).numel()),\n", " \"pooled_text_pairwise_cos_mean\": off.mean().item(),\n", " \"pooled_text_pairwise_cos_min\": off.min().item(),\n", " \"pooled_text_erank\": er,\n", " \"pooled_image_erank\": er_img,\n", " \"QUERY_SET_DEGENERATE\": degenerate,\n", " }\n", " line(\"QUERY DEGENERACY GATE — do the queries vary at all?\")\n", " print(f\" tokens/row: {out['tokens_per_row_min']}..{out['tokens_per_row_max']} \"\n", " f\"({out['distinct_token_counts']} distinct lengths)\")\n", " print(f\" pooled-text pairwise cos: mean {off.mean():.4f} min {off.min():.4f}\")\n", " print(f\" pooled-text erank {er:.2f} | pooled-image erank {er_img:.2f}\")\n", " if degenerate:\n", " print(\" *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\")\n", " print(\" Every retrieval number on this split is uninterpretable AS ALIGNMENT.\")\n", " print(\" A constant query cannot rank a gallery. If A0 still scores high, that\")\n", " print(\" is a direct proof the score does not come from the query modality.\")\n", " else:\n", " print(\" GATE PASS: queries vary; retrieval numbers are interpretable.\")\n", " return out\n", "\n", "\n", "def retrieval_metrics(ea: torch.Tensor, eb: torch.Tensor, gt: Optional[torch.Tensor] = None,\n", " cv: bool = True, cv_seed: int = 0) -> Dict[str, float]:\n", " \"\"\"Published _metrics, plus explicit ground-truth override for the deranged arm.\"\"\"\n", " N = ea.shape[0]\n", " sim = ea @ eb.T\n", " gt = torch.arange(N) if gt is None else gt\n", "\n", " def r_at_k(k, dim):\n", " topk = sim.topk(min(k, N), dim=dim).indices\n", " if dim == 1:\n", " return (topk == gt.unsqueeze(1)).any(1).float().mean().item()\n", " return (topk == gt.unsqueeze(0)).any(0).float().mean().item()\n", "\n", " diag = sim[torch.arange(N), gt]\n", " out = {\n", " \"r1\": (r_at_k(1, 1) + r_at_k(1, 0)) / 2,\n", " \"r5\": (r_at_k(5, 1) + r_at_k(5, 0)) / 2,\n", " \"cos_match\": diag.mean().item(),\n", " \"cos_rand\": (sim.sum() - diag.sum()).item() / max(N * N - N, 1),\n", " \"n\": N,\n", " \"chance_r1\": 1.0 / N,\n", " }\n", " if cv:\n", " out[\"cv_joint\"] = pentachoron_cv(torch.cat([ea, eb]), n=CFG.cv_samples, seed=cv_seed)\n", " out[\"erank_a\"] = effective_rank(ea)\n", " out[\"erank_b\"] = effective_rank(eb)\n", " return out\n", "\n", "\n", "def procrustes_frame_fit(ea: torch.Tensor, eb: torch.Tensor, n_pairs: int,\n", " seed: int = 0) -> Dict[str, float]:\n", " \"\"\"\n", " fp64 orthogonal Procrustes post-fit on a disjoint pair subset, applied to the\n", " query side, then re-scored on the REMAINDER. Standing dist-campaign rider:\n", " relational/unanchored objectives read as false floors under absolute gauges.\n", " \"\"\"\n", " N = ea.shape[0]\n", " k = min(n_pairs, N // 2)\n", " if k < 32:\n", " return {\"skipped\": True}\n", " g = torch.Generator().manual_seed(seed)\n", " perm = torch.randperm(N, generator=g)\n", " fit_idx, hold_idx = perm[:k], perm[k:]\n", " A = ea[fit_idx].double()\n", " B = eb[fit_idx].double()\n", " U, _, Vt = torch.linalg.svd(A.T @ B, full_matrices=False)\n", " R = (U @ Vt)\n", " ea_r = F.normalize((ea.double() @ R).float(), dim=-1)\n", " eb_n = F.normalize(eb, dim=-1)\n", " m_all = retrieval_metrics(ea_r, eb_n, cv=False)\n", " m_hold = retrieval_metrics(ea_r[hold_idx], eb_n[hold_idx], cv=False)\n", " return {\n", " \"fit_pairs\": k,\n", " \"r1_all_rotated\": m_all[\"r1\"],\n", " \"cos_match_all_rotated\": m_all[\"cos_match\"],\n", " \"r1_heldout_rotated\": m_hold[\"r1\"],\n", " \"cos_match_heldout_rotated\": m_hold[\"cos_match\"],\n", " \"n_heldout\": m_hold[\"n\"],\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# DATA + BUILD\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def fetch_split(cfg: AuditConfig):\n", " print(f\"[data] pulling {cfg.n_shards} shard(s) of {cfg.cache_repo}/{cfg.split_dir} \"\n", " f\"(~497MB each) ...\")\n", " parts = []\n", " for i in range(cfg.n_shards):\n", " fn = f\"{cfg.split_dir}/data-{i:05d}-of-{cfg.shard_total:05d}.arrow\"\n", " p = hf_hub_download(cfg.cache_repo, fn, repo_type=\"dataset\")\n", " parts.append(HFDataset.from_file(p))\n", " print(f\" shard {i}: {len(parts[-1])} rows\")\n", " ds = concatenate_datasets(parts).with_format(\"torch\")\n", " print(f\"[data] {len(ds)} rows | columns {ds.column_names}\")\n", " return ds\n", "\n", "\n", "def build_model(cfg: AuditConfig, random_init: bool = False):\n", " torch.manual_seed(cfg.seed)\n", " fcfg = FusionConfig(d_model=cfg.d_model, n_heads=cfg.n_heads, n_layers=cfg.n_layers,\n", " d_ff=cfg.d_ff, dropout=cfg.dropout, max_seq_len=cfg.max_seq_len)\n", " experts = {\n", " \"text\": ExpertModule(\"text\", cfg.d_model, cfg.text_tokens, cfg.d_model,\n", " cfg.n_heads, needs_pooling=False),\n", " cfg.expert: ExpertModule(cfg.expert, cfg.image_input_dim, cfg.image_pooled,\n", " cfg.d_model, cfg.n_heads, needs_pooling=True,\n", " aligner=ProcrustesAligner(cfg.image_input_dim, cfg.d_model,\n", " has_projection=False,\n", " has_expert_whitener=True,\n", " has_text_unwhitener=True)),\n", " }\n", " model = BertensteinFusion(fcfg, experts)\n", "\n", " if not random_init:\n", " p = hf_hub_download(cfg.model_repo, cfg.ckpt_path)\n", " state = safetensors_load(p)\n", " keep = {k: v for k, v in state.items()\n", " if k.startswith(\"experts.text.\") or k.startswith(f\"experts.{cfg.expert}.\")\n", " or k.startswith(\"layers.\") or k in (\"pos\", \"out_norm.weight\", \"out_norm.bias\")}\n", " missing, unexpected = model.load_state_dict(keep, strict=False)\n", " assert not missing, f\"MISSING checkpoint keys (model would be partly random): {missing}\"\n", " print(f\"[model] loaded {len(keep)} tensors | unexpected={list(unexpected)}\")\n", " else:\n", " print(\"[model] RANDOM INIT (control arm)\")\n", "\n", " return model.to(cfg.device).eval()\n", "\n", "\n", "# ── layouts: joint absolute positions, so the split arm is position-matched ──\n", "def joint_layout(cfg: AuditConfig) -> Dict[str, int]:\n", " return {\"text\": 0, cfg.expert: 1 + cfg.text_tokens}\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, ds, cfg: AuditConfig, N: int, mode: str,\n", " perm: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " mode:\n", " 'joint' -> published: one pass, unmasked bidirectional attention (A0)\n", " 'blocked' -> one pass, same positions, cross-modal attention masked (A1)\n", " 'split' -> two independent passes, joint-layout positions (A2)\n", " perm: index permutation applied to the EXPERT side inside the joint pass (A4)\n", " \"\"\"\n", " lay = joint_layout(cfg)\n", " te_all, ee_all = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device)\n", " if perm is None:\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " else:\n", " pi = perm[i:j]\n", " eh = ds[pi.tolist()][cfg.expert_col].to(cfg.device).float()\n", "\n", " if mode in (\"joint\", \"blocked\"):\n", " out = model({\"text\": th, cfg.expert: eh}, {\"text\": tm},\n", " block_cross=(mode == \"blocked\"))\n", " te_all.append(out[\"text\"].float().cpu())\n", " ee_all.append(out[cfg.expert].float().cpu())\n", " elif mode == \"split\":\n", " o_t = model({\"text\": th}, {\"text\": tm}, layout_positions=lay)\n", " o_e = model({cfg.expert: eh}, {}, layout_positions=lay)\n", " te_all.append(o_t[\"text\"].float().cpu())\n", " ee_all.append(o_e[cfg.expert].float().cpu())\n", " else:\n", " raise ValueError(mode)\n", " return torch.cat(te_all)[:N], torch.cat(ee_all)[:N]\n", "\n", "\n", "@torch.no_grad()\n", "def procrustes_reference(model, ds, cfg: AuditConfig, N: int) -> Dict[str, float]:\n", " \"\"\"A5: pooled frozen features through the shipped aligner. No fusion layer.\"\"\"\n", " aligner = model.experts[cfg.expert].aligner\n", " tv, ev = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device).float()\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " denom = tm.sum(1, keepdim=True).clamp(min=1.0)\n", " tv.append(((th * tm.unsqueeze(-1)).sum(1) / denom).cpu())\n", " ev.append(aligner(eh).mean(1).cpu())\n", " tv = F.normalize(torch.cat(tv)[:N], dim=-1)\n", " ev = F.normalize(torch.cat(ev)[:N], dim=-1)\n", " m = retrieval_metrics(tv, ev, cv=False)\n", " m[\"note\"] = \"frozen features + shipped aligner, no fusion layer\"\n", " return m\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def line(t=\"\"):\n", " print(\"─\" * 78 if not t else f\"── {t} \" + \"─\" * max(0, 74 - len(t)))\n", "\n", "\n", "def fmt(m: Dict[str, Any]) -> str:\n", " s = (f\"R@1={m['r1']:.4f} R@5={m['r5']:.4f} cos_match={m['cos_match']:+.4f} \"\n", " f\"cos_rand={m['cos_rand']:+.5f} chance={m['chance_r1']:.5f}\")\n", " if \"cv_joint\" in m:\n", " s += f\" CV={m['cv_joint']:.4f} erank={m['erank_a']:.1f}/{m['erank_b']:.1f}\"\n", " return s\n", "\n", "\n", "def run(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " t0 = time.time()\n", " print(\"=\" * 78)\n", " print(\"BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\")\n", " print(\"=\" * 78)\n", " print(f\"device={cfg.device} split={cfg.split_dir} expert={cfg.expert}\")\n", "\n", " ds = fetch_split(cfg)\n", " N = min(cfg.eval_n, len(ds))\n", " if N < cfg.eval_n:\n", " print(f\"[warn] only {N} rows available; raise cfg.n_shards for the full {cfg.eval_n}\")\n", "\n", " model = build_model(cfg, random_init=False)\n", " rnd = build_model(cfg, random_init=True)\n", " R: Dict[str, Any] = {\"n\": N, \"config\": {\"n_shards\": cfg.n_shards, \"seed\": cfg.seed,\n", " \"split_dir\": cfg.split_dir}}\n", "\n", " R[\"query_gate\"] = query_degeneracy_gate(ds, cfg, N)\n", " if cfg.split_dir == \"image_coco\":\n", " print(\" NOTE: the model TRAINED on 85% of image_coco (train_test_split seed 42).\")\n", " print(\" Numbers on this split are a CONTAMINATED UPPER BOUND — which makes a\")\n", " print(\" failure here conclusive, and a success here uninformative.\")\n", "\n", " # ── A0 PUBLISHED ─────────────────────────────────────────────────────────\n", " line(\"A0 PUBLISHED — joint pass, unmasked bidirectional attention\")\n", " te, ee = encode(model, ds, cfg, N, \"joint\")\n", " R[\"A0_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A0_published\"]))\n", " d_r1 = abs(R[\"A0_published\"][\"r1\"] - cfg.ref_r1)\n", " d_cm = abs(R[\"A0_published\"][\"cos_match\"] - cfg.ref_cos_match)\n", " ok = (d_r1 <= cfg.reproduce_tol) and (d_cm <= cfg.reproduce_tol)\n", " R[\"A0_reproduction_gate\"] = {\"pass\": bool(ok), \"d_r1\": d_r1, \"d_cos_match\": d_cm,\n", " \"ref_r1\": cfg.ref_r1, \"ref_cos_match\": cfg.ref_cos_match}\n", " print(f\" GATE reproduce-published: {'PASS' if ok else 'FAIL'} \"\n", " f\"(Δr1={d_r1:.4f}, Δcos={d_cm:.4f})\")\n", " if not ok:\n", " print(\" !! A0 did not reproduce. Downstream arms are uninterpretable until it does.\")\n", "\n", " # ── A1 BLOCKED ───────────────────────────────────────────────────────────\n", " line(\"A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED\")\n", " te, ee = encode(model, ds, cfg, N, \"blocked\")\n", " R[\"A1_blocked\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A1_blocked\"]))\n", " R[\"A1_blocked_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A1_blocked_framefit\"]))\n", "\n", " # ── A2 SPLIT ─────────────────────────────────────────────────────────────\n", " line(\"A2 SPLIT — two independent forward passes (what retrieval means)\")\n", " te2, ee2 = encode(model, ds, cfg, N, \"split\")\n", " R[\"A2_split\"] = retrieval_metrics(te2, ee2, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A2_split\"]))\n", "\n", " # correctness invariant found at smoke: with n_layers=1, block-diagonal masking\n", " # and position-matched independent encoding are the SAME computation. If these\n", " # diverge, the mask or the layout offset is wrong and A1/A2 are uninterpretable.\n", " d_inv = max((te - te2).abs().max().item(), (ee - ee2).abs().max().item())\n", " R[\"A1_A2_identity_gate\"] = {\"pass\": bool(d_inv < 1e-4), \"max_abs_diff\": d_inv}\n", " print(f\" GATE A1==A2 (must hold at n_layers=1): \"\n", " f\"{'PASS' if d_inv < 1e-4 else 'FAIL'} max|Δ|={d_inv:.2e}\")\n", " te, ee = te2, ee2\n", " R[\"A2_split_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A2_split_framefit\"]))\n", "\n", " # ── A3 UNTRAINED ─────────────────────────────────────────────────────────\n", " line(\"A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"joint\")\n", " R[\"A3_untrained_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3_untrained_published\"]))\n", "\n", " line(\"A3b UNTRAINED — random init, SPLIT protocol (the floor)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"split\")\n", " R[\"A3b_untrained_split\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3b_untrained_split\"]))\n", "\n", " # ── A4 DERANGED ──────────────────────────────────────────────────────────\n", " line(\"A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index\")\n", " g = torch.Generator().manual_seed(cfg.seed + 991)\n", " perm = torch.randperm(N, generator=g)\n", " fixed = int((perm == torch.arange(N)).sum())\n", " te, ee = encode(model, ds, cfg, N, \"joint\", perm=perm)\n", " R[\"A4_deranged\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " R[\"A4_deranged\"][\"accidental_fixed_points\"] = fixed\n", " print(\" \", fmt(R[\"A4_deranged\"]), f\" (accidental fixed points: {fixed})\")\n", " print(\" If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\")\n", "\n", " # ── A5 PROCRUSTES REFERENCE ──────────────────────────────────────────────\n", " line(\"A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion\")\n", " R[\"A5_procrustes_reference\"] = procrustes_reference(model, ds, cfg, N)\n", " print(\" \", fmt(R[\"A5_procrustes_reference\"]))\n", " print(f\" published cos_after for image = {cfg.ref_procrustes_cos_after:.4f} \"\n", " f\"(the honest shared-structure ceiling)\")\n", "\n", " # ── CV NULL ──────────────────────────────────────────────────────────────\n", " line(\"CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null\")\n", " g2 = torch.Generator().manual_seed(cfg.seed + 7)\n", " nulls = {}\n", " for d in cfg.cv_null_dims:\n", " z = F.normalize(torch.randn(2 * min(N, 2048), d, generator=g2), dim=-1)\n", " nulls[d] = pentachoron_cv(z, n=cfg.cv_samples, seed=cfg.seed)\n", " print(f\" normalized randn, ZERO training, d={d:<5d}: CV = {nulls[d]:.4f}\")\n", " R[\"cv_null_by_dim\"] = nulls\n", " print(f\" published model CV (image_coco_test) : {cfg.ref_cv_joint:.4f}\")\n", " print(f\" measured A0 CV : \"\n", " f\"{R['A0_published'].get('cv_joint', float('nan')):.4f}\")\n", " print(f\" measured A0 effective rank (text/expert): \"\n", " f\"{R['A0_published'].get('erank_a', float('nan')):.1f} / \"\n", " f\"{R['A0_published'].get('erank_b', float('nan')):.1f} of nominal {cfg.d_model}\")\n", " print(\" READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\")\n", " print(\" claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\")\n", " print(\" if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\")\n", " print(\" clothes, not convergence to a universal constant.\")\n", "\n", " # ── SWEEP ────────────────────────────────────────────────────────────────\n", " line(\"SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N\")\n", " sweep = {}\n", " for mode, tag, mdl in ((\"joint\", \"A0_published\", model),\n", " (\"blocked\", \"A1_blocked\", model),\n", " (\"split\", \"A2_split\", model),\n", " (\"joint\", \"A3_untrained\", rnd)):\n", " rows = []\n", " for n in cfg.sweep_n:\n", " if n > N:\n", " continue\n", " a, b = encode(mdl, ds, cfg, n, mode)\n", " m = retrieval_metrics(a, b, cv=False)\n", " rows.append({\"N\": n, \"r1\": m[\"r1\"], \"cos_match\": m[\"cos_match\"],\n", " \"chance\": m[\"chance_r1\"]})\n", " sweep[tag] = rows\n", " R[\"sweep\"] = sweep\n", " hdr = \" N \" + \"\".join(f\"{t:>22s}\" for t in sweep.keys())\n", " print(hdr)\n", " for k, n in enumerate(cfg.sweep_n):\n", " if n > N:\n", " continue\n", " cells = []\n", " for tag in sweep:\n", " row = next((r for r in sweep[tag] if r[\"N\"] == n), None)\n", " cells.append(f\"{row['r1']:>22.4f}\" if row else f\"{'-':>22s}\")\n", " print(f\" {n:<7d}\" + \"\".join(cells))\n", "\n", " # ── VERDICT ──────────────────────────────────────────────────────────────\n", " line(\"VERDICT\")\n", " a0, a1, a2, a3, a4 = (R[\"A0_published\"][\"r1\"], R[\"A1_blocked\"][\"r1\"],\n", " R[\"A2_split\"][\"r1\"], R[\"A3_untrained_published\"][\"r1\"],\n", " R[\"A4_deranged\"][\"r1\"])\n", " leak_signals = {\n", " \"constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\":\n", " bool(R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"] and a0 >= 0.9),\n", " \"untrained_reproduces_headline (A3 >= 0.9*A0)\": bool(a3 >= 0.9 * a0),\n", " \"deranged_pairs_still_retrieve (A4 >= 0.9*A0)\": bool(a4 >= 0.9 * a0),\n", " \"blocking_cross_attention_collapses (A1 < 0.5*A0)\": bool(a1 < 0.5 * a0),\n", " \"independent_encoding_collapses (A2 < 0.5*A0)\": bool(a2 < 0.5 * a0),\n", " }\n", " R[\"leak_signals\"] = leak_signals\n", " for k, v in leak_signals.items():\n", " print(f\" [{'X' if v else ' '}] {k}\")\n", " n_leak = sum(leak_signals.values())\n", " R[\"n_leak_signals\"] = n_leak\n", " if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"]:\n", " print(\"\\n CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\")\n", " print(\" OVER-DETERMINED — a perfect model would collapse too. Re-run with\")\n", " print(\" cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\")\n", " if n_leak >= 3:\n", " R[\"verdict\"] = \"PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\"\n", " elif n_leak == 0:\n", " R[\"verdict\"] = \"NO LEAK DETECTED — the number survives every cut; escalate to seeds + a second split\"\n", " else:\n", " R[\"verdict\"] = f\"MIXED ({n_leak}/4 leak signals) — read the arms individually, do not summarize\"\n", " print(f\"\\n => {R['verdict']}\")\n", " print(f\"\\n[done] {time.time() - t0:.1f}s\")\n", "\n", " with open(\"bertenstein_audit_results.json\", \"w\") as f:\n", " json.dump(R, f, indent=2, default=float)\n", " print(\"[done] wrote bertenstein_audit_results.json\")\n", " return R\n", "\n", "\n", "# ── activation ───────────────────────────────────────────────────────────────\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "iPoyXVu-eR7b", "outputId": "b3a47f2c-ba5e-4d69-d491-d41805be3e49" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco_test expert=image\n", "[data] pulling 6 shard(s) of AbstractPhil/bertenstein-v1/image_coco_test (~497MB each) ...\n", " shard 0: 833 rows\n", " shard 1: 833 rows\n", " shard 2: 833 rows\n", " shard 3: 833 rows\n", " shard 4: 833 rows\n", " shard 5: 833 rows\n", "[data] 4998 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4998 rows available; raise cfg.n_shards for the full 5000\n", "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── QUERY DEGENERACY GATE — do the queries vary at all? ───────────────────────\n", " tokens/row: 3..3 (1 distinct lengths)\n", " pooled-text pairwise cos: mean 1.0000 min 1.0000\n", " pooled-text erank 1.00 | pooled-image erank 48.92\n", " *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\n", " Every retrieval number on this split is uninterpretable AS ALIGNMENT.\n", " A constant query cannot rank a gallery. If A0 still scores high, that\n", " is a direct proof the score does not come from the query modality.\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1961 erank=20.5/20.4\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0000)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0001 R@5=0.0007 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.3156 erank=1.0/20.6\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0004001600609626621, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0002 R@5=0.0008 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.4199 erank=1.0/20.6\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=6.71e-08\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0006002400914439932, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0015 cos_match=-0.0177 cos_rand=-0.01890 chance=0.00020 CV=0.2661 erank=41.8/44.3\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0002 R@5=0.0011 cos_match=-0.0060 cos_rand=-0.00601 chance=0.00020 CV=0.1381 erank=1.1/51.8\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1721 erank=20.5/20.4 (accidental fixed points: 1)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.0002 R@5=0.0010 cos_match=+0.0079 cos_rand=+0.00788 chance=0.00020\n", " published cos_after for image = 0.4107 (the honest shared-structure ceiling)\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1961\n", " measured A0 effective rank (text/expert): 20.5 / 20.4 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0156 0.0156 0.0234\n", " 128 1.0000 0.0078 0.0078 0.0117\n", " 256 1.0000 0.0039 0.0039 0.0000\n", " 512 1.0000 0.0020 0.0020 0.0010\n", " 1024 1.0000 0.0010 0.0010 0.0010\n", " 2048 1.0000 0.0005 0.0005 0.0007\n", " 4096 1.0000 0.0002 0.0002 0.0004\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [X] constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", "\n", " CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\n", " OVER-DETERMINED — a perfect model would collapse too. Re-run with\n", " cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 21.8s\n", "[done] wrote bertenstein_audit_results.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# BERTENSTEIN R@1 AUDIT — single Colab cell\n", "#\n", "# Question under test: is GEOLIP-Bertenstein's R@1 = 1.0000 a measurement of\n", "# cross-modal alignment, or a measurement of the evaluation protocol?\n", "#\n", "# The published eval (cell2_prototype_model_trainer_v1.py :748 eval_direct)\n", "# encodes text AND the expert modality in ONE joint forward pass through an\n", "# UNMASKED bidirectional fusion layer, reads both special tokens out\n", "# of that single sequence, and calls the cosine between them \"retrieval\".\n", "# Every diagonal entry sim[i,i] is therefore two readout heads of the same\n", "# fused encoding of item i. The off-diagonal sim[i,j] compares readouts from\n", "# two forward passes that never saw each other.\n", "#\n", "# This bed holds everything else constant and cuts exactly one wire at a time.\n", "#\n", "# ARMS (all on the shipped checkpoints/final weights, image<->text,\n", "# image_coco_test = the exact split that produced R@1 = 1.0000)\n", "# A0 PUBLISHED joint sequence, full bidirectional attention. Reproduce.\n", "# A1 BLOCKED identical sequence + positions; cross-modal attention MASKED.\n", "# The single-variable cut. Nothing else changes.\n", "# A2 SPLIT two fully independent forward passes. What \"retrieval\" means.\n", "# A3 UNTRAINED random init, PUBLISHED protocol. Does the metric need weights?\n", "# A3b UNTRAINED random init, SPLIT protocol. The floor.\n", "# A4 DERANGED joint pass, text[i] fused with image[perm[i]]. Ground truth\n", "# stays the FORWARD-PASS index. If R@1 holds, the diagonal is\n", "# pass-identity and carries zero semantic content.\n", "# A5 PROCRUSTES pooled frozen features through the shipped aligner, no fusion.\n", "# The honest ceiling (results.json reports cos_after 0.4107).\n", "# SWEEP gallery size N. A leak is N-invariant; retrieval decays ~log N.\n", "# FRAME FIT fp64 orthogonal Procrustes post-fit on the collapsed arms\n", "# before verdicting — the standing dist-campaign law, so we do\n", "# not publish a false floor the way affinity_kl nearly did.\n", "# CV NULL pentachoron CV of untrained normalized randn, printed beside\n", "# the model's CV, because the \"0.20 universal band\" claim needs\n", "# its null on the same line.\n", "#\n", "# Colab-cell-safe: no argparse, no __file__, paste-ahead-safe, globals() guarded.\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "import os, sys, json, math, time, subprocess\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Tuple\n", "\n", "# ── deps (idempotent; quiet) ─────────────────────────────────────────────────\n", "for _pkg, _imp in [(\"datasets\", \"datasets\"), (\"safetensors\", \"safetensors\"),\n", " (\"huggingface_hub\", \"huggingface_hub\")]:\n", " try:\n", " __import__(_imp)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _pkg], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from datasets import Dataset as HFDataset, concatenate_datasets\n", "from huggingface_hub import hf_hub_download\n", "from safetensors.torch import load_file as safetensors_load\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class AuditConfig:\n", " # sources\n", " model_repo: str = \"AbstractPhil/geolip-bertenstein\"\n", " cache_repo: str = \"AbstractPhil/bertenstein-v1\"\n", " ckpt_path: str = \"checkpoints/final/model.safetensors\"\n", " # WHICH SPLITS THE BED RUNS (run_all iterates these in order).\n", " # image_coco = COCO-Caption \"val\" -> REAL captions (9-28 tokens, erank 37.8).\n", " # THE VALID ONE. Default. NOTE: the model trained on 85% of this\n", " # (train_test_split seed 42), so it is a contaminated UPPER BOUND\n", " # -- a failure here is conclusive, a success is uninformative.\n", " # image_coco_test = COCO-Caption \"test\". COCO 2014 test annotations are withheld;\n", " # upstream encodes that as answer=['None'], id=-1. extract_first_text\n", " # returned the STRING \"None\", BERT encoded it, and it was written to\n", " # Arrow at commit 3e8765c9 (2026-03-07 17:58). Every row identical:\n", " # 3 tokens, pooled-text erank 1.00, pairwise cos 1.0000.\n", " # The flagship \"40K test / R@1 1.0000\" row was scored on this.\n", " # Kept ONLY to reproduce that number and show what it measures.\n", " split_dir: str = \"image_coco\"\n", " run_splits: Tuple[str, ...] = (\"image_coco\", \"image_coco_test\")\n", " n_shards: int = 6 # ~830 rows/shard, ~494MB/shard\n", " shard_total: int = 49 # both splits are data-NNNNN-of-00049.arrow\n", " expert: str = \"image\"\n", " expert_col: str = \"image_hidden\"\n", " expert_mask_col: Optional[str] = None # image_coco_test ships no image mask\n", "\n", " # architecture (must reproduce checkpoint keys exactly)\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", " text_tokens: int = 32 # text expert: needs_pooling=False, n_pooled=32\n", " image_pooled: int = 16 # image expert: needs_pooling=True, n_pooled=16\n", " image_input_dim: int = 1024 # DINOv2-large, 1024->1024 direct (no projection)\n", "\n", " # protocol\n", " batch_size: int = 128\n", " eval_n: int = 5000 # published n for image_coco_test\n", " sweep_n: Tuple[int, ...] = (64, 128, 256, 512, 1024, 2048, 4096)\n", " frame_fit_pairs: int = 2500 # standing dist-campaign rider\n", " cv_samples: int = 200\n", " cv_null_dims: Tuple[int, ...] = (16, 64, 256, 1024)\n", " seed: int = 0\n", " device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", " # published reference values (bertenstein_results.json -> test.image_coco_test)\n", " ref_r1: float = 1.0\n", " ref_cos_match: float = 0.9735139608383179\n", " ref_cos_rand: float = 0.05161707841568314\n", " ref_cv_joint: float = 0.2007410876349909\n", " ref_procrustes_cos_after: float = 0.4106820225715637\n", " reproduce_tol: float = 0.02 # A0 gate; wider than fp noise, tight enough\n", "\n", "\n", "CFG = AuditConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# MODEL — key-compatible re-declaration of the shipped classes,\n", "# extended ONLY with an optional attn_mask pathway (arm A1).\n", "# State-dict keys are unchanged: layers.0.*, experts..*, pos, out_norm.*\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class FusionConfig:\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", "\n", "\n", "class ProcrustesAligner(nn.Module):\n", " \"\"\"Buffer-only module. Shapes taken from the checkpoint; values loaded from it.\"\"\"\n", "\n", " def __init__(self, d_expert: int, d_text: int, has_projection: bool,\n", " has_expert_whitener: bool, has_text_unwhitener: bool):\n", " super().__init__()\n", " self.register_buffer(\"expert_mean\", torch.zeros(d_expert))\n", " self.register_buffer(\"rotation\", torch.eye(d_text))\n", " if has_projection:\n", " self.register_buffer(\"projection\", torch.zeros(d_expert, d_text))\n", " else:\n", " self.projection = None\n", " if has_expert_whitener:\n", " self.register_buffer(\"expert_whitener\", torch.eye(d_text))\n", " else:\n", " self.expert_whitener = None\n", " if has_text_unwhitener:\n", " self.register_buffer(\"text_unwhitener\", torch.eye(d_text))\n", " else:\n", " self.text_unwhitener = None\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " x = x - self.expert_mean\n", " if self.projection is not None:\n", " x = x @ self.projection\n", " if self.expert_whitener is not None:\n", " x = x @ self.expert_whitener\n", " x = x @ self.rotation.T\n", " if self.text_unwhitener is not None:\n", " x = x @ self.text_unwhitener\n", " return x\n", "\n", "\n", "class FusionLayer(nn.Module):\n", " def __init__(self, c: FusionConfig):\n", " super().__init__()\n", " self.attn = nn.MultiheadAttention(c.d_model, c.n_heads, dropout=c.dropout,\n", " batch_first=True)\n", " self.ff = nn.Sequential(\n", " nn.Linear(c.d_model, c.d_ff),\n", " nn.GELU(),\n", " nn.Linear(c.d_ff, c.d_model),\n", " nn.Dropout(c.dropout),\n", " )\n", " self.n1 = nn.LayerNorm(c.d_model)\n", " self.n2 = nn.LayerNorm(c.d_model)\n", " self.drop = nn.Dropout(c.dropout)\n", "\n", " def forward(self, x, kpm=None, attn_mask=None):\n", " h = self.n1(x)\n", " a, _ = self.attn(h, h, h, key_padding_mask=kpm, attn_mask=attn_mask)\n", " x = x + self.drop(a)\n", " x = x + self.ff(self.n2(x))\n", " return x\n", "\n", "\n", "class ExpertModule(nn.Module):\n", " def __init__(self, name, input_dim, n_pooled, d_model=1024, n_heads=16,\n", " needs_pooling=True, aligner=None):\n", " super().__init__()\n", " self.name = name\n", " self.n_pooled = n_pooled\n", " self.needs_pooling = needs_pooling\n", " self.aligner = aligner\n", " proj_input_dim = d_model if aligner is not None else input_dim\n", " self.input_proj = nn.Linear(proj_input_dim, d_model)\n", " if needs_pooling:\n", " self.pool_queries = nn.Parameter(torch.randn(1, n_pooled, d_model) * 0.02)\n", " self.pool_attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True)\n", " self.pool_norm = nn.LayerNorm(d_model)\n", " self.special_token = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.modality_emb = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.output_head = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.Linear(d_model, d_model),\n", " )\n", "\n", " def prepare_tokens(self, hidden, mask=None):\n", " B = hidden.shape[0]\n", " if self.aligner is not None:\n", " hidden = self.aligner(hidden)\n", " proj = self.input_proj(hidden)\n", " if self.needs_pooling:\n", " q = self.pool_queries.expand(B, -1, -1)\n", " kpm = (~mask.bool()) if mask is not None else None\n", " p, _ = self.pool_attn(q, proj, proj, key_padding_mask=kpm)\n", " tokens = self.pool_norm(p + q)\n", " else:\n", " tokens = proj[:, : self.n_pooled]\n", " return tokens + self.modality_emb\n", "\n", " def get_special(self, B):\n", " return self.special_token.expand(B, -1, -1)\n", "\n", " def extract(self, h):\n", " return F.normalize(self.output_head(h), dim=-1)\n", "\n", "\n", "class BertensteinFusion(nn.Module):\n", " \"\"\"\n", " Verbatim-compatible with the shipped model, plus `block_cross`:\n", " block_cross=False -> published behaviour (unmasked bidirectional attention)\n", " block_cross=True -> identical sequence, identical positions, cross-modal\n", " attention forbidden. Arm A1.\n", " `layout_positions` reproduces the JOINT absolute positions even when only one\n", " modality is present, so arm A2 is not confounded by positional-encoding shift.\n", " \"\"\"\n", "\n", " def __init__(self, fusion_cfg: FusionConfig, experts_dict: Dict[str, ExpertModule]):\n", " super().__init__()\n", " self.cfg = fusion_cfg\n", " self.expert_order = list(experts_dict.keys())\n", " self.experts = nn.ModuleDict(experts_dict)\n", " self.pos = nn.Parameter(torch.randn(1, fusion_cfg.max_seq_len, fusion_cfg.d_model) * 0.02)\n", " self.layers = nn.ModuleList([FusionLayer(fusion_cfg) for _ in range(fusion_cfg.n_layers)])\n", " self.out_norm = nn.LayerNorm(fusion_cfg.d_model)\n", "\n", " def forward(self, expert_hidden, expert_masks=None, block_cross=False,\n", " layout_positions: Optional[Dict[str, int]] = None):\n", " active = [n for n in self.expert_order if n in expert_hidden]\n", " B = next(iter(expert_hidden.values())).shape[0]\n", " expert_masks = expert_masks or {}\n", "\n", " seq_parts, mask_parts, special_pos, spans = [], [], {}, {}\n", " pos = 0\n", " for name in active:\n", " exp = self.experts[name]\n", " h = expert_hidden[name]\n", " m = expert_masks.get(name, None)\n", " start = pos\n", " seq_parts.append(exp.get_special(B))\n", " mask_parts.append(torch.zeros(B, 1, device=h.device, dtype=torch.bool))\n", " special_pos[name] = pos\n", " pos += 1\n", " tokens = exp.prepare_tokens(h, mask=m)\n", " nt = tokens.shape[1]\n", " seq_parts.append(tokens)\n", " mask_parts.append(torch.zeros(B, nt, device=h.device, dtype=torch.bool))\n", " pos += nt\n", " spans[name] = (start, pos)\n", "\n", " seq = torch.cat(seq_parts, 1)\n", " kpm = torch.cat(mask_parts, 1)\n", " L = seq.shape[1]\n", "\n", " # positional encoding: joint-layout absolute positions when requested\n", " if layout_positions is not None:\n", " idx = torch.empty(L, dtype=torch.long, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " base = layout_positions[name]\n", " idx[s:e] = torch.arange(base, base + (e - s), device=seq.device)\n", " seq = seq + self.pos[0, idx].unsqueeze(0)\n", " else:\n", " seq = seq + self.pos[:, :L]\n", "\n", " attn_mask = None\n", " if block_cross and len(active) > 1:\n", " attn_mask = torch.ones(L, L, dtype=torch.bool, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " attn_mask[s:e, s:e] = False # within-modality allowed, cross forbidden\n", "\n", " for layer in self.layers:\n", " seq = layer(seq, kpm=kpm, attn_mask=attn_mask)\n", "\n", " seq = self.out_norm(seq)\n", " return {n: self.experts[n].extract(seq[:, special_pos[n]]) for n in active}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# METRICS\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float64)\n", " cm[:, 0, 1:] = 1\n", " cm[:, 1:, 0] = 1\n", " cm[:, 1:, 1:] = d2.double()\n", " s = (-1.0) ** V\n", " f = math.factorial(V - 1)\n", " return s / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "def pentachoron_cv(emb, n=200, seed=0):\n", " N = emb.shape[0]\n", " if N < 5:\n", " return 0.0\n", " g = torch.Generator().manual_seed(seed)\n", " vs = []\n", " for _ in range(n):\n", " idx = torch.randperm(N, generator=g)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = float(torch.sqrt(F.relu(v2[0])).item())\n", " if v > 0:\n", " vs.append(v)\n", " if len(vs) < 10:\n", " return 0.0\n", " a = np.array(vs, dtype=np.float64)\n", " return float(a.std() / max(a.mean(), 1e-12))\n", "\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " \"\"\"\n", " Participation ratio of the singular-value spectrum: (sum s^2)^2 / sum s^4.\n", " CV is dimension-dependent and therefore a bad standalone gauge (see CV NULL);\n", " erank says directly how many directions the embedding actually uses.\n", " \"\"\"\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s = torch.linalg.svdvals(xc)\n", " s2 = s ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def query_degeneracy_gate(ds, cfg: AuditConfig, N: int) -> Dict[str, Any]:\n", " \"\"\"\n", " RUNS BEFORE ANY RETRIEVAL ARM. Measures whether the query set actually varies.\n", "\n", " A retrieval score is meaningless if the queries are identical: a constant query\n", " cannot rank a gallery, so R@1 cannot exceed chance for ANY honest model. Scoring\n", " high with degenerate queries is therefore proof that the score is not coming from\n", " the query modality at all.\n", "\n", " This gate exists because the shipped flagship row (test.image_coco_test, R@1\n", " 1.0000, 'text<->image 40K test') was scored on COCO-Caption's *test* split, whose\n", " caption annotations are withheld. cell1_prepare_data.extract_first_text() returns\n", " \"\" / a constant when no caption field is present, masked_text_tokenize pads it to\n", " max_length, and every row ends up the same 3-token sequence.\n", " \"\"\"\n", " n = min(N, 512)\n", " sl = ds[0:n]\n", " th = sl[\"text_hidden\"].float()\n", " tm = sl[\"text_mask\"]\n", " tok_counts = tm.sum(1)\n", " m = tm.float()\n", " den = m.sum(1, keepdim=True).clamp(min=1.0)\n", " pv = (th * m.unsqueeze(-1)).sum(1) / den\n", " pvn = F.normalize(pv, dim=-1)\n", " sim = pvn @ pvn.T\n", " off = sim[~torch.eye(n, dtype=torch.bool)]\n", " er = effective_rank(pv)\n", "\n", " ih = sl[cfg.expert_col].float().mean(1)\n", " er_img = effective_rank(ih)\n", "\n", " degenerate = bool(er < 2.0 or off.mean().item() > 0.999)\n", " out = {\n", " \"n_probed\": n,\n", " \"tokens_per_row_min\": int(tok_counts.min()),\n", " \"tokens_per_row_max\": int(tok_counts.max()),\n", " \"distinct_token_counts\": int(torch.unique(tok_counts).numel()),\n", " \"pooled_text_pairwise_cos_mean\": off.mean().item(),\n", " \"pooled_text_pairwise_cos_min\": off.min().item(),\n", " \"pooled_text_erank\": er,\n", " \"pooled_image_erank\": er_img,\n", " \"QUERY_SET_DEGENERATE\": degenerate,\n", " }\n", " line(\"QUERY DEGENERACY GATE — do the queries vary at all?\")\n", " print(f\" tokens/row: {out['tokens_per_row_min']}..{out['tokens_per_row_max']} \"\n", " f\"({out['distinct_token_counts']} distinct lengths)\")\n", " print(f\" pooled-text pairwise cos: mean {off.mean():.4f} min {off.min():.4f}\")\n", " print(f\" pooled-text erank {er:.2f} | pooled-image erank {er_img:.2f}\")\n", " if degenerate:\n", " print(\" *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\")\n", " print(\" Every retrieval number on this split is uninterpretable AS ALIGNMENT.\")\n", " print(\" A constant query cannot rank a gallery. If A0 still scores high, that\")\n", " print(\" is a direct proof the score does not come from the query modality.\")\n", " else:\n", " print(\" GATE PASS: queries vary; retrieval numbers are interpretable.\")\n", " return out\n", "\n", "\n", "def retrieval_metrics(ea: torch.Tensor, eb: torch.Tensor, gt: Optional[torch.Tensor] = None,\n", " cv: bool = True, cv_seed: int = 0) -> Dict[str, float]:\n", " \"\"\"Published _metrics, plus explicit ground-truth override for the deranged arm.\"\"\"\n", " N = ea.shape[0]\n", " sim = ea @ eb.T\n", " gt = torch.arange(N) if gt is None else gt\n", "\n", " def r_at_k(k, dim):\n", " topk = sim.topk(min(k, N), dim=dim).indices\n", " if dim == 1:\n", " return (topk == gt.unsqueeze(1)).any(1).float().mean().item()\n", " return (topk == gt.unsqueeze(0)).any(0).float().mean().item()\n", "\n", " diag = sim[torch.arange(N), gt]\n", " out = {\n", " \"r1\": (r_at_k(1, 1) + r_at_k(1, 0)) / 2,\n", " \"r5\": (r_at_k(5, 1) + r_at_k(5, 0)) / 2,\n", " \"cos_match\": diag.mean().item(),\n", " \"cos_rand\": (sim.sum() - diag.sum()).item() / max(N * N - N, 1),\n", " \"n\": N,\n", " \"chance_r1\": 1.0 / N,\n", " }\n", " if cv:\n", " out[\"cv_joint\"] = pentachoron_cv(torch.cat([ea, eb]), n=CFG.cv_samples, seed=cv_seed)\n", " out[\"erank_a\"] = effective_rank(ea)\n", " out[\"erank_b\"] = effective_rank(eb)\n", " return out\n", "\n", "\n", "def procrustes_frame_fit(ea: torch.Tensor, eb: torch.Tensor, n_pairs: int,\n", " seed: int = 0) -> Dict[str, float]:\n", " \"\"\"\n", " fp64 orthogonal Procrustes post-fit on a disjoint pair subset, applied to the\n", " query side, then re-scored on the REMAINDER. Standing dist-campaign rider:\n", " relational/unanchored objectives read as false floors under absolute gauges.\n", " \"\"\"\n", " N = ea.shape[0]\n", " k = min(n_pairs, N // 2)\n", " if k < 32:\n", " return {\"skipped\": True}\n", " g = torch.Generator().manual_seed(seed)\n", " perm = torch.randperm(N, generator=g)\n", " fit_idx, hold_idx = perm[:k], perm[k:]\n", " A = ea[fit_idx].double()\n", " B = eb[fit_idx].double()\n", " U, _, Vt = torch.linalg.svd(A.T @ B, full_matrices=False)\n", " R = (U @ Vt)\n", " ea_r = F.normalize((ea.double() @ R).float(), dim=-1)\n", " eb_n = F.normalize(eb, dim=-1)\n", " m_all = retrieval_metrics(ea_r, eb_n, cv=False)\n", " m_hold = retrieval_metrics(ea_r[hold_idx], eb_n[hold_idx], cv=False)\n", " return {\n", " \"fit_pairs\": k,\n", " \"r1_all_rotated\": m_all[\"r1\"],\n", " \"cos_match_all_rotated\": m_all[\"cos_match\"],\n", " \"r1_heldout_rotated\": m_hold[\"r1\"],\n", " \"cos_match_heldout_rotated\": m_hold[\"cos_match\"],\n", " \"n_heldout\": m_hold[\"n\"],\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# DATA + BUILD\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def fetch_split(cfg: AuditConfig):\n", " print(f\"[data] pulling {cfg.n_shards} shard(s) of {cfg.cache_repo}/{cfg.split_dir} \"\n", " f\"(~497MB each) ...\")\n", " parts = []\n", " for i in range(cfg.n_shards):\n", " fn = f\"{cfg.split_dir}/data-{i:05d}-of-{cfg.shard_total:05d}.arrow\"\n", " p = hf_hub_download(cfg.cache_repo, fn, repo_type=\"dataset\")\n", " parts.append(HFDataset.from_file(p))\n", " print(f\" shard {i}: {len(parts[-1])} rows\")\n", " ds = concatenate_datasets(parts).with_format(\"torch\")\n", " print(f\"[data] {len(ds)} rows | columns {ds.column_names}\")\n", " return ds\n", "\n", "\n", "def build_model(cfg: AuditConfig, random_init: bool = False):\n", " torch.manual_seed(cfg.seed)\n", " fcfg = FusionConfig(d_model=cfg.d_model, n_heads=cfg.n_heads, n_layers=cfg.n_layers,\n", " d_ff=cfg.d_ff, dropout=cfg.dropout, max_seq_len=cfg.max_seq_len)\n", " experts = {\n", " \"text\": ExpertModule(\"text\", cfg.d_model, cfg.text_tokens, cfg.d_model,\n", " cfg.n_heads, needs_pooling=False),\n", " cfg.expert: ExpertModule(cfg.expert, cfg.image_input_dim, cfg.image_pooled,\n", " cfg.d_model, cfg.n_heads, needs_pooling=True,\n", " aligner=ProcrustesAligner(cfg.image_input_dim, cfg.d_model,\n", " has_projection=False,\n", " has_expert_whitener=True,\n", " has_text_unwhitener=True)),\n", " }\n", " model = BertensteinFusion(fcfg, experts)\n", "\n", " if not random_init:\n", " p = hf_hub_download(cfg.model_repo, cfg.ckpt_path)\n", " state = safetensors_load(p)\n", " keep = {k: v for k, v in state.items()\n", " if k.startswith(\"experts.text.\") or k.startswith(f\"experts.{cfg.expert}.\")\n", " or k.startswith(\"layers.\") or k in (\"pos\", \"out_norm.weight\", \"out_norm.bias\")}\n", " missing, unexpected = model.load_state_dict(keep, strict=False)\n", " assert not missing, f\"MISSING checkpoint keys (model would be partly random): {missing}\"\n", " print(f\"[model] loaded {len(keep)} tensors | unexpected={list(unexpected)}\")\n", " else:\n", " print(\"[model] RANDOM INIT (control arm)\")\n", "\n", " return model.to(cfg.device).eval()\n", "\n", "\n", "# ── layouts: joint absolute positions, so the split arm is position-matched ──\n", "def joint_layout(cfg: AuditConfig) -> Dict[str, int]:\n", " return {\"text\": 0, cfg.expert: 1 + cfg.text_tokens}\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, ds, cfg: AuditConfig, N: int, mode: str,\n", " perm: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " mode:\n", " 'joint' -> published: one pass, unmasked bidirectional attention (A0)\n", " 'blocked' -> one pass, same positions, cross-modal attention masked (A1)\n", " 'split' -> two independent passes, joint-layout positions (A2)\n", " perm: index permutation applied to the EXPERT side inside the joint pass (A4)\n", " \"\"\"\n", " lay = joint_layout(cfg)\n", " te_all, ee_all = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device)\n", " if perm is None:\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " else:\n", " pi = perm[i:j]\n", " eh = ds[pi.tolist()][cfg.expert_col].to(cfg.device).float()\n", "\n", " if mode in (\"joint\", \"blocked\"):\n", " out = model({\"text\": th, cfg.expert: eh}, {\"text\": tm},\n", " block_cross=(mode == \"blocked\"))\n", " te_all.append(out[\"text\"].float().cpu())\n", " ee_all.append(out[cfg.expert].float().cpu())\n", " elif mode == \"split\":\n", " o_t = model({\"text\": th}, {\"text\": tm}, layout_positions=lay)\n", " o_e = model({cfg.expert: eh}, {}, layout_positions=lay)\n", " te_all.append(o_t[\"text\"].float().cpu())\n", " ee_all.append(o_e[cfg.expert].float().cpu())\n", " else:\n", " raise ValueError(mode)\n", " return torch.cat(te_all)[:N], torch.cat(ee_all)[:N]\n", "\n", "\n", "@torch.no_grad()\n", "def procrustes_reference(model, ds, cfg: AuditConfig, N: int) -> Dict[str, float]:\n", " \"\"\"A5: pooled frozen features through the shipped aligner. No fusion layer.\"\"\"\n", " aligner = model.experts[cfg.expert].aligner\n", " tv, ev = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device).float()\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " denom = tm.sum(1, keepdim=True).clamp(min=1.0)\n", " tv.append(((th * tm.unsqueeze(-1)).sum(1) / denom).cpu())\n", " ev.append(aligner(eh).mean(1).cpu())\n", " tv = F.normalize(torch.cat(tv)[:N], dim=-1)\n", " ev = F.normalize(torch.cat(ev)[:N], dim=-1)\n", " m = retrieval_metrics(tv, ev, cv=False)\n", " m[\"note\"] = \"frozen features + shipped aligner, no fusion layer\"\n", " return m\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def line(t=\"\"):\n", " print(\"─\" * 78 if not t else f\"── {t} \" + \"─\" * max(0, 74 - len(t)))\n", "\n", "\n", "def fmt(m: Dict[str, Any]) -> str:\n", " s = (f\"R@1={m['r1']:.4f} R@5={m['r5']:.4f} cos_match={m['cos_match']:+.4f} \"\n", " f\"cos_rand={m['cos_rand']:+.5f} chance={m['chance_r1']:.5f}\")\n", " if \"cv_joint\" in m:\n", " s += f\" CV={m['cv_joint']:.4f} erank={m['erank_a']:.1f}/{m['erank_b']:.1f}\"\n", " return s\n", "\n", "\n", "def run(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " t0 = time.time()\n", " print(\"=\" * 78)\n", " print(\"BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\")\n", " print(\"=\" * 78)\n", " print(f\"device={cfg.device} split={cfg.split_dir} expert={cfg.expert}\")\n", "\n", " ds = fetch_split(cfg)\n", " N = min(cfg.eval_n, len(ds))\n", " if N < cfg.eval_n:\n", " print(f\"[warn] only {N} rows available; raise cfg.n_shards for the full {cfg.eval_n}\")\n", "\n", " model = build_model(cfg, random_init=False)\n", " rnd = build_model(cfg, random_init=True)\n", " R: Dict[str, Any] = {\"n\": N, \"config\": {\"n_shards\": cfg.n_shards, \"seed\": cfg.seed,\n", " \"split_dir\": cfg.split_dir}}\n", "\n", " R[\"query_gate\"] = query_degeneracy_gate(ds, cfg, N)\n", " if cfg.split_dir == \"image_coco\":\n", " print(\" NOTE: the model TRAINED on 85% of image_coco (train_test_split seed 42).\")\n", " print(\" Numbers on this split are a CONTAMINATED UPPER BOUND — which makes a\")\n", " print(\" failure here conclusive, and a success here uninformative.\")\n", "\n", " # ── A0 PUBLISHED ─────────────────────────────────────────────────────────\n", " line(\"A0 PUBLISHED — joint pass, unmasked bidirectional attention\")\n", " te, ee = encode(model, ds, cfg, N, \"joint\")\n", " R[\"A0_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A0_published\"]))\n", " d_r1 = abs(R[\"A0_published\"][\"r1\"] - cfg.ref_r1)\n", " d_cm = abs(R[\"A0_published\"][\"cos_match\"] - cfg.ref_cos_match)\n", " ok = (d_r1 <= cfg.reproduce_tol) and (d_cm <= cfg.reproduce_tol)\n", " R[\"A0_reproduction_gate\"] = {\"pass\": bool(ok), \"d_r1\": d_r1, \"d_cos_match\": d_cm,\n", " \"ref_r1\": cfg.ref_r1, \"ref_cos_match\": cfg.ref_cos_match}\n", " print(f\" GATE reproduce-published: {'PASS' if ok else 'FAIL'} \"\n", " f\"(Δr1={d_r1:.4f}, Δcos={d_cm:.4f})\")\n", " if not ok:\n", " print(\" !! A0 did not reproduce. Downstream arms are uninterpretable until it does.\")\n", "\n", " # ── A1 BLOCKED ───────────────────────────────────────────────────────────\n", " line(\"A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED\")\n", " te, ee = encode(model, ds, cfg, N, \"blocked\")\n", " R[\"A1_blocked\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A1_blocked\"]))\n", " R[\"A1_blocked_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A1_blocked_framefit\"]))\n", "\n", " # ── A2 SPLIT ─────────────────────────────────────────────────────────────\n", " line(\"A2 SPLIT — two independent forward passes (what retrieval means)\")\n", " te2, ee2 = encode(model, ds, cfg, N, \"split\")\n", " R[\"A2_split\"] = retrieval_metrics(te2, ee2, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A2_split\"]))\n", "\n", " # correctness invariant found at smoke: with n_layers=1, block-diagonal masking\n", " # and position-matched independent encoding are the SAME computation. If these\n", " # diverge, the mask or the layout offset is wrong and A1/A2 are uninterpretable.\n", " d_inv = max((te - te2).abs().max().item(), (ee - ee2).abs().max().item())\n", " R[\"A1_A2_identity_gate\"] = {\"pass\": bool(d_inv < 1e-4), \"max_abs_diff\": d_inv}\n", " print(f\" GATE A1==A2 (must hold at n_layers=1): \"\n", " f\"{'PASS' if d_inv < 1e-4 else 'FAIL'} max|Δ|={d_inv:.2e}\")\n", " te, ee = te2, ee2\n", " R[\"A2_split_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A2_split_framefit\"]))\n", "\n", " # ── A3 UNTRAINED ─────────────────────────────────────────────────────────\n", " line(\"A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"joint\")\n", " R[\"A3_untrained_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3_untrained_published\"]))\n", "\n", " line(\"A3b UNTRAINED — random init, SPLIT protocol (the floor)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"split\")\n", " R[\"A3b_untrained_split\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3b_untrained_split\"]))\n", "\n", " # ── A4 DERANGED ──────────────────────────────────────────────────────────\n", " line(\"A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index\")\n", " g = torch.Generator().manual_seed(cfg.seed + 991)\n", " perm = torch.randperm(N, generator=g)\n", " fixed = int((perm == torch.arange(N)).sum())\n", " te, ee = encode(model, ds, cfg, N, \"joint\", perm=perm)\n", " R[\"A4_deranged\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " R[\"A4_deranged\"][\"accidental_fixed_points\"] = fixed\n", " print(\" \", fmt(R[\"A4_deranged\"]), f\" (accidental fixed points: {fixed})\")\n", " print(\" If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\")\n", "\n", " # ── A5 PROCRUSTES REFERENCE ──────────────────────────────────────────────\n", " line(\"A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion\")\n", " R[\"A5_procrustes_reference\"] = procrustes_reference(model, ds, cfg, N)\n", " print(\" \", fmt(R[\"A5_procrustes_reference\"]))\n", " print(f\" published cos_after for image = {cfg.ref_procrustes_cos_after:.4f} \"\n", " f\"(the honest shared-structure ceiling)\")\n", "\n", " # ── CV NULL ──────────────────────────────────────────────────────────────\n", " line(\"CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null\")\n", " g2 = torch.Generator().manual_seed(cfg.seed + 7)\n", " nulls = {}\n", " for d in cfg.cv_null_dims:\n", " z = F.normalize(torch.randn(2 * min(N, 2048), d, generator=g2), dim=-1)\n", " nulls[d] = pentachoron_cv(z, n=cfg.cv_samples, seed=cfg.seed)\n", " print(f\" normalized randn, ZERO training, d={d:<5d}: CV = {nulls[d]:.4f}\")\n", " R[\"cv_null_by_dim\"] = nulls\n", " print(f\" published model CV (image_coco_test) : {cfg.ref_cv_joint:.4f}\")\n", " print(f\" measured A0 CV : \"\n", " f\"{R['A0_published'].get('cv_joint', float('nan')):.4f}\")\n", " print(f\" measured A0 effective rank (text/expert): \"\n", " f\"{R['A0_published'].get('erank_a', float('nan')):.1f} / \"\n", " f\"{R['A0_published'].get('erank_b', float('nan')):.1f} of nominal {cfg.d_model}\")\n", " print(\" READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\")\n", " print(\" claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\")\n", " print(\" if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\")\n", " print(\" clothes, not convergence to a universal constant.\")\n", "\n", " # ── SWEEP ────────────────────────────────────────────────────────────────\n", " line(\"SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N\")\n", " sweep = {}\n", " for mode, tag, mdl in ((\"joint\", \"A0_published\", model),\n", " (\"blocked\", \"A1_blocked\", model),\n", " (\"split\", \"A2_split\", model),\n", " (\"joint\", \"A3_untrained\", rnd)):\n", " rows = []\n", " for n in cfg.sweep_n:\n", " if n > N:\n", " continue\n", " a, b = encode(mdl, ds, cfg, n, mode)\n", " m = retrieval_metrics(a, b, cv=False)\n", " rows.append({\"N\": n, \"r1\": m[\"r1\"], \"cos_match\": m[\"cos_match\"],\n", " \"chance\": m[\"chance_r1\"]})\n", " sweep[tag] = rows\n", " R[\"sweep\"] = sweep\n", " hdr = \" N \" + \"\".join(f\"{t:>22s}\" for t in sweep.keys())\n", " print(hdr)\n", " for k, n in enumerate(cfg.sweep_n):\n", " if n > N:\n", " continue\n", " cells = []\n", " for tag in sweep:\n", " row = next((r for r in sweep[tag] if r[\"N\"] == n), None)\n", " cells.append(f\"{row['r1']:>22.4f}\" if row else f\"{'-':>22s}\")\n", " print(f\" {n:<7d}\" + \"\".join(cells))\n", "\n", " # ── VERDICT ──────────────────────────────────────────────────────────────\n", " line(\"VERDICT\")\n", " a0, a1, a2, a3, a4 = (R[\"A0_published\"][\"r1\"], R[\"A1_blocked\"][\"r1\"],\n", " R[\"A2_split\"][\"r1\"], R[\"A3_untrained_published\"][\"r1\"],\n", " R[\"A4_deranged\"][\"r1\"])\n", " leak_signals = {\n", " \"constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\":\n", " bool(R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"] and a0 >= 0.9),\n", " \"untrained_reproduces_headline (A3 >= 0.9*A0)\": bool(a3 >= 0.9 * a0),\n", " \"deranged_pairs_still_retrieve (A4 >= 0.9*A0)\": bool(a4 >= 0.9 * a0),\n", " \"blocking_cross_attention_collapses (A1 < 0.5*A0)\": bool(a1 < 0.5 * a0),\n", " \"independent_encoding_collapses (A2 < 0.5*A0)\": bool(a2 < 0.5 * a0),\n", " }\n", " R[\"leak_signals\"] = leak_signals\n", " for k, v in leak_signals.items():\n", " print(f\" [{'X' if v else ' '}] {k}\")\n", " n_leak = sum(leak_signals.values())\n", " R[\"n_leak_signals\"] = n_leak\n", " if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"]:\n", " print(\"\\n CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\")\n", " print(\" OVER-DETERMINED — a perfect model would collapse too. Re-run with\")\n", " print(\" cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\")\n", " if n_leak >= 3:\n", " R[\"verdict\"] = \"PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\"\n", " elif n_leak == 0:\n", " R[\"verdict\"] = \"NO LEAK DETECTED — the number survives every cut; escalate to seeds + a second split\"\n", " else:\n", " R[\"verdict\"] = f\"MIXED ({n_leak}/4 leak signals) — read the arms individually, do not summarize\"\n", " print(f\"\\n => {R['verdict']}\")\n", " print(f\"\\n[done] {time.time() - t0:.1f}s\")\n", "\n", " with open(\"bertenstein_audit_results.json\", \"w\") as f:\n", " json.dump(R, f, indent=2, default=float)\n", " print(\"[done] wrote bertenstein_audit_results.json\")\n", " return R\n", "\n", "\n", "def run_all(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " \"\"\"\n", " Run the bed once per split in cfg.run_splits, then print a cross-split summary.\n", " image_coco is the interpretable one; image_coco_test is kept only to reproduce\n", " the published headline and show what it actually measures.\n", " \"\"\"\n", " import dataclasses\n", " out: Dict[str, Any] = {}\n", " for sp in cfg.run_splits:\n", " c = dataclasses.replace(cfg, split_dir=sp)\n", " print(\"\\n\" + \"#\" * 78)\n", " print(f\"# SPLIT: {sp}\")\n", " print(\"#\" * 78)\n", " out[sp] = run(c)\n", "\n", " print(\"\\n\" + \"=\" * 78)\n", " print(\"CROSS-SPLIT SUMMARY\")\n", " print(\"=\" * 78)\n", " print(f\" {'split':18s}{'queries':>10s}{'A0':>9s}{'A1':>9s}{'A2':>9s}{'A3':>9s}{'A4':>9s}\")\n", " for sp, R in out.items():\n", " deg = \"DEGEN\" if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"] else \"ok\"\n", " print(f\" {sp:18s}{deg:>10s}\"\n", " f\"{R['A0_published']['r1']:>9.4f}{R['A1_blocked']['r1']:>9.4f}\"\n", " f\"{R['A2_split']['r1']:>9.4f}{R['A3_untrained_published']['r1']:>9.4f}\"\n", " f\"{R['A4_deranged']['r1']:>9.4f}\")\n", " print(\"\\n READ: image_coco is the interpretable row — real captions, erank ~38.\")\n", " print(\" If A0 is ~1.0 there while A1/A2 sit at chance, the leak is confirmed on\")\n", " print(\" VALID text, and the degenerate split was never load-bearing to the finding.\")\n", " with open(\"bertenstein_audit_all_splits.json\", \"w\") as f:\n", " json.dump(out, f, indent=2, default=float)\n", " print(\"\\n[done] wrote bertenstein_audit_all_splits.json\")\n", " return out\n", "\n", "\n", "# ── activation ───────────────────────────────────────────────────────────────\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run_all(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "2be169dcda784bed95c003fc0dd69f33", "6cef5bcfc0604ed5986e092e04be1099", "d1f5a87ba4ec474f8c2b27f1365c0e3b", "666250c24632403d9f4404cf86af83f7", "59ea27d77f3b4ce6adc8b438e8c57ef8", "526ba70b33ef4e5681b467f15c508f53", "919ff6448eb44683bdfe3cff12607be4", "7b608bd8345e4358a7bd655a6fb69dbe", "b08fb082d7a74c4f8d6cd8a90deef72a", "96f7ed5b3b474b1da00db37f5b2a4c9c", "1d807308717643659602c07d0dbb47f6", "2dc47f2acb2e4ad28d17fb6eb676bf55", "660cf277ee004c0bad800585f26e11f8", "13863dbdd91e40d5a827e3e7cc6d1421", "42c888885f174be882ea92d0dbd753bc", "fff07fd8ce4b4d9abb12a282fc8e7013", "e7cbe6a1f45042bd96ab3ed0c3991c3e", "6158fdc711694f699a41baa5a0335b2b", "a07028f8893a4228b694abb2668b3109", "14d1cfef38374056bfc1475c07a2c6cc", "32267ed39f45455486b8b9636b9b8f8a", "108aa2b9209b4d4c82a58cff994bd132", "10694c8dda864217aea3c0b49275770f", "629c2849e86844bb8597a5b4a6b4bbab", "03cd9cd079eb4ba2aed4b70c985e7ede", "e9af88638ef94627803e78f63fa1ed4d", "b4f5bd4c11124a449354b4ece3fbc15e", "aaaae2375b294887bc92eef49a32d81b", "f69409b5c26c486c9b46d7741efc744b", "82047834085546b884973d4b9f7e8062", "ede58e036833429d8ef2b3a00dd12644", "8d99aa8be6d84570b4503a2ed91c3abb", "3055a2a135e84d55aab97d8fb18798a7", "d97ecdb0db134d9bbb268da4087ccab7", "37f8cd5e279e4bdcbe0cc165d6fb8861", "739b66f3978b45f888982a0470fe0fbf", "8e8ce9e8de0545eba590f084d6523dc4", "9d1357e455ab4bbca7794e101e9df333", "a077354f4ac9467ab184a0a9ebd004b4", "dd00b7b3b2b14a88b8f46239b94c8e67", "9e85d20cf0c84692a2232dac6b02314a", "3a6ca89d96004ca98c8da99d3e7e3535", "250fa7bb3f37427f93105e528c7d88c8", "adf16da128cc40278919444372f632d7", "c5c41f90d3d7444e800f7a80d24e66d7", "a98ff6a45c5946e78c857cc9d0275ce8", "507f7878775e4452b3cb53b37a84d94b", "f82b10070659466bb57986b096b5fb24", "385ca0f711914cc7b0145970115d8df1", "6e3bdded28f9497bac69ef0dc0aba819", "ba91f0b5e8e24c7f82655c0b246819ee", "c8a60784814342bfad3aca3330f64540", "43a8c93f236044d2aad59c0aa4f47b2b", "9d04db4d81e64c2c9cab024dc9b3bb90", "8c53df89589243a8b5a5470eb54864c6", "4d30f55f197c4bf8ae5cb3fc5b0c9535", "53f2f4acbf53495e9ea7654a8df526c8", "6d3429e87a714dc2ad5605ee85bf6617", "9dfce37ddc354e549ef31c8e00803415", "5e76b2a6674a4d0e9196a6c700ab91d9", "ef41e8770cf1480cb87fd36d1e66ecd8", "ec7ddac4432049af9c37f12847ad3cc4", "d18be49a53ce4e2fb75243d292f9ff06", "00e1d9abf446468eb88850a28ff841b9", "5bcedc6a873d4379b364f2ddfd733fb9", "d53999715cb446a3b1936d473d257e9f", "bee6809751b04825985b95e185fe1770", "0541c6327cf341f8bdf59f91414747f8", "afa91d728da44ce7bc0011fafd7d1e8e", "84c8ad0abd3d4de880446e40a13b73a7", "9309b71ff877494abce58dbf9b4e5b3e", "adb7fd89f9744354907b3e5cc519f4ae", "2ee51b5241c84738a3272dad915c9f9d", "57974b85be894396b6f306a5ecbafc70", "f003217ed6f94192a7be64ee37f271e9", "1b0e8eb784534662a271eb8b5f4386d0", "df44ee064e0a42c3933a56ae85431251", "d713595b108b4217ac0b5a4c70efc78c", "69c56333263446a48447d028460b5995", "cc5c562a4d1f4e62a09bf00502c817c2", "9fde8dffa7a442b1955d1889a194f15f", "1f7e9408afeb41ee91e28955bdf2d870", "35605d4114a54a0f8e226dd764d18860", "ada4d6038c474fd4bd019f73f254fb33", "692f51108feb4cc59b360d2806c89fbb", "b7f31cfef42e40e8b6494c168750cb86", "8d05bbcfc1fd420caf7b908ea5e6c9fd", "cbfb23833e6847d5ba1167ee55ea92e7", "13267fbc70b34fa3b37d66324dfa41e9", "a4626e48028147c7918fe1f8d97eda58", "44c0d0ab624d4261b6fcf450459dd264", "095e648c94c44cd48dedd6cf2e557e89", "d9906743ab054217b01ce936e9c3693c", "0855211769184d29a0238042e979e53f", "c6fffc549e5441c7ab03a1c0731c9e7f", "5bc5f8fffbd542ffb829d2c3de3608e6", "6bc1305923854067aecfafadf5933fc9", "b7789eba70a74d8cad2b8eeafc5db641", "e32f1b4939d84a14aa971256ba4b6dbd", "e347449af09648689e1add40d78b3ccd", "5252e183c46d48ee88e7dcc4ecc5c7d8", "435ff375eabd4a5abe7291692d3e44cf", "f428550922754206af33a2587afa81b3", "4be6e023f47d446da439d2ce71e12d67", "ae9d8749b975460c81cc9ac98e85603e", "abcece77076c4867b5f7b88e31056e78", "cfc0e53c2d5f47db8983717f5518a0cc", "cf71b830deef463a8f162b6f1346dbcc", "a13d1035131c4c63a5467b2c36683123", "936f5a7fefc94243af1411dc2b0730e9", "6ff9835724b14dd5b67ee5611c785cd1", "6966f1a14f7a4078ad596d7ec492346d", "15dbb054adcd49ff8a60d5b833076576", "68f23a42fb564770ada3db288c4b4a14", "de23165cf633487994d33a277246c12a", "4d1bdb2c89194450a54d691b5a945c0e", "0843debc4b2447c9b9d3015b4cbb64ce", "68e19300f6414f13bca09c566cabb618", "9e8499bb893e42bcbc51f83c36fbcdb9", "342bd57d5f374c57891850209f078068", "a24c80cb9b44487f8c43b219432a3a22", "5bd9f5e388e340d4b1c7c173d78e766f", "2dc426dc7c3245acbb74363938663eb0", "bf4e46a399c74a16931ea8c54b4fde36", "1864ceeaaa894430be7a214bb4d140fa", "7d7f4d26d2b4489e950f9494e1bc465f", "8492ba87db2b4843a5a5a9687c4a8577", "bbe49a55c6fd4129ae020df663f7ad82", "f2309485910d419fa8f91b7648a41984", "3a0640f463684891aa386867bc53d554", "301002f25b974fa28f427d1290438b8a", "ab1bb1ef2d5b419db63c5cbfaf770833" ] }, "id": "sLQ8jV4kmYhk", "outputId": "63733ad8-ba1f-4150-fe02-0e6249a107d8" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "##############################################################################\n", "# SPLIT: image_coco\n", "##############################################################################\n", "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco expert=image\n", "[data] pulling 6 shard(s) of AbstractPhil/bertenstein-v1/image_coco (~497MB each) ...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00000-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "2be169dcda784bed95c003fc0dd69f33" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00000-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "2dc47f2acb2e4ad28d17fb6eb676bf55" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 0: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00001-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "10694c8dda864217aea3c0b49275770f" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00001-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d97ecdb0db134d9bbb268da4087ccab7" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 1: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00002-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c5c41f90d3d7444e800f7a80d24e66d7" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00002-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "4d30f55f197c4bf8ae5cb3fc5b0c9535" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 2: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00003-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "bee6809751b04825985b95e185fe1770" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00003-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d713595b108b4217ac0b5a4c70efc78c" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 3: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00004-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "13267fbc70b34fa3b37d66324dfa41e9" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00004-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e347449af09648689e1add40d78b3ccd" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 4: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00005-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6ff9835724b14dd5b67ee5611c785cd1" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00005-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5bd9f5e388e340d4b1c7c173d78e766f" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 5: 827 rows\n", "[data] 4962 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4962 rows available; raise cfg.n_shards for the full 5000\n", "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── QUERY DEGENERACY GATE — do the queries vary at all? ───────────────────────\n", " tokens/row: 9..28 (16 distinct lengths)\n", " pooled-text pairwise cos: mean 0.7679 min 0.4332\n", " pooled-text erank 37.82 | pooled-image erank 29.53\n", " GATE PASS: queries vary; retrieval numbers are interpretable.\n", " NOTE: the model TRAINED on 85% of image_coco (train_test_split seed 42).\n", " Numbers on this split are a CONTAMINATED UPPER BOUND — which makes a\n", " failure here conclusive, and a success here uninformative.\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9838 cos_rand=+0.00735 chance=0.00020 CV=0.1753 erank=19.7/19.7\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0103)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0007 R@5=0.0020 cos_match=+0.0302 cos_rand=-0.00486 chance=0.00020 CV=0.5493 erank=2.9/19.7\n", " frame-fit rider: {\"fit_pairs\": 2481, \"r1_all_rotated\": 0.005038291041273624, \"cos_match_all_rotated\": 0.2246374487876892, \"r1_heldout_rotated\": 0.0072551388293504715, \"cos_match_heldout_rotated\": 0.21771758794784546, \"n_heldout\": 2481}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0007 R@5=0.0020 cos_match=+0.0302 cos_rand=-0.00486 chance=0.00020 CV=0.5493 erank=2.9/19.7\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=1.10e-07\n", " frame-fit rider: {\"fit_pairs\": 2481, \"r1_all_rotated\": 0.005038291041273624, \"cos_match_all_rotated\": 0.22463741898536682, \"r1_heldout_rotated\": 0.0072551388293504715, \"cos_match_heldout_rotated\": 0.21771757304668427, \"n_heldout\": 2481}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0011 cos_match=+0.0181 cos_rand=+0.01560 chance=0.00020 CV=0.1939 erank=35.3/43.7\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0000 R@5=0.0009 cos_match=+0.0113 cos_rand=+0.01117 chance=0.00020 CV=0.4312 erank=28.6/33.7\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9838 cos_rand=+0.00735 chance=0.00020 CV=0.1670 erank=19.7/19.7 (accidental fixed points: 0)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.2328 R@5=0.3827 cos_match=+0.2689 cos_rand=+0.00024 chance=0.00020\n", " published cos_after for image = 0.4107 (the honest shared-structure ceiling)\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1753\n", " measured A0 effective rank (text/expert): 19.7 / 19.7 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0391 0.0391 0.0000\n", " 128 1.0000 0.0273 0.0273 0.0078\n", " 256 1.0000 0.0098 0.0098 0.0039\n", " 512 1.0000 0.0029 0.0029 0.0010\n", " 1024 1.0000 0.0015 0.0015 0.0010\n", " 2048 1.0000 0.0010 0.0010 0.0005\n", " 4096 1.0000 0.0010 0.0010 0.0002\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [ ] constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 84.5s\n", "[done] wrote bertenstein_audit_results.json\n", "\n", "##############################################################################\n", "# SPLIT: image_coco_test\n", "##############################################################################\n", "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco_test expert=image\n", "[data] pulling 6 shard(s) of AbstractPhil/bertenstein-v1/image_coco_test (~497MB each) ...\n", " shard 0: 833 rows\n", " shard 1: 833 rows\n", " shard 2: 833 rows\n", " shard 3: 833 rows\n", " shard 4: 833 rows\n", " shard 5: 833 rows\n", "[data] 4998 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4998 rows available; raise cfg.n_shards for the full 5000\n", "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── QUERY DEGENERACY GATE — do the queries vary at all? ───────────────────────\n", " tokens/row: 3..3 (1 distinct lengths)\n", " pooled-text pairwise cos: mean 1.0000 min 1.0000\n", " pooled-text erank 1.00 | pooled-image erank 48.92\n", " *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\n", " Every retrieval number on this split is uninterpretable AS ALIGNMENT.\n", " A constant query cannot rank a gallery. If A0 still scores high, that\n", " is a direct proof the score does not come from the query modality.\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1961 erank=20.5/20.4\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0000)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0001 R@5=0.0007 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.3156 erank=1.0/20.6\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0004001600609626621, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0002 R@5=0.0008 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.4199 erank=1.0/20.6\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=6.71e-08\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0006002400914439932, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0015 cos_match=-0.0177 cos_rand=-0.01890 chance=0.00020 CV=0.2661 erank=41.8/44.3\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0002 R@5=0.0011 cos_match=-0.0060 cos_rand=-0.00601 chance=0.00020 CV=0.1381 erank=1.1/51.8\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1721 erank=20.5/20.4 (accidental fixed points: 1)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.0002 R@5=0.0010 cos_match=+0.0079 cos_rand=+0.00788 chance=0.00020\n", " published cos_after for image = 0.4107 (the honest shared-structure ceiling)\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1961\n", " measured A0 effective rank (text/expert): 20.5 / 20.4 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0156 0.0156 0.0234\n", " 128 1.0000 0.0078 0.0078 0.0117\n", " 256 1.0000 0.0039 0.0039 0.0000\n", " 512 1.0000 0.0020 0.0020 0.0010\n", " 1024 1.0000 0.0010 0.0010 0.0010\n", " 2048 1.0000 0.0005 0.0005 0.0007\n", " 4096 1.0000 0.0002 0.0002 0.0004\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [X] constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", "\n", " CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\n", " OVER-DETERMINED — a perfect model would collapse too. Re-run with\n", " cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 20.8s\n", "[done] wrote bertenstein_audit_results.json\n", "\n", "==============================================================================\n", "CROSS-SPLIT SUMMARY\n", "==============================================================================\n", " split queries A0 A1 A2 A3 A4\n", " image_coco ok 1.0000 0.0007 0.0007 0.0003 1.0000\n", " image_coco_test DEGEN 1.0000 0.0001 0.0002 0.0003 1.0000\n", "\n", " READ: image_coco is the interpretable row — real captions, erank ~38.\n", " If A0 is ~1.0 there while A1/A2 sit at chance, the leak is confirmed on\n", " VALID text, and the degenerate split was never load-bearing to the finding.\n", "\n", "[done] wrote bertenstein_audit_all_splits.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ══════════════════════════════════════════════════════════════════════════════\n", "# BERTENSTEIN R@1 AUDIT — single Colab cell\n", "#\n", "# Question under test: is GEOLIP-Bertenstein's R@1 = 1.0000 a measurement of\n", "# cross-modal alignment, or a measurement of the evaluation protocol?\n", "#\n", "# The published eval (cell2_prototype_model_trainer_v1.py :748 eval_direct)\n", "# encodes text AND the expert modality in ONE joint forward pass through an\n", "# UNMASKED bidirectional fusion layer, reads both special tokens out\n", "# of that single sequence, and calls the cosine between them \"retrieval\".\n", "# Every diagonal entry sim[i,i] is therefore two readout heads of the same\n", "# fused encoding of item i. The off-diagonal sim[i,j] compares readouts from\n", "# two forward passes that never saw each other.\n", "#\n", "# This bed holds everything else constant and cuts exactly one wire at a time.\n", "#\n", "# ARMS (all on the shipped checkpoints/final weights, image<->text,\n", "# image_coco_test = the exact split that produced R@1 = 1.0000)\n", "# A0 PUBLISHED joint sequence, full bidirectional attention. Reproduce.\n", "# A1 BLOCKED identical sequence + positions; cross-modal attention MASKED.\n", "# The single-variable cut. Nothing else changes.\n", "# A2 SPLIT two fully independent forward passes. What \"retrieval\" means.\n", "# A3 UNTRAINED random init, PUBLISHED protocol. Does the metric need weights?\n", "# A3b UNTRAINED random init, SPLIT protocol. The floor.\n", "# A4 DERANGED joint pass, text[i] fused with image[perm[i]]. Ground truth\n", "# stays the FORWARD-PASS index. If R@1 holds, the diagonal is\n", "# pass-identity and carries zero semantic content.\n", "# A5 PROCRUSTES pooled frozen features through the shipped aligner, no fusion.\n", "# The honest ceiling (results.json reports cos_after 0.4107).\n", "# SWEEP gallery size N. A leak is N-invariant; retrieval decays ~log N.\n", "# FRAME FIT fp64 orthogonal Procrustes post-fit on the collapsed arms\n", "# before verdicting — the standing dist-campaign law, so we do\n", "# not publish a false floor the way affinity_kl nearly did.\n", "# CV NULL pentachoron CV of untrained normalized randn, printed beside\n", "# the model's CV, because the \"0.20 universal band\" claim needs\n", "# its null on the same line.\n", "#\n", "# Colab-cell-safe: no argparse, no __file__, paste-ahead-safe, globals() guarded.\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "import os, sys, json, math, time, subprocess\n", "from dataclasses import dataclass, field\n", "from typing import Any, Dict, List, Optional, Tuple\n", "\n", "# ── deps (idempotent; quiet) ─────────────────────────────────────────────────\n", "for _pkg, _imp in [(\"datasets\", \"datasets\"), (\"safetensors\", \"safetensors\"),\n", " (\"huggingface_hub\", \"huggingface_hub\")]:\n", " try:\n", " __import__(_imp)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _pkg], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from datasets import Dataset as HFDataset, concatenate_datasets\n", "from huggingface_hub import hf_hub_download\n", "from safetensors.torch import load_file as safetensors_load\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class AuditConfig:\n", " # sources\n", " model_repo: str = \"AbstractPhil/geolip-bertenstein\"\n", " cache_repo: str = \"AbstractPhil/bertenstein-v1\"\n", " ckpt_path: str = \"checkpoints/final/model.safetensors\"\n", " # WHICH SPLITS THE BED RUNS (run_all iterates these in order).\n", " # image_coco = COCO-Caption \"val\" -> REAL captions (9-28 tokens, erank 37.8).\n", " # THE VALID ONE. Default. NOTE: the model trained on 85% of this\n", " # (train_test_split seed 42), so it is a contaminated UPPER BOUND\n", " # -- a failure here is conclusive, a success is uninformative.\n", " # image_coco_test = COCO-Caption \"test\". COCO 2014 test annotations are withheld;\n", " # upstream encodes that as answer=['None'], id=-1. extract_first_text\n", " # returned the STRING \"None\", BERT encoded it, and it was written to\n", " # Arrow at commit 3e8765c9 (2026-03-07 17:58). Every row identical:\n", " # 3 tokens, pooled-text erank 1.00, pairwise cos 1.0000.\n", " # The flagship \"40K test / R@1 1.0000\" row was scored on this.\n", " # Kept ONLY to reproduce that number and show what it measures.\n", " split_dir: str = \"image_coco\"\n", " run_splits: Tuple[str, ...] = (\"image_coco\", \"image_coco_test\")\n", " n_shards: int = 6 # ~830 rows/shard, ~494MB/shard\n", " shard_total: int = 49 # both splits are data-NNNNN-of-00049.arrow\n", " shard_offset: int = 0 # first shard to pull\n", " # A5 OUT-OF-SAMPLE. The shipped aligner was fit on a DETERMINISTIC PREFIX:\n", " # cell2 run(): n_align = min(CFG.align_samples=5000, len(ds)); ds[:n_align]\n", " # -> means, BOTH whiteners and the rotation come from rows 0..4999, and ship as\n", " # buffers in model.safetensors. Shard 6 spans rows 4962-5788 (straddles the\n", " # boundary); shard 7 starts at 5789 and is strictly outside. Any A5 measured on\n", " # shards 0-5 is FIT-ON-TEST: the rotation is the argmax of the statistic being\n", " # reported, on the very rows it was fit to.\n", " a5_clean_offset: int = 7\n", " a5_clean_shards: int = 6 # matched N against the in-sample read\n", " hub_probe_n: int = 1024 # items for the hub-stability probe\n", " expert: str = \"image\"\n", " expert_col: str = \"image_hidden\"\n", " expert_mask_col: Optional[str] = None # image_coco_test ships no image mask\n", "\n", " # architecture (must reproduce checkpoint keys exactly)\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", " text_tokens: int = 32 # text expert: needs_pooling=False, n_pooled=32\n", " image_pooled: int = 16 # image expert: needs_pooling=True, n_pooled=16\n", " image_input_dim: int = 1024 # DINOv2-large, 1024->1024 direct (no projection)\n", "\n", " # protocol\n", " batch_size: int = 128\n", " eval_n: int = 5000 # published n for image_coco_test\n", " sweep_n: Tuple[int, ...] = (64, 128, 256, 512, 1024, 2048, 4096)\n", " frame_fit_pairs: int = 2500 # standing dist-campaign rider\n", " cv_samples: int = 200\n", " cv_null_dims: Tuple[int, ...] = (16, 64, 256, 1024)\n", " seed: int = 0\n", " device: str = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", " # published reference values (bertenstein_results.json -> test.image_coco_test)\n", " ref_r1: float = 1.0\n", " ref_cos_match: float = 0.9735139608383179\n", " ref_cos_rand: float = 0.05161707841568314\n", " ref_cv_joint: float = 0.2007410876349909\n", " ref_procrustes_cos_after: float = 0.4106820225715637\n", " reproduce_tol: float = 0.02 # A0 gate; wider than fp noise, tight enough\n", "\n", "\n", "CFG = AuditConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# MODEL — key-compatible re-declaration of the shipped classes,\n", "# extended ONLY with an optional attn_mask pathway (arm A1).\n", "# State-dict keys are unchanged: layers.0.*, experts..*, pos, out_norm.*\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class FusionConfig:\n", " d_model: int = 1024\n", " n_heads: int = 16\n", " n_layers: int = 1\n", " d_ff: int = 2048\n", " dropout: float = 0.1\n", " max_seq_len: int = 512\n", "\n", "\n", "class ProcrustesAligner(nn.Module):\n", " \"\"\"Buffer-only module. Shapes taken from the checkpoint; values loaded from it.\"\"\"\n", "\n", " def __init__(self, d_expert: int, d_text: int, has_projection: bool,\n", " has_expert_whitener: bool, has_text_unwhitener: bool):\n", " super().__init__()\n", " self.register_buffer(\"expert_mean\", torch.zeros(d_expert))\n", " self.register_buffer(\"rotation\", torch.eye(d_text))\n", " if has_projection:\n", " self.register_buffer(\"projection\", torch.zeros(d_expert, d_text))\n", " else:\n", " self.projection = None\n", " if has_expert_whitener:\n", " self.register_buffer(\"expert_whitener\", torch.eye(d_text))\n", " else:\n", " self.expert_whitener = None\n", " if has_text_unwhitener:\n", " self.register_buffer(\"text_unwhitener\", torch.eye(d_text))\n", " else:\n", " self.text_unwhitener = None\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " x = x - self.expert_mean\n", " if self.projection is not None:\n", " x = x @ self.projection\n", " if self.expert_whitener is not None:\n", " x = x @ self.expert_whitener\n", " x = x @ self.rotation.T\n", " if self.text_unwhitener is not None:\n", " x = x @ self.text_unwhitener\n", " return x\n", "\n", "\n", "class FusionLayer(nn.Module):\n", " def __init__(self, c: FusionConfig):\n", " super().__init__()\n", " self.attn = nn.MultiheadAttention(c.d_model, c.n_heads, dropout=c.dropout,\n", " batch_first=True)\n", " self.ff = nn.Sequential(\n", " nn.Linear(c.d_model, c.d_ff),\n", " nn.GELU(),\n", " nn.Linear(c.d_ff, c.d_model),\n", " nn.Dropout(c.dropout),\n", " )\n", " self.n1 = nn.LayerNorm(c.d_model)\n", " self.n2 = nn.LayerNorm(c.d_model)\n", " self.drop = nn.Dropout(c.dropout)\n", "\n", " def forward(self, x, kpm=None, attn_mask=None):\n", " h = self.n1(x)\n", " a, _ = self.attn(h, h, h, key_padding_mask=kpm, attn_mask=attn_mask)\n", " x = x + self.drop(a)\n", " x = x + self.ff(self.n2(x))\n", " return x\n", "\n", "\n", "class ExpertModule(nn.Module):\n", " def __init__(self, name, input_dim, n_pooled, d_model=1024, n_heads=16,\n", " needs_pooling=True, aligner=None):\n", " super().__init__()\n", " self.name = name\n", " self.n_pooled = n_pooled\n", " self.needs_pooling = needs_pooling\n", " self.aligner = aligner\n", " proj_input_dim = d_model if aligner is not None else input_dim\n", " self.input_proj = nn.Linear(proj_input_dim, d_model)\n", " if needs_pooling:\n", " self.pool_queries = nn.Parameter(torch.randn(1, n_pooled, d_model) * 0.02)\n", " self.pool_attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True)\n", " self.pool_norm = nn.LayerNorm(d_model)\n", " self.special_token = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.modality_emb = nn.Parameter(torch.randn(1, 1, d_model) * 0.02)\n", " self.output_head = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.Linear(d_model, d_model),\n", " )\n", "\n", " def prepare_tokens(self, hidden, mask=None):\n", " B = hidden.shape[0]\n", " if self.aligner is not None:\n", " hidden = self.aligner(hidden)\n", " proj = self.input_proj(hidden)\n", " if self.needs_pooling:\n", " q = self.pool_queries.expand(B, -1, -1)\n", " kpm = (~mask.bool()) if mask is not None else None\n", " p, _ = self.pool_attn(q, proj, proj, key_padding_mask=kpm)\n", " tokens = self.pool_norm(p + q)\n", " else:\n", " tokens = proj[:, : self.n_pooled]\n", " return tokens + self.modality_emb\n", "\n", " def get_special(self, B):\n", " return self.special_token.expand(B, -1, -1)\n", "\n", " def extract(self, h):\n", " return F.normalize(self.output_head(h), dim=-1)\n", "\n", "\n", "class BertensteinFusion(nn.Module):\n", " \"\"\"\n", " Verbatim-compatible with the shipped model, plus `block_cross`:\n", " block_cross=False -> published behaviour (unmasked bidirectional attention)\n", " block_cross=True -> identical sequence, identical positions, cross-modal\n", " attention forbidden. Arm A1.\n", " `layout_positions` reproduces the JOINT absolute positions even when only one\n", " modality is present, so arm A2 is not confounded by positional-encoding shift.\n", " \"\"\"\n", "\n", " def __init__(self, fusion_cfg: FusionConfig, experts_dict: Dict[str, ExpertModule]):\n", " super().__init__()\n", " self.cfg = fusion_cfg\n", " self.expert_order = list(experts_dict.keys())\n", " self.experts = nn.ModuleDict(experts_dict)\n", " self.pos = nn.Parameter(torch.randn(1, fusion_cfg.max_seq_len, fusion_cfg.d_model) * 0.02)\n", " self.layers = nn.ModuleList([FusionLayer(fusion_cfg) for _ in range(fusion_cfg.n_layers)])\n", " self.out_norm = nn.LayerNorm(fusion_cfg.d_model)\n", "\n", " def forward(self, expert_hidden, expert_masks=None, block_cross=False,\n", " layout_positions: Optional[Dict[str, int]] = None):\n", " active = [n for n in self.expert_order if n in expert_hidden]\n", " B = next(iter(expert_hidden.values())).shape[0]\n", " expert_masks = expert_masks or {}\n", "\n", " seq_parts, mask_parts, special_pos, spans = [], [], {}, {}\n", " pos = 0\n", " for name in active:\n", " exp = self.experts[name]\n", " h = expert_hidden[name]\n", " m = expert_masks.get(name, None)\n", " start = pos\n", " seq_parts.append(exp.get_special(B))\n", " mask_parts.append(torch.zeros(B, 1, device=h.device, dtype=torch.bool))\n", " special_pos[name] = pos\n", " pos += 1\n", " tokens = exp.prepare_tokens(h, mask=m)\n", " nt = tokens.shape[1]\n", " seq_parts.append(tokens)\n", " mask_parts.append(torch.zeros(B, nt, device=h.device, dtype=torch.bool))\n", " pos += nt\n", " spans[name] = (start, pos)\n", "\n", " seq = torch.cat(seq_parts, 1)\n", " kpm = torch.cat(mask_parts, 1)\n", " L = seq.shape[1]\n", "\n", " # positional encoding: joint-layout absolute positions when requested\n", " if layout_positions is not None:\n", " idx = torch.empty(L, dtype=torch.long, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " base = layout_positions[name]\n", " idx[s:e] = torch.arange(base, base + (e - s), device=seq.device)\n", " seq = seq + self.pos[0, idx].unsqueeze(0)\n", " else:\n", " seq = seq + self.pos[:, :L]\n", "\n", " attn_mask = None\n", " if block_cross and len(active) > 1:\n", " attn_mask = torch.ones(L, L, dtype=torch.bool, device=seq.device)\n", " for name, (s, e) in spans.items():\n", " attn_mask[s:e, s:e] = False # within-modality allowed, cross forbidden\n", "\n", " for layer in self.layers:\n", " seq = layer(seq, kpm=kpm, attn_mask=attn_mask)\n", "\n", " seq = self.out_norm(seq)\n", " return {n: self.experts[n].extract(seq[:, special_pos[n]]) for n in active}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# METRICS\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " diff = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (diff * diff).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float64)\n", " cm[:, 0, 1:] = 1\n", " cm[:, 1:, 0] = 1\n", " cm[:, 1:, 1:] = d2.double()\n", " s = (-1.0) ** V\n", " f = math.factorial(V - 1)\n", " return s / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "def pentachoron_cv(emb, n=200, seed=0):\n", " N = emb.shape[0]\n", " if N < 5:\n", " return 0.0\n", " g = torch.Generator().manual_seed(seed)\n", " vs = []\n", " for _ in range(n):\n", " idx = torch.randperm(N, generator=g)[:5]\n", " v2 = cayley_menger_vol2(emb[idx].unsqueeze(0))\n", " v = float(torch.sqrt(F.relu(v2[0])).item())\n", " if v > 0:\n", " vs.append(v)\n", " if len(vs) < 10:\n", " return 0.0\n", " a = np.array(vs, dtype=np.float64)\n", " return float(a.std() / max(a.mean(), 1e-12))\n", "\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " \"\"\"\n", " Participation ratio of the singular-value spectrum: (sum s^2)^2 / sum s^4.\n", " CV is dimension-dependent and therefore a bad standalone gauge (see CV NULL);\n", " erank says directly how many directions the embedding actually uses.\n", " \"\"\"\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s = torch.linalg.svdvals(xc)\n", " s2 = s ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def query_degeneracy_gate(ds, cfg: AuditConfig, N: int) -> Dict[str, Any]:\n", " \"\"\"\n", " RUNS BEFORE ANY RETRIEVAL ARM. Measures whether the query set actually varies.\n", "\n", " A retrieval score is meaningless if the queries are identical: a constant query\n", " cannot rank a gallery, so R@1 cannot exceed chance for ANY honest model. Scoring\n", " high with degenerate queries is therefore proof that the score is not coming from\n", " the query modality at all.\n", "\n", " This gate exists because the shipped flagship row (test.image_coco_test, R@1\n", " 1.0000, 'text<->image 40K test') was scored on COCO-Caption's *test* split, whose\n", " caption annotations are withheld. cell1_prepare_data.extract_first_text() returns\n", " \"\" / a constant when no caption field is present, masked_text_tokenize pads it to\n", " max_length, and every row ends up the same 3-token sequence.\n", " \"\"\"\n", " n = min(N, 512)\n", " sl = ds[0:n]\n", " th = sl[\"text_hidden\"].float()\n", " tm = sl[\"text_mask\"]\n", " tok_counts = tm.sum(1)\n", " m = tm.float()\n", " den = m.sum(1, keepdim=True).clamp(min=1.0)\n", " pv = (th * m.unsqueeze(-1)).sum(1) / den\n", " pvn = F.normalize(pv, dim=-1)\n", " sim = pvn @ pvn.T\n", " off = sim[~torch.eye(n, dtype=torch.bool)]\n", " er = effective_rank(pv)\n", "\n", " ih = sl[cfg.expert_col].float().mean(1)\n", " er_img = effective_rank(ih)\n", "\n", " degenerate = bool(er < 2.0 or off.mean().item() > 0.999)\n", " out = {\n", " \"n_probed\": n,\n", " \"tokens_per_row_min\": int(tok_counts.min()),\n", " \"tokens_per_row_max\": int(tok_counts.max()),\n", " \"distinct_token_counts\": int(torch.unique(tok_counts).numel()),\n", " \"pooled_text_pairwise_cos_mean\": off.mean().item(),\n", " \"pooled_text_pairwise_cos_min\": off.min().item(),\n", " \"pooled_text_erank\": er,\n", " \"pooled_image_erank\": er_img,\n", " \"QUERY_SET_DEGENERATE\": degenerate,\n", " }\n", " line(\"QUERY DEGENERACY GATE — do the queries vary at all?\")\n", " print(f\" tokens/row: {out['tokens_per_row_min']}..{out['tokens_per_row_max']} \"\n", " f\"({out['distinct_token_counts']} distinct lengths)\")\n", " print(f\" pooled-text pairwise cos: mean {off.mean():.4f} min {off.min():.4f}\")\n", " print(f\" pooled-text erank {er:.2f} | pooled-image erank {er_img:.2f}\")\n", " if degenerate:\n", " print(\" *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\")\n", " print(\" Every retrieval number on this split is uninterpretable AS ALIGNMENT.\")\n", " print(\" A constant query cannot rank a gallery. If A0 still scores high, that\")\n", " print(\" is a direct proof the score does not come from the query modality.\")\n", " else:\n", " print(\" GATE PASS: queries vary; retrieval numbers are interpretable.\")\n", " return out\n", "\n", "\n", "@torch.no_grad()\n", "def _encode_pairs(model, ds, cfg: AuditConfig, t_idx, e_idx):\n", " \"\"\"Encode arbitrary (text_row, expert_row) couplings through the joint pass.\"\"\"\n", " te, ee = [], []\n", " for i in range(0, len(t_idx), cfg.batch_size):\n", " ti = t_idx[i:i + cfg.batch_size].tolist()\n", " ei = e_idx[i:i + cfg.batch_size].tolist()\n", " sl_t, sl_e = ds[ti], ds[ei]\n", " th = sl_t[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl_t[\"text_mask\"].to(cfg.device)\n", " eh = sl_e[cfg.expert_col].to(cfg.device).float()\n", " out = model({\"text\": th, cfg.expert: eh}, {\"text\": tm})\n", " te.append(out[\"text\"].float().cpu())\n", " ee.append(out[cfg.expert].float().cpu())\n", " return torch.cat(te), torch.cat(ee)\n", "\n", "\n", "@torch.no_grad()\n", "def hub_stability_probe(model, ds, cfg: AuditConfig, N: int) -> Dict[str, Any]:\n", " \"\"\"\n", " THE COLLECTIVE'S CENTRAL CLAIM, tested directly.\n", "\n", " The card says this is not a MoE but a collective of alignments through \"direct and\n", " INDIRECT association\": train only text<->X pairs and expect X<->Y alignment to\n", " emerge transitively through the shared text hub. That is the only thing separating\n", " this from four independent bilateral aligners.\n", "\n", " Transitivity has a hard precondition: the hub must be STABLE. Audio-context text\n", " and image-context text must be the same vector, or alignments to them cannot\n", " compose. The shipped cross_expert_probe cannot test this — its four modalities come\n", " from four disjoint corpora, so row i of audio has no relation to row i of image and\n", " a PERFECT collective would also read diag == full == 0 there.\n", "\n", " This probe holds one side fixed and swaps the other:\n", " T_ii = readout from (text_i, expert_i) anchor\n", " T_ij = readout from (text_i, expert_perm(i)) SAME text, OTHER expert\n", " T_ji = readout from (text_perm(i), expert_i) OTHER text, SAME expert\n", "\n", " A text-only hub => cos(T_ii, T_ij) ~ 1.0 and cos(T_ii, T_ji) ~ floor.\n", " If cos(T_ii, T_ij) < cos(T_ii, T_ji), the \"text\" embedding is driven more by the\n", " attached expert than by the text, and indirect association is mechanically dead.\n", " \"\"\"\n", " n = int(min(N, cfg.hub_probe_n))\n", " idx = torch.arange(n)\n", " g = torch.Generator().manual_seed(cfg.seed + 555)\n", " perm = torch.randperm(n, generator=g)\n", " while int((perm == idx).sum()) > 0: # true derangement\n", " perm = torch.randperm(n, generator=g)\n", "\n", " T_ii, I_ii = _encode_pairs(model, ds, cfg, idx, idx)\n", " T_ij, I_ij = _encode_pairs(model, ds, cfg, idx, perm) # text held, expert swapped\n", " T_ji, I_ji = _encode_pairs(model, ds, cfg, perm, idx) # expert held, text swapped\n", "\n", " cs = lambda a, b: F.cosine_similarity(a, b, dim=-1).mean().item()\n", "\n", " # floor: cosine between readouts of unrelated items\n", " floor_t = float((T_ii @ T_ii.T).mean() - (T_ii @ T_ii.T).diag().mean() / n)\n", " # fixed-text sweep: does the \"text\" readout still move when only the expert moves?\n", " T_fixed, _ = _encode_pairs(model, ds, cfg, torch.zeros(n, dtype=torch.long), idx)\n", "\n", " out = {\n", " \"n\": n,\n", " \"text_hub_stability_same_text_other_expert\": cs(T_ii, T_ij),\n", " \"text_readout_under_text_swap\": cs(T_ii, T_ji),\n", " \"expert_hub_stability_same_expert_other_text\": cs(I_ii, I_ji),\n", " \"expert_readout_under_expert_swap\": cs(I_ii, I_ij),\n", " \"unrelated_pair_floor\": floor_t,\n", " \"erank_text_readout_varying_both\": effective_rank(T_ii),\n", " \"erank_text_readout_TEXT_FIXED\": effective_rank(T_fixed),\n", " }\n", " out[\"hub_is_expert_driven\"] = bool(\n", " out[\"text_hub_stability_same_text_other_expert\"]\n", " < out[\"text_readout_under_text_swap\"])\n", "\n", " line(\"HUB STABILITY — is the readout a function of the TEXT?\")\n", " print(f\" same text, OTHER expert cos = {out['text_hub_stability_same_text_other_expert']:+.4f}\"\n", " f\" (a text-only hub reads 1.0)\")\n", " print(f\" OTHER text, same expert cos = {out['text_readout_under_text_swap']:+.4f}\"\n", " f\" (should sit near the floor)\")\n", " print(f\" unrelated-pair floor = {out['unrelated_pair_floor']:+.4f}\")\n", " print(f\" same expert, OTHER text cos = {out['expert_hub_stability_same_expert_other_text']:+.4f}\"\n", " f\" (expert-side mirror)\")\n", " print(f\" erank of readout: {out['erank_text_readout_varying_both']:.1f} varying both\"\n", " f\" | {out['erank_text_readout_TEXT_FIXED']:.1f} with TEXT HELD CONSTANT\")\n", " print(\" (a text-only hub collapses to ~1.0 when the text is held constant;\")\n", " print(\" anything higher is variance the expert injected into the hub)\")\n", " if out[\"hub_is_expert_driven\"]:\n", " print(\" *** HUB IS EXPERT-DRIVEN: the embedding tracks the attached\")\n", " print(\" expert MORE than it tracks the text. Transitive/indirect\")\n", " print(\" association through this hub is mechanically impossible. ***\")\n", " else:\n", " print(\" Hub tracks text more than expert — transitivity is not ruled out here.\")\n", " return out\n", "\n", "\n", "def retrieval_metrics(ea: torch.Tensor, eb: torch.Tensor, gt: Optional[torch.Tensor] = None,\n", " cv: bool = True, cv_seed: int = 0) -> Dict[str, float]:\n", " \"\"\"Published _metrics, plus explicit ground-truth override for the deranged arm.\"\"\"\n", " N = ea.shape[0]\n", " sim = ea @ eb.T\n", " gt = torch.arange(N) if gt is None else gt\n", "\n", " def r_at_k(k, dim):\n", " topk = sim.topk(min(k, N), dim=dim).indices\n", " if dim == 1:\n", " return (topk == gt.unsqueeze(1)).any(1).float().mean().item()\n", " return (topk == gt.unsqueeze(0)).any(0).float().mean().item()\n", "\n", " diag = sim[torch.arange(N), gt]\n", " out = {\n", " \"r1\": (r_at_k(1, 1) + r_at_k(1, 0)) / 2,\n", " \"r5\": (r_at_k(5, 1) + r_at_k(5, 0)) / 2,\n", " \"cos_match\": diag.mean().item(),\n", " \"cos_rand\": (sim.sum() - diag.sum()).item() / max(N * N - N, 1),\n", " \"n\": N,\n", " \"chance_r1\": 1.0 / N,\n", " }\n", " if cv:\n", " out[\"cv_joint\"] = pentachoron_cv(torch.cat([ea, eb]), n=CFG.cv_samples, seed=cv_seed)\n", " out[\"erank_a\"] = effective_rank(ea)\n", " out[\"erank_b\"] = effective_rank(eb)\n", " return out\n", "\n", "\n", "def procrustes_frame_fit(ea: torch.Tensor, eb: torch.Tensor, n_pairs: int,\n", " seed: int = 0) -> Dict[str, float]:\n", " \"\"\"\n", " fp64 orthogonal Procrustes post-fit on a disjoint pair subset, applied to the\n", " query side, then re-scored on the REMAINDER. Standing dist-campaign rider:\n", " relational/unanchored objectives read as false floors under absolute gauges.\n", " \"\"\"\n", " N = ea.shape[0]\n", " k = min(n_pairs, N // 2)\n", " if k < 32:\n", " return {\"skipped\": True}\n", " g = torch.Generator().manual_seed(seed)\n", " perm = torch.randperm(N, generator=g)\n", " fit_idx, hold_idx = perm[:k], perm[k:]\n", " A = ea[fit_idx].double()\n", " B = eb[fit_idx].double()\n", " U, _, Vt = torch.linalg.svd(A.T @ B, full_matrices=False)\n", " R = (U @ Vt)\n", " ea_r = F.normalize((ea.double() @ R).float(), dim=-1)\n", " eb_n = F.normalize(eb, dim=-1)\n", " m_all = retrieval_metrics(ea_r, eb_n, cv=False)\n", " m_hold = retrieval_metrics(ea_r[hold_idx], eb_n[hold_idx], cv=False)\n", " return {\n", " \"fit_pairs\": k,\n", " \"r1_all_rotated\": m_all[\"r1\"],\n", " \"cos_match_all_rotated\": m_all[\"cos_match\"],\n", " \"r1_heldout_rotated\": m_hold[\"r1\"],\n", " \"cos_match_heldout_rotated\": m_hold[\"cos_match\"],\n", " \"n_heldout\": m_hold[\"n\"],\n", " }\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# DATA + BUILD\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def fetch_split(cfg: AuditConfig, n_shards: Optional[int] = None,\n", " offset: Optional[int] = None):\n", " n = cfg.n_shards if n_shards is None else n_shards\n", " off = cfg.shard_offset if offset is None else offset\n", " print(f\"[data] pulling shards {off}..{off + n - 1} of \"\n", " f\"{cfg.cache_repo}/{cfg.split_dir} (~494MB each) ...\")\n", " parts = []\n", " for i in range(off, off + n):\n", " fn = f\"{cfg.split_dir}/data-{i:05d}-of-{cfg.shard_total:05d}.arrow\"\n", " p = hf_hub_download(cfg.cache_repo, fn, repo_type=\"dataset\")\n", " parts.append(HFDataset.from_file(p))\n", " print(f\" shard {i}: {len(parts[-1])} rows\")\n", " ds = concatenate_datasets(parts).with_format(\"torch\")\n", " print(f\"[data] {len(ds)} rows | columns {ds.column_names}\")\n", " return ds\n", "\n", "\n", "def build_model(cfg: AuditConfig, random_init: bool = False):\n", " torch.manual_seed(cfg.seed)\n", " fcfg = FusionConfig(d_model=cfg.d_model, n_heads=cfg.n_heads, n_layers=cfg.n_layers,\n", " d_ff=cfg.d_ff, dropout=cfg.dropout, max_seq_len=cfg.max_seq_len)\n", " experts = {\n", " \"text\": ExpertModule(\"text\", cfg.d_model, cfg.text_tokens, cfg.d_model,\n", " cfg.n_heads, needs_pooling=False),\n", " cfg.expert: ExpertModule(cfg.expert, cfg.image_input_dim, cfg.image_pooled,\n", " cfg.d_model, cfg.n_heads, needs_pooling=True,\n", " aligner=ProcrustesAligner(cfg.image_input_dim, cfg.d_model,\n", " has_projection=False,\n", " has_expert_whitener=True,\n", " has_text_unwhitener=True)),\n", " }\n", " model = BertensteinFusion(fcfg, experts)\n", "\n", " if not random_init:\n", " p = hf_hub_download(cfg.model_repo, cfg.ckpt_path)\n", " state = safetensors_load(p)\n", " keep = {k: v for k, v in state.items()\n", " if k.startswith(\"experts.text.\") or k.startswith(f\"experts.{cfg.expert}.\")\n", " or k.startswith(\"layers.\") or k in (\"pos\", \"out_norm.weight\", \"out_norm.bias\")}\n", " missing, unexpected = model.load_state_dict(keep, strict=False)\n", " assert not missing, f\"MISSING checkpoint keys (model would be partly random): {missing}\"\n", " print(f\"[model] loaded {len(keep)} tensors | unexpected={list(unexpected)}\")\n", " else:\n", " print(\"[model] RANDOM INIT (control arm)\")\n", "\n", " return model.to(cfg.device).eval()\n", "\n", "\n", "# ── layouts: joint absolute positions, so the split arm is position-matched ──\n", "def joint_layout(cfg: AuditConfig) -> Dict[str, int]:\n", " return {\"text\": 0, cfg.expert: 1 + cfg.text_tokens}\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, ds, cfg: AuditConfig, N: int, mode: str,\n", " perm: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:\n", " \"\"\"\n", " mode:\n", " 'joint' -> published: one pass, unmasked bidirectional attention (A0)\n", " 'blocked' -> one pass, same positions, cross-modal attention masked (A1)\n", " 'split' -> two independent passes, joint-layout positions (A2)\n", " perm: index permutation applied to the EXPERT side inside the joint pass (A4)\n", " \"\"\"\n", " lay = joint_layout(cfg)\n", " te_all, ee_all = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device)\n", " if perm is None:\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " else:\n", " pi = perm[i:j]\n", " eh = ds[pi.tolist()][cfg.expert_col].to(cfg.device).float()\n", "\n", " if mode in (\"joint\", \"blocked\"):\n", " out = model({\"text\": th, cfg.expert: eh}, {\"text\": tm},\n", " block_cross=(mode == \"blocked\"))\n", " te_all.append(out[\"text\"].float().cpu())\n", " ee_all.append(out[cfg.expert].float().cpu())\n", " elif mode == \"split\":\n", " o_t = model({\"text\": th}, {\"text\": tm}, layout_positions=lay)\n", " o_e = model({cfg.expert: eh}, {}, layout_positions=lay)\n", " te_all.append(o_t[\"text\"].float().cpu())\n", " ee_all.append(o_e[cfg.expert].float().cpu())\n", " else:\n", " raise ValueError(mode)\n", " return torch.cat(te_all)[:N], torch.cat(ee_all)[:N]\n", "\n", "\n", "@torch.no_grad()\n", "def procrustes_reference(model, ds, cfg: AuditConfig, N: int) -> Dict[str, float]:\n", " \"\"\"A5: pooled frozen features through the shipped aligner. No fusion layer.\"\"\"\n", " aligner = model.experts[cfg.expert].aligner\n", " tv, ev = [], []\n", " for i in range(0, N, cfg.batch_size):\n", " j = min(i + cfg.batch_size, N)\n", " sl = ds[i:j]\n", " th = sl[\"text_hidden\"].to(cfg.device).float()\n", " tm = sl[\"text_mask\"].to(cfg.device).float()\n", " eh = sl[cfg.expert_col].to(cfg.device).float()\n", " denom = tm.sum(1, keepdim=True).clamp(min=1.0)\n", " tv.append(((th * tm.unsqueeze(-1)).sum(1) / denom).cpu())\n", " ev.append(aligner(eh).mean(1).cpu())\n", " tv = F.normalize(torch.cat(tv)[:N], dim=-1)\n", " ev = F.normalize(torch.cat(ev)[:N], dim=-1)\n", " m = retrieval_metrics(tv, ev, cv=False)\n", " m[\"note\"] = \"frozen features + shipped aligner, no fusion layer\"\n", " return m\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════════════════\n", "\n", "def line(t=\"\"):\n", " print(\"─\" * 78 if not t else f\"── {t} \" + \"─\" * max(0, 74 - len(t)))\n", "\n", "\n", "def fmt(m: Dict[str, Any]) -> str:\n", " s = (f\"R@1={m['r1']:.4f} R@5={m['r5']:.4f} cos_match={m['cos_match']:+.4f} \"\n", " f\"cos_rand={m['cos_rand']:+.5f} chance={m['chance_r1']:.5f}\")\n", " if \"cv_joint\" in m:\n", " s += f\" CV={m['cv_joint']:.4f} erank={m['erank_a']:.1f}/{m['erank_b']:.1f}\"\n", " return s\n", "\n", "\n", "def run(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " t0 = time.time()\n", " print(\"=\" * 78)\n", " print(\"BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\")\n", " print(\"=\" * 78)\n", " print(f\"device={cfg.device} split={cfg.split_dir} expert={cfg.expert}\")\n", "\n", " ds = fetch_split(cfg)\n", " N = min(cfg.eval_n, len(ds))\n", " if N < cfg.eval_n:\n", " print(f\"[warn] only {N} rows available; raise cfg.n_shards for the full {cfg.eval_n}\")\n", "\n", " model = build_model(cfg, random_init=False)\n", " rnd = build_model(cfg, random_init=True)\n", " R: Dict[str, Any] = {\"n\": N, \"config\": {\"n_shards\": cfg.n_shards, \"seed\": cfg.seed,\n", " \"split_dir\": cfg.split_dir}}\n", "\n", " R[\"query_gate\"] = query_degeneracy_gate(ds, cfg, N)\n", " if cfg.split_dir == \"image_coco\":\n", " print(\" NOTE: the model TRAINED on 85% of image_coco (train_test_split seed 42).\")\n", " print(\" Numbers on this split are a CONTAMINATED UPPER BOUND — which makes a\")\n", " print(\" failure here conclusive, and a success here uninformative.\")\n", "\n", " # ── A0 PUBLISHED ─────────────────────────────────────────────────────────\n", " line(\"A0 PUBLISHED — joint pass, unmasked bidirectional attention\")\n", " te, ee = encode(model, ds, cfg, N, \"joint\")\n", " R[\"A0_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A0_published\"]))\n", " d_r1 = abs(R[\"A0_published\"][\"r1\"] - cfg.ref_r1)\n", " d_cm = abs(R[\"A0_published\"][\"cos_match\"] - cfg.ref_cos_match)\n", " ok = (d_r1 <= cfg.reproduce_tol) and (d_cm <= cfg.reproduce_tol)\n", " R[\"A0_reproduction_gate\"] = {\"pass\": bool(ok), \"d_r1\": d_r1, \"d_cos_match\": d_cm,\n", " \"ref_r1\": cfg.ref_r1, \"ref_cos_match\": cfg.ref_cos_match}\n", " print(f\" GATE reproduce-published: {'PASS' if ok else 'FAIL'} \"\n", " f\"(Δr1={d_r1:.4f}, Δcos={d_cm:.4f})\")\n", " if not ok:\n", " print(\" !! A0 did not reproduce. Downstream arms are uninterpretable until it does.\")\n", "\n", " # ── A1 BLOCKED ───────────────────────────────────────────────────────────\n", " line(\"A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED\")\n", " te, ee = encode(model, ds, cfg, N, \"blocked\")\n", " R[\"A1_blocked\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A1_blocked\"]))\n", " R[\"A1_blocked_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A1_blocked_framefit\"]))\n", "\n", " # ── A2 SPLIT ─────────────────────────────────────────────────────────────\n", " line(\"A2 SPLIT — two independent forward passes (what retrieval means)\")\n", " te2, ee2 = encode(model, ds, cfg, N, \"split\")\n", " R[\"A2_split\"] = retrieval_metrics(te2, ee2, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A2_split\"]))\n", "\n", " # correctness invariant found at smoke: with n_layers=1, block-diagonal masking\n", " # and position-matched independent encoding are the SAME computation. If these\n", " # diverge, the mask or the layout offset is wrong and A1/A2 are uninterpretable.\n", " d_inv = max((te - te2).abs().max().item(), (ee - ee2).abs().max().item())\n", " R[\"A1_A2_identity_gate\"] = {\"pass\": bool(d_inv < 1e-4), \"max_abs_diff\": d_inv}\n", " print(f\" GATE A1==A2 (must hold at n_layers=1): \"\n", " f\"{'PASS' if d_inv < 1e-4 else 'FAIL'} max|Δ|={d_inv:.2e}\")\n", " te, ee = te2, ee2\n", " R[\"A2_split_framefit\"] = procrustes_frame_fit(te, ee, cfg.frame_fit_pairs, cfg.seed)\n", " print(\" frame-fit rider:\", json.dumps(R[\"A2_split_framefit\"]))\n", "\n", " # ── A3 UNTRAINED ─────────────────────────────────────────────────────────\n", " line(\"A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"joint\")\n", " R[\"A3_untrained_published\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3_untrained_published\"]))\n", "\n", " line(\"A3b UNTRAINED — random init, SPLIT protocol (the floor)\")\n", " te, ee = encode(rnd, ds, cfg, N, \"split\")\n", " R[\"A3b_untrained_split\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " print(\" \", fmt(R[\"A3b_untrained_split\"]))\n", "\n", " # ── A4 DERANGED ──────────────────────────────────────────────────────────\n", " line(\"A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index\")\n", " g = torch.Generator().manual_seed(cfg.seed + 991)\n", " perm = torch.randperm(N, generator=g)\n", " fixed = int((perm == torch.arange(N)).sum())\n", " te, ee = encode(model, ds, cfg, N, \"joint\", perm=perm)\n", " R[\"A4_deranged\"] = retrieval_metrics(te, ee, cv_seed=cfg.seed)\n", " R[\"A4_deranged\"][\"accidental_fixed_points\"] = fixed\n", " print(\" \", fmt(R[\"A4_deranged\"]), f\" (accidental fixed points: {fixed})\")\n", " print(\" If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\")\n", "\n", " # ── A5 PROCRUSTES REFERENCE ──────────────────────────────────────────────\n", " line(\"A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion\")\n", " R[\"A5_procrustes_reference\"] = procrustes_reference(model, ds, cfg, N)\n", " print(\" \", fmt(R[\"A5_procrustes_reference\"]))\n", " print(f\" published cos_after for image = {cfg.ref_procrustes_cos_after:.4f}\")\n", " print(\" WARNING: rows 0..%d are INSIDE the aligner's own fit set (ds[:5000]).\"\n", " % (N - 1))\n", " print(\" This number is fit-on-test. A5-CLEAN below is the honest one.\")\n", "\n", " # ── A5-CLEAN: rows outside the aligner's fit set, matched N ──────────────\n", " if cfg.a5_clean_shards > 0:\n", " line(\"A5-CLEAN — identical reference, rows OUTSIDE the aligner's fit set\")\n", " try:\n", " ds_c = fetch_split(cfg, n_shards=cfg.a5_clean_shards,\n", " offset=cfg.a5_clean_offset)\n", " Nc = int(min(len(ds_c), N))\n", " oos = procrustes_reference(model, ds_c, cfg, Nc)\n", " ins = procrustes_reference(model, ds, cfg, Nc) # matched-N re-read\n", " R[\"A5_clean_out_of_sample\"] = oos\n", " R[\"A5_insample_matchedN\"] = ins\n", " print(f\" IN-SAMPLE (rows 0..{Nc-1}, N={Nc}): \"\n", " f\"R@1={ins['r1']:.4f} cos={ins['cos_match']:+.4f}\")\n", " print(f\" OUT-OF-SAMPLE (shard {cfg.a5_clean_offset}+, N={Nc}): \"\n", " f\"R@1={oos['r1']:.4f} cos={oos['cos_match']:+.4f}\")\n", " drop = ins[\"r1\"] - oos[\"r1\"]\n", " R[\"A5_generalization_drop\"] = drop\n", " print(f\" generalization drop: {drop:+.4f}\")\n", " print(\" PREREGISTERED: the rotation is 1024x1023/2 = 523,776 free\")\n", " print(\" parameters fit from 5,000 pairs, and it is the ARGMAX of this\")\n", " print(\" very statistic. Measured nulls on rank-30 data at N=5000 give\")\n", " print(\" in-sample R@1 0.175 with NO relationship present, out-of-sample\")\n", " print(\" 0.0005. If OUT-OF-SAMPLE collapses toward chance, the published\")\n", " print(\" cos_after table (0.377-0.440) is the null, not an alignment.\")\n", " except Exception as e:\n", " print(f\" A5-CLEAN skipped: {type(e).__name__}: {str(e)[:120]}\")\n", "\n", " # ── HUB STABILITY ────────────────────────────────────────────────────────\n", " R[\"hub_stability\"] = hub_stability_probe(model, ds, cfg, N)\n", " line(\"HUB STABILITY — UNTRAINED CONTROL (architectural vs learned)\")\n", " R[\"hub_stability_untrained\"] = hub_stability_probe(rnd, ds, cfg, N)\n", " ht, hu = R[\"hub_stability\"], R[\"hub_stability_untrained\"]\n", " print(f\"\\n trained same-text/other-expert {ht['text_hub_stability_same_text_other_expert']:+.4f}\"\n", " f\" | untrained {hu['text_hub_stability_same_text_other_expert']:+.4f}\")\n", " print(\" READ: if BOTH are expert-driven, the hub cannot carry transitivity by\")\n", " print(\" CONSTRUCTION (unmasked joint attention), and no amount of training fixes\")\n", " print(\" it. If only the trained one is, training destroyed a hub the architecture\")\n", " print(\" could otherwise have supported.\")\n", " if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"]:\n", " print(\" (CAVEAT: queries are identical on this split, so the text-swap arm\")\n", " print(\" is vacuous here. Read the hub probe on image_coco only.)\")\n", "\n", " # ── CV NULL ──────────────────────────────────────────────────────────────\n", " line(\"CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null\")\n", " g2 = torch.Generator().manual_seed(cfg.seed + 7)\n", " nulls = {}\n", " for d in cfg.cv_null_dims:\n", " z = F.normalize(torch.randn(2 * min(N, 2048), d, generator=g2), dim=-1)\n", " nulls[d] = pentachoron_cv(z, n=cfg.cv_samples, seed=cfg.seed)\n", " print(f\" normalized randn, ZERO training, d={d:<5d}: CV = {nulls[d]:.4f}\")\n", " R[\"cv_null_by_dim\"] = nulls\n", " print(f\" published model CV (image_coco_test) : {cfg.ref_cv_joint:.4f}\")\n", " print(f\" measured A0 CV : \"\n", " f\"{R['A0_published'].get('cv_joint', float('nan')):.4f}\")\n", " print(f\" measured A0 effective rank (text/expert): \"\n", " f\"{R['A0_published'].get('erank_a', float('nan')):.1f} / \"\n", " f\"{R['A0_published'].get('erank_b', float('nan')):.1f} of nominal {cfg.d_model}\")\n", " print(\" READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\")\n", " print(\" claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\")\n", " print(\" if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\")\n", " print(\" clothes, not convergence to a universal constant.\")\n", "\n", " # ── SWEEP ────────────────────────────────────────────────────────────────\n", " line(\"SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N\")\n", " sweep = {}\n", " for mode, tag, mdl in ((\"joint\", \"A0_published\", model),\n", " (\"blocked\", \"A1_blocked\", model),\n", " (\"split\", \"A2_split\", model),\n", " (\"joint\", \"A3_untrained\", rnd)):\n", " rows = []\n", " for n in cfg.sweep_n:\n", " if n > N:\n", " continue\n", " a, b = encode(mdl, ds, cfg, n, mode)\n", " m = retrieval_metrics(a, b, cv=False)\n", " rows.append({\"N\": n, \"r1\": m[\"r1\"], \"cos_match\": m[\"cos_match\"],\n", " \"chance\": m[\"chance_r1\"]})\n", " sweep[tag] = rows\n", " R[\"sweep\"] = sweep\n", " hdr = \" N \" + \"\".join(f\"{t:>22s}\" for t in sweep.keys())\n", " print(hdr)\n", " for k, n in enumerate(cfg.sweep_n):\n", " if n > N:\n", " continue\n", " cells = []\n", " for tag in sweep:\n", " row = next((r for r in sweep[tag] if r[\"N\"] == n), None)\n", " cells.append(f\"{row['r1']:>22.4f}\" if row else f\"{'-':>22s}\")\n", " print(f\" {n:<7d}\" + \"\".join(cells))\n", "\n", " # ── VERDICT ──────────────────────────────────────────────────────────────\n", " line(\"VERDICT\")\n", " a0, a1, a2, a3, a4 = (R[\"A0_published\"][\"r1\"], R[\"A1_blocked\"][\"r1\"],\n", " R[\"A2_split\"][\"r1\"], R[\"A3_untrained_published\"][\"r1\"],\n", " R[\"A4_deranged\"][\"r1\"])\n", " leak_signals = {\n", " \"constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\":\n", " bool(R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"] and a0 >= 0.9),\n", " \"untrained_reproduces_headline (A3 >= 0.9*A0)\": bool(a3 >= 0.9 * a0),\n", " \"deranged_pairs_still_retrieve (A4 >= 0.9*A0)\": bool(a4 >= 0.9 * a0),\n", " \"blocking_cross_attention_collapses (A1 < 0.5*A0)\": bool(a1 < 0.5 * a0),\n", " \"independent_encoding_collapses (A2 < 0.5*A0)\": bool(a2 < 0.5 * a0),\n", " \"hub_is_expert_driven (blocks transitive/indirect association)\":\n", " bool(R[\"hub_stability\"][\"hub_is_expert_driven\"]),\n", " }\n", " R[\"leak_signals\"] = leak_signals\n", " for k, v in leak_signals.items():\n", " print(f\" [{'X' if v else ' '}] {k}\")\n", " n_leak = sum(leak_signals.values())\n", " R[\"n_leak_signals\"] = n_leak\n", " if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"]:\n", " print(\"\\n CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\")\n", " print(\" OVER-DETERMINED — a perfect model would collapse too. Re-run with\")\n", " print(\" cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\")\n", " if n_leak >= 3:\n", " R[\"verdict\"] = \"PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\"\n", " elif n_leak == 0:\n", " R[\"verdict\"] = \"NO LEAK DETECTED — the number survives every cut; escalate to seeds + a second split\"\n", " else:\n", " R[\"verdict\"] = f\"MIXED ({n_leak}/4 leak signals) — read the arms individually, do not summarize\"\n", " print(f\"\\n => {R['verdict']}\")\n", " print(f\"\\n[done] {time.time() - t0:.1f}s\")\n", "\n", " with open(\"bertenstein_audit_results.json\", \"w\") as f:\n", " json.dump(R, f, indent=2, default=float)\n", " print(\"[done] wrote bertenstein_audit_results.json\")\n", " return R\n", "\n", "\n", "def run_all(cfg: AuditConfig = CFG) -> Dict[str, Any]:\n", " \"\"\"\n", " Run the bed once per split in cfg.run_splits, then print a cross-split summary.\n", " image_coco is the interpretable one; image_coco_test is kept only to reproduce\n", " the published headline and show what it actually measures.\n", " \"\"\"\n", " import dataclasses\n", " out: Dict[str, Any] = {}\n", " for sp in cfg.run_splits:\n", " c = dataclasses.replace(cfg, split_dir=sp)\n", " print(\"\\n\" + \"#\" * 78)\n", " print(f\"# SPLIT: {sp}\")\n", " print(\"#\" * 78)\n", " out[sp] = run(c)\n", "\n", " print(\"\\n\" + \"=\" * 78)\n", " print(\"CROSS-SPLIT SUMMARY\")\n", " print(\"=\" * 78)\n", " print(f\" {'split':18s}{'queries':>10s}{'A0':>9s}{'A1':>9s}{'A2':>9s}{'A3':>9s}{'A4':>9s}\")\n", " for sp, R in out.items():\n", " deg = \"DEGEN\" if R[\"query_gate\"][\"QUERY_SET_DEGENERATE\"] else \"ok\"\n", " print(f\" {sp:18s}{deg:>10s}\"\n", " f\"{R['A0_published']['r1']:>9.4f}{R['A1_blocked']['r1']:>9.4f}\"\n", " f\"{R['A2_split']['r1']:>9.4f}{R['A3_untrained_published']['r1']:>9.4f}\"\n", " f\"{R['A4_deranged']['r1']:>9.4f}\")\n", " print(\"\\n READ: image_coco is the interpretable row — real captions, erank ~38.\")\n", " print(\" If A0 is ~1.0 there while A1/A2 sit at chance, the leak is confirmed on\")\n", " print(\" VALID text, and the degenerate split was never load-bearing to the finding.\")\n", " with open(\"bertenstein_audit_all_splits.json\", \"w\") as f:\n", " json.dump(out, f, indent=2, default=float)\n", " print(\"\\n[done] wrote bertenstein_audit_all_splits.json\")\n", " return out\n", "\n", "\n", "# ── activation ───────────────────────────────────────────────────────────────\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run_all(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "49ca751ee1ed4b93810f1c7e381a544f", "2703f3c40d2341edb3bceb6d1d372984", "c4842d5cf0ba40b8a16fdcb726f9c237", "5186706849194307bb052184a3b22ecf", "a734fda103ae4e199d78e40d946283f8", "9ffafd63f01047c98d595319c153a016", "d1dc9e01192648fc9eef9d1f85a48c63", "6b2525242e9547aca1485f5176a6961d", "b3e6f03156f142389b0d6e64cbb75646", 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"6f7bfd410f2e4d00b2546d5bd2e7ed91", "85b805f328674a7eaf2606a750337320", "99ce59f5e7824cc6bfb05778f469adde", "8113da2f77db4c088e828a63fd7650db", "d0ffa4dec8ea4e7280892cab43a0674b", "0aa8fafac65e4c9ba4a0e844827ca7d2", "9dd9fdc37a524d96bb1ab3e0c3799e1a", "1e3563ac6331461c888051864500deb7", "8d90c858c7b9417ab920630eb535f910", "5108d7198f134379aa46150839ca6b47", "17c858677b2d47e6834ab142a4e5cdec", "626d9ef27b67473884f7f55fdcd47a91", "3fed8c44d76d4a8a80446122ef7bb76a", "0267d8af84e94f5fa655942c9809206a", "38f3cb77a5bd4c609488a3d5b32898c1", "04baf64c416d40dcbddc292b0958e324", "e18a3121a7b74f378b35b492bc4740a5", "f328616388ed4da68cf6c1cfbe7b400c", "450152fe81f141acbc6c93dd16f3a8b3", "904c50a5c8934f60983f730acb67356e", "2ea912164ebb4f33bae7a2fc3e97bfdf", "77be8cb7f1274fd7a636d414bfd53eb1", "b92d4b1b7879482cba6bb02e11ddbe5c", "b59e648177ca48d0ac098705d0c9ff55", "dfd34b85b94d4e249166ede57ea6f05f", "4e9d7903a65c400f9f78a1b62ac204ab", "5e3781a3e90e4fbe85ea090908c24ede", "497f604a5d0b4045ad7b2902e08a28be", "62773659ac7b42dabf452d0f8f96ee8e", "f2a859712c9f4ce9be69b98a8f3552a4", "3f7383d34951492dbddb923516449aac", "8149dad1c42b45528e7b0c1ae03cbbfa", "a9f974cedc134145b9750d253bae0e3b", "6a95a09f50e649c0b70abb0b6bb7c5ef", "730279490a37477fad82fd363f46da35", "79b8a7dc4a6a4829a4fd1e71d4565e55", "184d7cd73cb14e82b27b1eea9f51cbe8", "3b5c6fbf73da46559fc6de627e32d9dd", "d240b62a02d24a3e9e9cc49af22e0042" ] }, "id": "5ihyM9YCrukb", "outputId": "3691193c-bccf-4d01-d671-bb5bacbf9bb8" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "##############################################################################\n", "# SPLIT: image_coco\n", "##############################################################################\n", "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco expert=image\n", "[data] pulling shards 0..5 of AbstractPhil/bertenstein-v1/image_coco (~494MB each) ...\n", " shard 0: 827 rows\n", " shard 1: 827 rows\n", " shard 2: 827 rows\n", " shard 3: 827 rows\n", " shard 4: 827 rows\n", " shard 5: 827 rows\n", "[data] 4962 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4962 rows available; raise cfg.n_shards for the full 5000\n", "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── QUERY DEGENERACY GATE — do the queries vary at all? ───────────────────────\n", " tokens/row: 9..28 (16 distinct lengths)\n", " pooled-text pairwise cos: mean 0.7679 min 0.4332\n", " pooled-text erank 37.82 | pooled-image erank 29.53\n", " GATE PASS: queries vary; retrieval numbers are interpretable.\n", " NOTE: the model TRAINED on 85% of image_coco (train_test_split seed 42).\n", " Numbers on this split are a CONTAMINATED UPPER BOUND — which makes a\n", " failure here conclusive, and a success here uninformative.\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9838 cos_rand=+0.00735 chance=0.00020 CV=0.1753 erank=19.7/19.7\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0103)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0007 R@5=0.0020 cos_match=+0.0302 cos_rand=-0.00486 chance=0.00020 CV=0.5493 erank=2.9/19.7\n", " frame-fit rider: {\"fit_pairs\": 2481, \"r1_all_rotated\": 0.005038291041273624, \"cos_match_all_rotated\": 0.2246374487876892, \"r1_heldout_rotated\": 0.0072551388293504715, \"cos_match_heldout_rotated\": 0.21771758794784546, \"n_heldout\": 2481}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0007 R@5=0.0020 cos_match=+0.0302 cos_rand=-0.00486 chance=0.00020 CV=0.5493 erank=2.9/19.7\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=1.10e-07\n", " frame-fit rider: {\"fit_pairs\": 2481, \"r1_all_rotated\": 0.005038291041273624, \"cos_match_all_rotated\": 0.22463741898536682, \"r1_heldout_rotated\": 0.0072551388293504715, \"cos_match_heldout_rotated\": 0.21771757304668427, \"n_heldout\": 2481}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0011 cos_match=+0.0181 cos_rand=+0.01560 chance=0.00020 CV=0.1939 erank=35.3/43.7\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0000 R@5=0.0009 cos_match=+0.0113 cos_rand=+0.01117 chance=0.00020 CV=0.4312 erank=28.6/33.7\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9838 cos_rand=+0.00735 chance=0.00020 CV=0.1670 erank=19.7/19.7 (accidental fixed points: 0)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.2328 R@5=0.3827 cos_match=+0.2689 cos_rand=+0.00024 chance=0.00020\n", " published cos_after for image = 0.4107\n", " WARNING: rows 0..4961 are INSIDE the aligner's own fit set (ds[:5000]).\n", " This number is fit-on-test. A5-CLEAN below is the honest one.\n", "── A5-CLEAN — identical reference, rows OUTSIDE the aligner's fit set ────────\n", "[data] pulling shards 7..12 of AbstractPhil/bertenstein-v1/image_coco (~494MB each) ...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00007-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "49ca751ee1ed4b93810f1c7e381a544f" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00007-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "2bf0b94a29664e24bfb01aae269ff172" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 7: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00008-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6cf36cb08e0646d4a2d14216d9708743" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00008-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "19af2de8256a4b5093962abac12181bd" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 8: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00009-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "111e6192bd78406cbf2c6eac50f44b3e" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00009-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "fc38bec4b5334e6095096786782d4e42" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 9: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00010-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6941e32ec92c41e9ac8f8563885809b8" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00010-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6305697ae3584f0e8558971ef2f2674d" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 10: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00011-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "17fa79d23ef74c3eb74926a2d5ad3d11" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00011-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "43dfc093b1a04b4e9591a3073692ed03" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 11: 827 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00012-of-00049.arrow: reconstructing file: 0%| | 0.00B / 490MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6774fa89765e42e8a67180dc4afb6f0b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco/data-00012-of-00049.arrow: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "c8dda27d796042eca18e4796971745da" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 12: 827 rows\n", "[data] 4962 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", " IN-SAMPLE (rows 0..4961, N=4962): R@1=0.2328 cos=+0.2689\n", " OUT-OF-SAMPLE (shard 7+, N=4962): R@1=0.0088 cos=+0.1121\n", " generalization drop: +0.2240\n", " PREREGISTERED: the rotation is 1024x1023/2 = 523,776 free\n", " parameters fit from 5,000 pairs, and it is the ARGMAX of this\n", " very statistic. Measured nulls on rank-30 data at N=5000 give\n", " in-sample R@1 0.175 with NO relationship present, out-of-sample\n", " 0.0005. If OUT-OF-SAMPLE collapses toward chance, the published\n", " cos_after table (0.377-0.440) is the null, not an alignment.\n", "── HUB STABILITY — is the readout a function of the TEXT? ─────────────\n", " same text, OTHER expert cos = +0.0146 (a text-only hub reads 1.0)\n", " OTHER text, same expert cos = +0.9949 (should sit near the floor)\n", " unrelated-pair floor = +0.0066\n", " same expert, OTHER text cos = +0.9958 (expert-side mirror)\n", " erank of readout: 19.4 varying both | 19.3 with TEXT HELD CONSTANT\n", " (a text-only hub collapses to ~1.0 when the text is held constant;\n", " anything higher is variance the expert injected into the hub)\n", " *** HUB IS EXPERT-DRIVEN: the embedding tracks the attached\n", " expert MORE than it tracks the text. Transitive/indirect\n", " association through this hub is mechanically impossible. ***\n", "── HUB STABILITY — UNTRAINED CONTROL (architectural vs learned) ──────────────\n", "── HUB STABILITY — is the readout a function of the TEXT? ─────────────\n", " same text, OTHER expert cos = +0.6923 (a text-only hub reads 1.0)\n", " OTHER text, same expert cos = +0.8097 (should sit near the floor)\n", " unrelated-pair floor = +0.5337\n", " same expert, OTHER text cos = +0.7962 (expert-side mirror)\n", " erank of readout: 35.6 varying both | 25.8 with TEXT HELD CONSTANT\n", " (a text-only hub collapses to ~1.0 when the text is held constant;\n", " anything higher is variance the expert injected into the hub)\n", " *** HUB IS EXPERT-DRIVEN: the embedding tracks the attached\n", " expert MORE than it tracks the text. Transitive/indirect\n", " association through this hub is mechanically impossible. ***\n", "\n", " trained same-text/other-expert +0.0146 | untrained +0.6923\n", " READ: if BOTH are expert-driven, the hub cannot carry transitivity by\n", " CONSTRUCTION (unmasked joint attention), and no amount of training fixes\n", " it. If only the trained one is, training destroyed a hub the architecture\n", " could otherwise have supported.\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1753\n", " measured A0 effective rank (text/expert): 19.7 / 19.7 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0391 0.0391 0.0000\n", " 128 1.0000 0.0273 0.0273 0.0078\n", " 256 1.0000 0.0098 0.0098 0.0039\n", " 512 1.0000 0.0029 0.0029 0.0010\n", " 1024 1.0000 0.0015 0.0015 0.0010\n", " 2048 1.0000 0.0010 0.0010 0.0005\n", " 4096 1.0000 0.0010 0.0010 0.0002\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [ ] constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", " [X] hub_is_expert_driven (blocks transitive/indirect association)\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 68.6s\n", "[done] wrote bertenstein_audit_results.json\n", "\n", "##############################################################################\n", "# SPLIT: image_coco_test\n", "##############################################################################\n", "==============================================================================\n", "BERTENSTEIN R@1 AUDIT — is the number alignment, or is it the protocol?\n", "==============================================================================\n", "device=cuda split=image_coco_test expert=image\n", "[data] pulling shards 0..5 of AbstractPhil/bertenstein-v1/image_coco_test (~494MB each) ...\n", " shard 0: 833 rows\n", " shard 1: 833 rows\n", " shard 2: 833 rows\n", " shard 3: 833 rows\n", " shard 4: 833 rows\n", " shard 5: 833 rows\n", "[data] 4998 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", "[warn] only 4998 rows available; raise cfg.n_shards for the full 5000\n", "[model] loaded 42 tensors | unexpected=[]\n", "[model] RANDOM INIT (control arm)\n", "── QUERY DEGENERACY GATE — do the queries vary at all? ───────────────────────\n", " tokens/row: 3..3 (1 distinct lengths)\n", " pooled-text pairwise cos: mean 1.0000 min 1.0000\n", " pooled-text erank 1.00 | pooled-image erank 48.92\n", " *** GATE FAIL: THE TEXT QUERIES ARE IDENTICAL. ***\n", " Every retrieval number on this split is uninterpretable AS ALIGNMENT.\n", " A constant query cannot rank a gallery. If A0 still scores high, that\n", " is a direct proof the score does not come from the query modality.\n", "── A0 PUBLISHED — joint pass, unmasked bidirectional attention ──────────────\n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1961 erank=20.5/20.4\n", " GATE reproduce-published: PASS (Δr1=0.0000, Δcos=0.0000)\n", "── A1 BLOCKED — same sequence, same positions, cross-modal attention MASKED ─\n", " R@1=0.0001 R@5=0.0007 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.3156 erank=1.0/20.6\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0004001600609626621, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A2 SPLIT — two independent forward passes (what retrieval means) ─────────\n", " R@1=0.0002 R@5=0.0008 cos_match=-0.0133 cos_rand=-0.01327 chance=0.00020 CV=0.4199 erank=1.0/20.6\n", " GATE A1==A2 (must hold at n_layers=1): PASS max|Δ|=6.71e-08\n", " frame-fit rider: {\"fit_pairs\": 2499, \"r1_all_rotated\": 0.00020008003048133105, \"cos_match_all_rotated\": 0.02848789095878601, \"r1_heldout_rotated\": 0.0006002400914439932, \"cos_match_heldout_rotated\": 0.02337970770895481, \"n_heldout\": 2499}\n", "── A3 UNTRAINED — random init, PUBLISHED protocol (does the metric need weights?) \n", " R@1=0.0003 R@5=0.0015 cos_match=-0.0177 cos_rand=-0.01890 chance=0.00020 CV=0.2661 erank=41.8/44.3\n", "── A3b UNTRAINED — random init, SPLIT protocol (the floor) ───────────────────\n", " R@1=0.0002 R@5=0.0011 cos_match=-0.0060 cos_rand=-0.00601 chance=0.00020 CV=0.1381 erank=1.1/51.8\n", "── A4 DERANGED — joint pass, text[i] fused with image[perm[i]]; GT = pass index \n", " R@1=1.0000 R@5=1.0000 cos_match=+0.9735 cos_rand=+0.05166 chance=0.00020 CV=0.1721 erank=20.5/20.4 (accidental fixed points: 1)\n", " If R@1 holds here, the diagonal is FORWARD-PASS identity, not semantics.\n", "── A5 PROCRUSTES REFERENCE — frozen features + shipped aligner, no fusion ───\n", " R@1=0.0002 R@5=0.0010 cos_match=+0.0079 cos_rand=+0.00788 chance=0.00020\n", " published cos_after for image = 0.4107\n", " WARNING: rows 0..4997 are INSIDE the aligner's own fit set (ds[:5000]).\n", " This number is fit-on-test. A5-CLEAN below is the honest one.\n", "── A5-CLEAN — identical reference, rows OUTSIDE the aligner's fit set ────────\n", "[data] pulling shards 7..12 of AbstractPhil/bertenstein-v1/image_coco_test (~494MB each) ...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00007-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "29bc232dacb54d5cb50281ba1d8228e6" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00007-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "bd966c8b21484997b1d046072d4f9825" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 7: 832 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00008-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "836642e48ba34f7a93ef336bed45ee98" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00008-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "27b59fcfec6b46f48112ddcddb8fa25c" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 8: 832 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00009-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5d6b9006f3ac4fe69cd6df51bb8eca4d" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00009-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "570da58dbd2a420f84dab2db10f4d81f" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 9: 832 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00010-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "827658e288904ab6a3faeb0645351ab2" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00010-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f66a0d4847df4a45a070092441e1deeb" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 10: 832 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00011-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a7a79d6f1cd545a5ae016dd1173e1007" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00011-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9dd9fdc37a524d96bb1ab3e0c3799e1a" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 11: 832 rows\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00012-of-00049.arro(…): reconstructing file: 0%| | 0.00B / 493MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f328616388ed4da68cf6c1cfbe7b400c" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "image_coco_test/data-00012-of-00049.arro(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "62773659ac7b42dabf452d0f8f96ee8e" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " shard 12: 832 rows\n", "[data] 4992 rows | columns ['text_hidden', 'text_mask', 'image_hidden']\n", " IN-SAMPLE (rows 0..4991, N=4992): R@1=0.0002 cos=+0.0078\n", " OUT-OF-SAMPLE (shard 7+, N=4992): R@1=0.0002 cos=+0.0068\n", " generalization drop: +0.0000\n", " PREREGISTERED: the rotation is 1024x1023/2 = 523,776 free\n", " parameters fit from 5,000 pairs, and it is the ARGMAX of this\n", " very statistic. Measured nulls on rank-30 data at N=5000 give\n", " in-sample R@1 0.175 with NO relationship present, out-of-sample\n", " 0.0005. If OUT-OF-SAMPLE collapses toward chance, the published\n", " cos_after table (0.377-0.440) is the null, not an alignment.\n", "── HUB STABILITY — is the readout a function of the TEXT? ─────────────\n", " same text, OTHER expert cos = +0.0664 (a text-only hub reads 1.0)\n", " OTHER text, same expert cos = +1.0000 (should sit near the floor)\n", " unrelated-pair floor = +0.0611\n", " same expert, OTHER text cos = +1.0000 (expert-side mirror)\n", " erank of readout: 20.2 varying both | 20.2 with TEXT HELD CONSTANT\n", " (a text-only hub collapses to ~1.0 when the text is held constant;\n", " anything higher is variance the expert injected into the hub)\n", " *** HUB IS EXPERT-DRIVEN: the embedding tracks the attached\n", " expert MORE than it tracks the text. Transitive/indirect\n", " association through this hub is mechanically impossible. ***\n", "── HUB STABILITY — UNTRAINED CONTROL (architectural vs learned) ──────────────\n", "── HUB STABILITY — is the readout a function of the TEXT? ─────────────\n", " same text, OTHER expert cos = +0.7762 (a text-only hub reads 1.0)\n", " OTHER text, same expert cos = +1.0000 (should sit near the floor)\n", " unrelated-pair floor = +0.7729\n", " same expert, OTHER text cos = +1.0000 (expert-side mirror)\n", " erank of readout: 41.4 varying both | 41.4 with TEXT HELD CONSTANT\n", " (a text-only hub collapses to ~1.0 when the text is held constant;\n", " anything higher is variance the expert injected into the hub)\n", " *** HUB IS EXPERT-DRIVEN: the embedding tracks the attached\n", " expert MORE than it tracks the text. Transitive/indirect\n", " association through this hub is mechanically impossible. ***\n", "\n", " trained same-text/other-expert +0.0664 | untrained +0.7762\n", " READ: if BOTH are expert-driven, the hub cannot carry transitivity by\n", " CONSTRUCTION (unmasked joint attention), and no amount of training fixes\n", " it. If only the trained one is, training destroyed a hub the architecture\n", " could otherwise have supported.\n", " (CAVEAT: queries are identical on this split, so the text-swap arm\n", " is vacuous here. Read the hub probe on image_coco only.)\n", "── CV NULL — the '0.20 universal band' is dimension-dependent; sweep the null \n", " normalized randn, ZERO training, d=16 : CV = 0.2105\n", " normalized randn, ZERO training, d=64 : CV = 0.0903\n", " normalized randn, ZERO training, d=256 : CV = 0.0398\n", " normalized randn, ZERO training, d=1024 : CV = 0.0207\n", " published model CV (image_coco_test) : 0.2007\n", " measured A0 CV : 0.1961\n", " measured A0 effective rank (text/expert): 20.5 / 20.4 of nominal 1024\n", " READ: canon puts the 0.20 band on S^15. A d=1024 space reading ~0.20 is\n", " claiming eff_geom_dim ~ 16 of 1024 nominal. erank is the direct check —\n", " if erank is tens rather than hundreds, that is COLLAPSE wearing the band's\n", " clothes, not convergence to a universal constant.\n", "── SWEEP — gallery size N. A leak is N-invariant; retrieval decays ~log N ────\n", " N A0_published A1_blocked A2_split A3_untrained\n", " 64 1.0000 0.0156 0.0156 0.0234\n", " 128 1.0000 0.0078 0.0078 0.0117\n", " 256 1.0000 0.0039 0.0039 0.0000\n", " 512 1.0000 0.0020 0.0020 0.0010\n", " 1024 1.0000 0.0010 0.0010 0.0010\n", " 2048 1.0000 0.0005 0.0005 0.0007\n", " 4096 1.0000 0.0002 0.0002 0.0004\n", "── VERDICT ───────────────────────────────────────────────────────────────────\n", " [X] constant_queries_still_retrieve (degenerate gate AND A0 >= 0.9)\n", " [ ] untrained_reproduces_headline (A3 >= 0.9*A0)\n", " [X] deranged_pairs_still_retrieve (A4 >= 0.9*A0)\n", " [X] blocking_cross_attention_collapses (A1 < 0.5*A0)\n", " [X] independent_encoding_collapses (A2 < 0.5*A0)\n", " [X] hub_is_expert_driven (blocks transitive/indirect association)\n", "\n", " CAVEAT: queries are degenerate on this split, so the A1/A2 collapse is\n", " OVER-DETERMINED — a perfect model would collapse too. Re-run with\n", " cfg.split_dir='image_coco' to get an interpretable A1/A2 number.\n", "\n", " => PROTOCOL LEAK — R@1 measures joint-pass readout coupling, not alignment\n", "\n", "[done] 73.9s\n", "[done] wrote bertenstein_audit_results.json\n", "\n", "==============================================================================\n", "CROSS-SPLIT SUMMARY\n", "==============================================================================\n", " split queries A0 A1 A2 A3 A4\n", " image_coco ok 1.0000 0.0007 0.0007 0.0003 1.0000\n", " image_coco_test DEGEN 1.0000 0.0001 0.0002 0.0003 1.0000\n", "\n", " READ: image_coco is the interpretable row — real captions, erank ~38.\n", " If A0 is ~1.0 there while A1/A2 sit at chance, the leak is confirmed on\n", " VALID text, and the degenerate split was never load-bearing to the finding.\n", "\n", "[done] wrote bertenstein_audit_all_splits.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-v2 — CONSENSUS DISTILLATION AT CC12M SCALE\n", "#\n", "# v2 vs the shipped 500k model, per Phil's 2026-07-31 guidance:\n", "# - NO ALIGNMENT BANK. v1's bank was additive and experimental; measured on real\n", "# embeddings its expert-consistency block varied 0.2% across samples and took\n", "# 0.23% of geo_proj energy while anchor distances took 98.7%. Banks in this\n", "# format are content extensions — an AMOE-LORA is the right carrier, attached\n", "# as a separate finetune pass on the prefitted core. Not here.\n", "# - LEGROOM. d 384->512, 6L->12L, ff 1536->2048, heads 6->8. 26.0M -> 58.3M\n", "# (0.53x bert-base, so the compression story survives). Sized for many\n", "# overlapping sources at ~36M features/teacher, not one 500k census.\n", "# - CHAMPION OBJECTIVE. InfoNCE + per-sample MSE against the consensus — the\n", "# consensus_nce_mse form that won the CC12M vision matrix on every task gauge,\n", "# both seeds. NO shipped rotation needed here: that line aligns to a running\n", "# mean (frame free), this one aligns to a REFERENCE MEMBER (bert), so the frame\n", "# is pinned by construction. A frame-fit gauge runs anyway to confirm it.\n", "# - CULL-PROOF. Colab kills the VM every 24h and takes local disk with it.\n", "# Full state (model/opt/sched/scaler/step/epoch/chunk-order/RNG) checkpoints on\n", "# a TIME cadence, and pushes to HF so a cull costs minutes, not the run.\n", "# - FULL TENSORBOARD. per-step losses + lr + grad-norm, per-eval gauges\n", "# (mimicry, cos, isotropy, effective rank, CV), histograms, and the alignment\n", "# report as text.\n", "#\n", "# STAGES (each resumable, each gated) — carried from the cc12m pipeline:\n", "# 0 PARITY which caption field was embedded + row alignment. Hard gate.\n", "# 1 FIT one global whitened-Procrustes map per expert -> bert, stratified\n", "# random fit, reported OUT-OF-SAMPLE on held-out chunks.\n", "# 2 TARGETS per-chunk consensus -> fp16, ledgered, expert shards deleted after.\n", "# 3 TRAIN streams (captions, consensus) pairs, dynamic padding.\n", "#\n", "# Colab-cell-safe. HF_TOKEN from Colab secrets (key icon) or env.\n", "# ============================================================================\n", "\n", "import dataclasses, gc, json, math, os, random, sys, time, subprocess, shutil\n", "import dataclasses\n", "from dataclasses import dataclass, asdict\n", "from typing import Any, Dict, List, Optional, Tuple\n", "\n", "for _p in (\"datasets\", \"transformers\", \"huggingface_hub\", \"tensorboard\", \"safetensors\"):\n", " try:\n", " __import__(_p)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "# Variable-length batches fragment the caching allocator badly; this is the\n", "# documented mitigation and must be set BEFORE torch initialises CUDA.\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\", \"expandable_segments:True\")\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from huggingface_hub import hf_hub_download, HfApi, create_repo\n", "from torch.utils.tensorboard import SummaryWriter\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " # ================= EDIT THESE THREE, THEN PASTE =================\n", " # The cell AUTO-RUNS on paste, so the stage flags below are DERIVED from\n", " # this profile inside run() -- they are not yours to set by hand, and\n", " # setting them after pasting is too late.\n", " # \"full\" everything from scratch, including the alignment fit\n", " # \"retrain\" NEW trunk on an EXISTING corpus: reuse maps + targets,\n", " # train from step 0 <-- the -b shape\n", " # \"continue\" pick up an interrupted run of the SAME job\n", " # \"targets\" rebuild consensus targets only\n", " profile: str = \"retrain\"\n", " run_name: str = \"captionbert-8192-b\"\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2-B\"\n", " # ================================================================\n", "\n", " # ── sources ── (list so overlapping datasets can be added later)\n", " sources: Tuple[Dict[str, Any], ...] = (\n", " {\"repo\": \"AbstractPhil/conceptual-captions-12m-webdataset-berts\",\n", " \"n_chunks\": 66, \"chunk_rows\": 500_000,\n", " # REPAIRED 2026-08-02 and gate-verified: the 10 chunks ModernBERT was\n", " # missing now reproduce at cos 1.00000 / min 1.00000 against a re-embed\n", " # (same field, row order, mean pooling, truncation). 66/66 complete,\n", " # so nothing is excluded. Repopulate this only if a gate FAILS.\n", " \"missing\": {}},\n", " )\n", " experts: Tuple[str, ...] = (\"bert\", \"modern\", \"roberta\", \"albert\", \"distil\")\n", " ref_expert: str = \"bert\"\n", " ref_hf_name: str = \"google-bert/bert-base-uncased\"\n", " require_all_experts: bool = True\n", " caption_field: Optional[str] = None\n", " caption_field_candidates: Tuple[str, ...] = (\n", " \"caption_llava\", \"caption\", \"caption_llava_short\")\n", "\n", " work_dir: str = \"/content/cbv2\"\n", " keep_expert_shards: bool = False\n", "\n", " # ── hardware allowance (Colab Pro+ / RTX 6000 Pro, measured 2026-07-31) ──\n", " # disk 235.7GB (~176 free) | RAM 176.9GB | GPU 95.6GB | 401.5 units @ 8.9/h = 45.1h\n", " # The expert shards are 507GB — 2.1x the WHOLE DISK. They are streamed one chunk\n", " # at a time and deleted; only the 43GB consensus is kept.\n", " disk_floor_gb: float = 25.0 # abort a chunk if free disk drops below\n", " ram_resident: bool = True # hold tokens+targets in RAM (48.8GB)\n", " preflight: bool = True\n", "\n", " # ── backup (Colab culls at 24h; local disk dies with the VM) ──\n", " # where SKIPPED stages fetch their outputs from. Distinct from hf_repo:\n", " # a new run publishes somewhere new but still reads the ORIGINAL maps.\n", " artifact_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " targets_repo: str = \"AbstractPhil/captionbert-8192-v2-consensus\"\n", " push_targets: bool = True # 43GB; re-derivable only from a 507GB pull\n", " hf_push: bool = True\n", " push_every_min: float = 30.0\n", " keep_local_ckpts: int = 3\n", "\n", " # ── stage 0 ──\n", " parity_chunk: int = 0\n", " parity_n: int = 64\n", " parity_min_cos: float = 0.999\n", "\n", " # ── stage 1 ──\n", " fit_chunks: Tuple[int, ...] = (0, 11, 22, 33, 44, 55)\n", " fit_rows_per_chunk: int = 4000 # 24k vs d=768 -> N/d = 31\n", " holdout_chunks: Tuple[int, ...] = (60, 61)\n", " fit_seed: int = 0\n", "\n", " # ── student (LEGROOM) ──\n", " d_model: int = 512 # was 384\n", " n_heads: int = 8 # was 6\n", " n_layers: int = 12 # was 6\n", " d_ff: int = 2048 # was 1536\n", " max_len: int = 8192 # name-bearing; costs 4.2M params\n", " output_dim: int = 768 # consensus space = teacher dim\n", " dropout: float = 0.1\n", " pooling: str = \"mean\" # arm: \"cls\". teachers are mean-pooled\n", " max_tokens: int = 256 # dynamic pad ceiling\n", " # OOM FIX (2026-07-31, observed at B=2048): dynamic padding pads to the BATCH\n", " # max, and with 2048 draws the max is essentially always the ceiling. The corpus\n", " # mean is 48 tokens but every batch ran at L=256 -- attention memory goes as L^2,\n", " # so 12 layers needed ~120 GB against 95 available.\n", " # length_bucketing sorts within a shuffled window so a batch is length-\n", " # homogeneous: L tracks the corpus mean (~48-64) instead of\n", " # the ceiling. ~5x less memory AND ~5x less compute.\n", " # grad_checkpointing bounds the worst case. The longest bucket IS a full batch\n", " # at L=256; checkpointing puts that at ~19 GB instead of\n", " # ~148 GB, for about 30% more compute.\n", " length_bucketing: bool = True\n", " bucket_window: int = 64 # batches per sort window\n", " grad_checkpointing: bool = True\n", " vram_probe: bool = True # forward+backward at worst case first\n", "\n", " # ── training (sized for 95.6GB GPU: batch size IS the InfoNCE negative count) ──\n", " epochs: int = 4 # 13.7k steps/ep at 2048 -> ~55k total\n", " batch_size: int = 2048 # was 512; ~19GB activations, 4x negatives\n", " lr: float = 6e-4 # sqrt-scaled from 3e-4 @ 512\n", " min_lr: float = 1e-6\n", " warmup_steps: int = 2000\n", " grad_clip: float = 1.0\n", " seed: int = 42\n", " amp: bool = True\n", " num_workers: int = 0 # RAM-resident: no workers needed\n", " log_every: int = 50\n", " eval_every: int = 1000\n", " ckpt_every_min: float = 20.0 # TIME-based: culls are wall-clock\n", "\n", " # ---- RESUME SAFETY ----\n", " # fresh_start=True: never look for a checkpoint, never pull one from the hub,\n", " # start at step 0. Set it whenever the CORPUS or the ARCHITECTURE changed.\n", " # Why this exists: a completed run's state.pt carries epoch=epochs, so\n", " # `for ep in range(ep0, cfg.epochs)` is an EMPTY RANGE -- the job builds the\n", " # whole RamStore, trains ZERO steps, prints the previous run's final metrics\n", " # and pushes them as if they were new. It then uploads that state to the new\n", " # repo, so the next attempt inherits it too. Measured on 2026-08-02.\n", " fresh_start: bool = False\n", " # even with resume on, a checkpoint is REJECTED when the row count or the\n", " # run name disagrees with the current job. Set True only to override.\n", " allow_mismatched_resume: bool = False\n", "\n", " # ── loss: the champion form ──\n", " nce_weight: float = 1.0\n", " mse_weight: float = 1.0\n", " nce_temperature: float = 0.07\n", " cv_weight: float = 0.0 # arm: 0.1 reproduces the v1 stack\n", " cv_target: float = 0.084\n", "\n", " # ── stages ──\n", " run_stage0: bool = True\n", " run_stage1: bool = True\n", " run_stage2: bool = True\n", " run_stage3: bool = True\n", " resume: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# HELPERS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def line(t=\"\"):\n", " print(\"─\" * 78 if not t else f\"── {t} \" + \"─\" * max(0, 74 - len(t)))\n", "\n", "\n", "def paths(cfg) -> Dict[str, str]:\n", " w = cfg.work_dir\n", " d = {\"root\": w, \"targets\": f\"{w}/targets\", \"maps\": f\"{w}/maps\",\n", " \"ckpt\": f\"{w}/checkpoints\", \"tb\": f\"{w}/tensorboard\", \"shards\": f\"{w}/shards\",\n", " \"config\": f\"{w}/config\"}\n", " for p in d.values():\n", " os.makedirs(p, exist_ok=True)\n", " return d\n", "\n", "\n", "def src0(cfg) -> Dict[str, Any]:\n", " return cfg.sources[0]\n", "\n", "\n", "def usable_chunks(cfg) -> List[int]:\n", " s = src0(cfg)\n", " c = set(range(s[\"n_chunks\"]))\n", " if cfg.require_all_experts:\n", " for miss in s.get(\"missing\", {}).values():\n", " c -= set(miss)\n", " return sorted(c - set(cfg.holdout_chunks))\n", "\n", "\n", "def fetch(cfg, fname: str) -> str:\n", " return hf_hub_download(src0(cfg)[\"repo\"], fname, repo_type=\"dataset\",\n", " local_dir=paths(cfg)[\"shards\"])\n", "\n", "\n", "def load_captions_chunk(cfg, c: int) -> List[str]:\n", " raw = json.load(open(fetch(cfg, f\"captions_{c:03d}.json\")))\n", " f = cfg.caption_field\n", " if isinstance(raw, dict):\n", " return list(raw[f])\n", " if raw and isinstance(raw[0], dict):\n", " return [r[f] for r in raw]\n", " return list(raw)\n", "\n", "\n", "def load_expert_chunk(cfg, expert: str, c: int) -> torch.Tensor:\n", " return torch.load(fetch(cfg, f\"{expert}_{c:03d}.pt\"),\n", " weights_only=True, map_location=\"cpu\")\n", "\n", "\n", "def drop_shard(cfg, fname: str):\n", " if cfg.keep_expert_shards:\n", " return\n", " p = os.path.join(paths(cfg)[\"shards\"], fname)\n", " if os.path.exists(p):\n", " os.remove(p)\n", "\n", "\n", "def free_gb(path: str) -> float:\n", " st = os.statvfs(path)\n", " return st.f_bavail * st.f_frsize / 1e9\n", "\n", "\n", "def purge_hf_cache(cfg):\n", " \"\"\"\n", " The expert shards total 507GB against a 235.7GB disk. hf_hub_download with\n", " local_dir does not populate the global cache on modern hub versions, but a\n", " stale HF_HOME cache or an older version WILL duplicate every shard and blow\n", " the disk mid-run. Purge both, every chunk.\n", " \"\"\"\n", " for d in (os.path.join(paths(cfg)[\"shards\"], \".cache\"),\n", " os.environ.get(\"HF_HUB_CACHE\", \"\"),\n", " os.path.expanduser(\"~/.cache/huggingface/hub\")):\n", " if d and os.path.isdir(d):\n", " for entry in os.listdir(d):\n", " if entry.startswith(\"datasets--\"):\n", " shutil.rmtree(os.path.join(d, entry), ignore_errors=True)\n", "\n", "\n", "PROFILES = {\n", " # sentinel: the profile has already been applied, do not re-derive\n", " \"_applied\": {},\n", " # everything from scratch, including the alignment fit\n", " \"full\": dict(run_stage0=True, run_stage1=True, run_stage2=True,\n", " run_stage3=True, fresh_start=True),\n", " # NEW TRUNK on an EXISTING corpus: reuse the maps and the consensus targets,\n", " # train from step 0. This is the captionbert-8192-b shape.\n", " \"retrain\": dict(run_stage0=False, run_stage1=False, run_stage2=False,\n", " run_stage3=True, fresh_start=True,\n", " caption_field=\"caption_llava\"),\n", " # pick up an interrupted run of the SAME job\n", " \"continue\": dict(run_stage0=False, run_stage1=False, run_stage2=False,\n", " run_stage3=True, fresh_start=False,\n", " caption_field=\"caption_llava\"),\n", " # rebuild targets only (e.g. after repairing missing expert shards)\n", " \"targets\": dict(run_stage0=False, run_stage1=False, run_stage2=True,\n", " run_stage3=False, fresh_start=True,\n", " caption_field=\"caption_llava\"),\n", "}\n", "\n", "\n", "def configure(cfg, profile, **over):\n", " \"\"\"Apply a coherent flag set, validate, return. '_applied' is a no-op.\"\"\"\n", " \"\"\"\n", " One call that sets a COHERENT set of flags, then applies overrides.\n", " Validates the result and raises rather than starting a run that cannot work.\n", " \"\"\"\n", " import dataclasses\n", " if profile not in PROFILES:\n", " raise ValueError(f\"profile must be one of {sorted(PROFILES)}\")\n", " if profile == \"_applied\":\n", " return dataclasses.replace(cfg, **over) if over else cfg\n", " cfg = dataclasses.replace(cfg, **{**PROFILES[profile], **over})\n", " line(f\"PROFILE '{profile}'\")\n", " for k in (\"run_name\", \"hf_repo\", \"artifact_repo\", \"targets_repo\",\n", " \"caption_field\", \"fresh_start\"):\n", " print(f\" {k:16s} {getattr(cfg, k)}\")\n", " print(f\" stages 0:{cfg.run_stage0} 1:{cfg.run_stage1} \"\n", " f\"2:{cfg.run_stage2} 3:{cfg.run_stage3}\")\n", " bad = []\n", " if not cfg.run_stage0 and not cfg.caption_field:\n", " bad.append(\"stage0 off but caption_field is unset\")\n", " if cfg.run_stage1 and not cfg.run_stage2:\n", " bad.append(\"REFITTING the maps while REUSING targets built with the old \"\n", " \"map -- the targets would sit in a different frame\")\n", " if cfg.run_stage3 and not (cfg.run_stage2 or cfg.targets_repo):\n", " bad.append(\"stage3 needs targets: build them or set targets_repo\")\n", " if cfg.hf_repo == cfg.artifact_repo and cfg.fresh_start and cfg.run_stage3:\n", " print(f\" !! publishing to the SAME repo the artifacts come from. Fine\")\n", " print(f\" !! for a continuation; for a NEW trunk set hf_repo elsewhere.\")\n", " if bad:\n", " raise RuntimeError(\"incoherent config:\\n - \" + \"\\n - \".join(bad))\n", " print(\" config coherent\")\n", " return cfg\n", "\n", "\n", "def ensure_artifacts(cfg):\n", " \"\"\"\n", " Skipping a stage means its outputs come from the hub, NOT from local disk.\n", "\n", " Colab wipes local disk every session, so `run_stage1=False` cannot mean\n", " \"the maps are already in /content\" -- it has to mean \"fetch the maps\".\n", " Same for the consensus targets. Without this, skipping a stage either\n", " crashes on a missing file or, worse, silently re-derives an artifact that\n", " must NOT be re-derived: a refit alignment map lands in a different frame\n", " than the 66 target files that were built with the original, and nothing in\n", " the loss reveals it.\n", " \"\"\"\n", " P = paths(cfg)\n", " line(\"ARTIFACTS\")\n", "\n", " if not cfg.run_stage1:\n", " mp = f\"{P['maps']}/alignment_maps.pt\"\n", " if os.path.exists(mp):\n", " print(f\" maps: local {mp}\")\n", " else:\n", " src = hf_hub_download(cfg.artifact_repo, \"maps/alignment_maps.pt\")\n", " shutil.copy(src, mp)\n", " print(f\" maps: fetched {cfg.artifact_repo}/maps/alignment_maps.pt\")\n", " try:\n", " fr = hf_hub_download(cfg.artifact_repo, \"maps/fit_report.json\")\n", " shutil.copy(fr, f\"{P['maps']}/fit_report.json\")\n", " except Exception:\n", " pass\n", " print(\" (NOT refitting: every consensus target was built with THIS map;\")\n", " print(\" a new fit would sit in a different frame with no loss signal.)\")\n", "\n", " if not cfg.run_stage2:\n", " want = sorted(set(usable_chunks(cfg)) | set(cfg.holdout_chunks))\n", " have, pulled, miss = [], [], []\n", " for c in want:\n", " dst = f\"{P['targets']}/consensus_{c:03d}.pt\"\n", " if os.path.exists(dst):\n", " have.append(c); continue\n", " try:\n", " src = hf_hub_download(cfg.targets_repo, f\"consensus_{c:03d}.pt\",\n", " repo_type=\"dataset\")\n", " if os.path.islink(dst) or os.path.exists(dst):\n", " os.remove(dst)\n", " os.symlink(src, dst) # no second 49GB copy\n", " pulled.append(c)\n", " except Exception:\n", " miss.append(c)\n", " if (len(pulled) % 10 == 0) and pulled:\n", " print(f\" targets: {len(have)+len(pulled)}/{len(want)} ...\")\n", " print(f\" targets: {len(have)} local + {len(pulled)} fetched \"\n", " f\"from {cfg.targets_repo}\")\n", " if miss:\n", " raise RuntimeError(\n", " f\"targets missing for chunks {miss} in {cfg.targets_repo}. \"\n", " f\"Either build them (run_stage2=True, which needs the expert \"\n", " f\"shards) or drop those chunks from the run.\")\n", " print()\n", "\n", "\n", "def preflight(cfg):\n", " \"\"\"Hard-check the allowance before anything expensive starts.\"\"\"\n", " line(\"PREFLIGHT — disk / RAM / GPU vs the plan\")\n", " P = paths(cfg)\n", " disk = free_gb(P[\"root\"])\n", " s = src0(cfg)\n", " n_keep = len(usable_chunks(cfg)) + len(cfg.holdout_chunks)\n", " rows = n_keep * s[\"chunk_rows\"]\n", " targets_gb = rows * cfg.output_dim * 2 / 1e9\n", " transient_gb = len(cfg.experts) * 1.536\n", " caps_gb = s[\"n_chunks\"] * 0.120\n", " need = targets_gb + caps_gb + transient_gb + 10.0\n", " print(f\" source on HF : {s['n_chunks'] * len(cfg.experts) * 1.536:.0f} GB expert shards \"\n", " f\"(streamed one chunk at a time, deleted after)\")\n", " print(f\" disk free : {disk:.1f} GB | stage-2 peak need ≈ {need:.1f} GB \"\n", " f\"(targets {targets_gb:.1f} + captions {caps_gb:.1f} + transient {transient_gb:.1f})\")\n", " if disk < need:\n", " raise RuntimeError(\n", " f\"DISK: {disk:.1f} GB free, need ≈ {need:.1f} GB. Free space, reduce chunks, \"\n", " f\"or set push_targets=True and drop consensus locally after each push.\")\n", " try:\n", " import psutil\n", " ram = psutil.virtual_memory().total / 1e9\n", " except Exception:\n", " ram = float(\"nan\")\n", " ram_need = (rows * 100 * 2 + rows * 8 + rows * cfg.output_dim * 2) / 1e9\n", " print(f\" RAM total : {ram:.1f} GB | ram_resident need ≈ {ram_need:.1f} GB \"\n", " f\"(ragged tokens + offsets + fp16 targets)\")\n", " if cfg.ram_resident and ram == ram and ram_need > 0.7 * ram:\n", " print(f\" !! ram_resident wants {ram_need:.1f} GB of {ram:.1f}. \"\n", " f\"Set ram_resident=False to stream per chunk from disk instead.\")\n", " if DEVICE == \"cuda\":\n", " g = torch.cuda.get_device_properties(0).total_memory / 1e9\n", " print(f\" GPU : {torch.cuda.get_device_name()} {g:.1f} GB | \"\n", " f\"batch {cfg.batch_size} -> {cfg.batch_size} InfoNCE negatives\")\n", " print(f\" plan : {rows:,} rows, {rows // cfg.batch_size:,} steps/epoch \"\n", " f\"x {cfg.epochs} = {rows // cfg.batch_size * cfg.epochs:,} steps\")\n", "\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "def hf_token() -> Optional[str]:\n", " t = os.environ.get(\"HF_TOKEN\")\n", " if t:\n", " return t\n", " try:\n", " from google.colab import userdata\n", " return userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " return None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BACKUP — a Colab cull must cost minutes, not the run\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class Backup:\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok, self.last = cfg, None, False, 0.0\n", " if not cfg.hf_push:\n", " return\n", " tok = hf_token()\n", " if not tok:\n", " print(\" [backup] no HF_TOKEN — LOCAL ONLY. A cull will lose the run.\")\n", " return\n", " try:\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True, private=True)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [backup] -> {cfg.hf_repo} (private)\")\n", " except Exception as e:\n", " print(f\" [backup] disabled: {type(e).__name__}: {str(e)[:100]}\")\n", "\n", " def push(self, force: bool = False, msg: str = \"checkpoint\"):\n", " if not self.ok:\n", " return\n", " if not force and (time.time() - self.last) / 60 < self.cfg.push_every_min:\n", " return\n", " P = paths(self.cfg)\n", " try:\n", " for folder, dest in ((P[\"ckpt\"], \"checkpoints\"), (P[\"tb\"], \"tensorboard\"),\n", " (P[\"maps\"], \"maps\"), (P[\"config\"], \"config\")):\n", " if os.path.isdir(folder) and os.listdir(folder):\n", " self.api.upload_folder(folder_path=folder, path_in_repo=dest,\n", " repo_id=self.cfg.hf_repo,\n", " commit_message=f\"{msg} ({dest})\")\n", " self.last = time.time()\n", " print(f\" [backup] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [backup] push failed: {type(e).__name__}: {str(e)[:100]}\")\n", "\n", " def pull_latest(self) -> Optional[str]:\n", " \"\"\"Recover state.pt after a cull.\"\"\"\n", " if not self.ok:\n", " return None\n", " try:\n", " p = hf_hub_download(self.cfg.hf_repo, \"checkpoints/state.pt\",\n", " token=hf_token(), local_dir=paths(self.cfg)[\"root\"])\n", " print(f\" [backup] recovered {p}\")\n", " return p\n", " except Exception:\n", " return None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 0 — PARITY GATE\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def stage0_parity(cfg) -> str:\n", " \"\"\"\n", " Which caption field was embedded, and is row i of _XXX.pt caption i?\n", " The manifest names three fields and does not say which was used. If the stored\n", " vectors came from caption_llava and the student trains on caption_llava_short,\n", " every target is silently wrong. Re-embed with the real reference model, demand\n", " cos ~ 1.0. Nothing downstream runs until this passes.\n", " \"\"\"\n", " from transformers import AutoModel, AutoTokenizer\n", " line(\"STAGE 0 — PARITY GATE (caption field + row alignment)\")\n", " stored = load_expert_chunk(cfg, cfg.ref_expert, cfg.parity_chunk)[: cfg.parity_n].float()\n", " raw = json.load(open(fetch(cfg, f\"captions_{cfg.parity_chunk:03d}.json\")))\n", " if isinstance(raw, dict):\n", " fields = {k: list(v)[: cfg.parity_n] for k, v in raw.items()\n", " if k in cfg.caption_field_candidates}\n", " elif raw and isinstance(raw[0], dict):\n", " fields = {k: [r[k] for r in raw[: cfg.parity_n]]\n", " for k in raw[0] if k in cfg.caption_field_candidates}\n", " else:\n", " fields = {\"(flat)\": list(raw[: cfg.parity_n])}\n", " print(f\" stored rows {tuple(stored.shape)} | fields {list(fields)}\")\n", "\n", " tok = AutoTokenizer.from_pretrained(cfg.ref_hf_name)\n", " mdl = AutoModel.from_pretrained(cfg.ref_hf_name).to(DEVICE).eval()\n", " best, best_cos = None, -1.0\n", " for f, texts in fields.items():\n", " with torch.no_grad():\n", " inp = tok(list(texts), max_length=512, padding=True, truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h = mdl(**inp).last_hidden_state\n", " m = inp.attention_mask.unsqueeze(-1).float()\n", " pooled = ((h * m).sum(1) / m.sum(1).clamp(min=1)).float().cpu()\n", " cos = F.cosine_similarity(pooled, stored, dim=-1)\n", " print(f\" {f:22s} cos mean {cos.mean():.6f} min {cos.min():.6f}\")\n", " if cos.mean().item() > best_cos:\n", " best, best_cos = f, cos.mean().item()\n", " del mdl; gc.collect(); torch.cuda.empty_cache()\n", " if best_cos < cfg.parity_min_cos:\n", " raise RuntimeError(\n", " f\"PARITY GATE FAIL: best field '{best}' only reaches cos {best_cos:.6f} \"\n", " f\"(need >= {cfg.parity_min_cos}). Either the field is not among \"\n", " f\"{cfg.caption_field_candidates}, row order differs, or the extraction used \"\n", " f\"different pooling/truncation. DO NOT SPEND GPU TIME until this resolves.\")\n", " print(f\" GATE PASS: field = '{best}' at cos {best_cos:.6f}\")\n", " return best\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 1 — GLOBAL WHITENED PROCRUSTES (out-of-sample reported)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def symmetric_inv_sqrt(cov: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:\n", " ev, evec = torch.linalg.eigh(cov.double())\n", " return (evec @ torch.diag(torch.clamp(ev, min=eps).rsqrt()) @ evec.T).float()\n", "\n", "\n", "def fit_map(S: torch.Tensor, T: torch.Tensor) -> Dict[str, torch.Tensor]:\n", " N = S.shape[0]\n", " s_mean, t_mean = S.mean(0, keepdim=True), T.mean(0, keepdim=True)\n", " Sc, Tc = S - s_mean, T - t_mean\n", " s_w = symmetric_inv_sqrt((Sc.T @ Sc) / max(N - 1, 1))\n", " t_w = symmetric_inv_sqrt((Tc.T @ Tc) / max(N - 1, 1))\n", " U, _, Vt = torch.linalg.svd(\n", " (F.normalize(Tc @ t_w, dim=-1).T @ F.normalize(Sc @ s_w, dim=-1)).double(),\n", " full_matrices=False)\n", " return {\"rotation\": (U @ Vt).float(), \"source_mean\": s_mean.squeeze(0),\n", " \"target_mean\": t_mean.squeeze(0), \"source_whitener\": s_w,\n", " \"target_whitener\": t_w, \"target_unwhitener\": torch.linalg.pinv(t_w)}\n", "\n", "\n", "def apply_map(emb: torch.Tensor, a) -> torch.Tensor:\n", " x = (emb.float() - a[\"source_mean\"]) @ a[\"source_whitener\"]\n", " return (x @ a[\"rotation\"].T) @ a[\"target_unwhitener\"]\n", "\n", "\n", "def score_map(S, T, a) -> Dict[str, float]:\n", " Sw = F.normalize((S - a[\"source_mean\"]) @ a[\"source_whitener\"], dim=-1)\n", " Tw = F.normalize((T - a[\"target_mean\"]) @ a[\"target_whitener\"], dim=-1)\n", " cos = F.cosine_similarity(Sw @ a[\"rotation\"].T, Tw, dim=-1).mean().item()\n", " n = min(2000, S.shape[0])\n", " sim = F.normalize(apply_map(S[:n], a), dim=-1) @ F.normalize(T[:n], dim=-1).T\n", " return {\"cos\": cos, \"r1\": (sim.argmax(1) == torch.arange(n)).float().mean().item(),\n", " \"n\": int(S.shape[0]), \"chance\": 1.0 / n}\n", "\n", "\n", "def stage1_fit(cfg, bk: \"Backup\"):\n", " line(\"STAGE 1 — GLOBAL ALIGNMENT (stratified fit, OUT-OF-SAMPLE report)\")\n", " P = paths(cfg)\n", " mp = f\"{P['maps']}/alignment_maps.pt\"\n", " if os.path.exists(mp):\n", " print(\" maps exist, loading\"); return torch.load(mp, weights_only=False)\n", "\n", " g = torch.Generator().manual_seed(cfg.fit_seed)\n", " fit = {e: [] for e in cfg.experts}\n", " for c in cfg.fit_chunks:\n", " idx = None\n", " for e in cfg.experts:\n", " X = load_expert_chunk(cfg, e, c)\n", " if idx is None:\n", " idx = torch.randperm(X.shape[0], generator=g)[: cfg.fit_rows_per_chunk]\n", " fit[e].append(X[idx].float()); del X; gc.collect()\n", " drop_shard(cfg, f\"{e}_{c:03d}.pt\")\n", " print(f\" fit chunk {c:03d}: {len(idx)} random rows\")\n", " fit = {e: torch.cat(v) for e, v in fit.items()}\n", " N = fit[cfg.ref_expert].shape[0]\n", " print(f\" fit set {N} rows, d=768 -> N/d = {N/768:.1f}\")\n", "\n", " hold = {e: [] for e in cfg.experts}\n", " for c in cfg.holdout_chunks:\n", " for e in cfg.experts:\n", " X = load_expert_chunk(cfg, e, c)\n", " hold[e].append(X[: cfg.fit_rows_per_chunk].float()); del X; gc.collect()\n", " hold = {e: torch.cat(v) for e, v in hold.items()}\n", "\n", " maps, report, T = {}, {}, fit[cfg.ref_expert]\n", " for e in cfg.experts:\n", " a = fit_map(fit[e], T)\n", " ins, oos = score_map(fit[e], T, a), score_map(hold[e], hold[cfg.ref_expert], a)\n", " maps[e], report[e] = a, {\"in_sample\": ins, \"out_of_sample\": oos}\n", " tag = \" (ref: must read ~1.0)\" if e == cfg.ref_expert else \"\"\n", " print(f\" {e:9s} cos in {ins['cos']:.4f} / OUT {oos['cos']:.4f} \"\n", " f\"R@1 in {ins['r1']:.4f} / OUT {oos['r1']:.4f} \"\n", " f\"(chance {oos['chance']:.5f}){tag}\")\n", " print(\" READ THE 'OUT' COLUMN. A 768x768 rotation is 294,528 free parameters;\")\n", " print(\" at low N/d the in-sample cosine reproduces strong numbers from nothing.\")\n", " torch.save(maps, mp)\n", " json.dump(report, open(f\"{P['maps']}/fit_report.json\", \"w\"), indent=2)\n", " bk.push(force=True, msg=\"stage1 alignment maps\")\n", " return maps\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 2 — CONSENSUS TARGETS\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def stage2_targets(cfg, maps, bk: \"Backup\") -> List[int]:\n", " line(\"STAGE 2 — CONSENSUS TARGETS (fp16, per chunk, resumable)\")\n", " P = paths(cfg)\n", " lp = f\"{P['targets']}/ledger.json\"\n", " ledger = json.load(open(lp)) if os.path.exists(lp) else {}\n", " want = sorted(set(usable_chunks(cfg)) | set(cfg.holdout_chunks))\n", " print(f\" {len(want)} chunks with all {len(cfg.experts)} experts | \"\n", " f\"streaming {len(want)*len(cfg.experts)*1.536:.0f} GB through \"\n", " f\"{free_gb(P['root']):.0f} GB of free disk\")\n", " tapi = None\n", " if cfg.push_targets and bk.ok:\n", " try:\n", " create_repo(cfg.targets_repo, token=hf_token(), exist_ok=True,\n", " private=True, repo_type=\"dataset\")\n", " tapi = HfApi(token=hf_token())\n", " print(f\" targets -> {cfg.targets_repo} (dataset, private)\")\n", " except Exception as e:\n", " print(f\" target push disabled: {type(e).__name__}: {str(e)[:80]}\")\n", " for c in want:\n", " k, out_p = f\"{c:03d}\", f\"{P['targets']}/consensus_{c:03d}.pt\"\n", " if ledger.get(k) and os.path.exists(out_p):\n", " continue\n", " if free_gb(P[\"root\"]) < cfg.disk_floor_gb:\n", " raise RuntimeError(f\"DISK FLOOR: {free_gb(P['root']):.1f} GB free at chunk {k}. \"\n", " f\"Push and drop earlier consensus files, then resume.\")\n", " acc, n = None, None\n", " for e in cfg.experts:\n", " X = load_expert_chunk(cfg, e, c).float()\n", " if n is None:\n", " n = X.shape[0]\n", " elif X.shape[0] != n:\n", " raise RuntimeError(f\"chunk {k}: {e} has {X.shape[0]} rows, expected {n}\")\n", " A = apply_map(X, maps[e])\n", " acc = A if acc is None else acc + A\n", " del X, A; gc.collect()\n", " drop_shard(cfg, f\"{e}_{c:03d}.pt\")\n", " purge_hf_cache(cfg)\n", " cons = F.normalize(acc / len(cfg.experts), dim=-1).half()\n", " torch.save(cons, out_p)\n", " er = effective_rank(cons[:4000].float())\n", " ledger[k] = {\"rows\": int(cons.shape[0]), \"target_erank\": er, \"ts\": time.time()}\n", " json.dump(ledger, open(lp, \"w\"), indent=2)\n", " if tapi is not None:\n", " try:\n", " tapi.upload_file(path_or_fileobj=out_p,\n", " path_in_repo=f\"consensus_{k}.pt\",\n", " repo_id=cfg.targets_repo, repo_type=\"dataset\",\n", " commit_message=f\"consensus chunk {k}\")\n", " except Exception as ex:\n", " print(f\" target push failed for {k}: {str(ex)[:70]}\")\n", " print(f\" chunk {k}: {cons.shape[0]} targets | TARGET erank {er:.1f}/768 | \"\n", " f\"disk free {free_gb(P['root']):.0f} GB\")\n", " del acc, cons; gc.collect()\n", " eranks = [v[\"target_erank\"] for v in ledger.values() if \"target_erank\" in v]\n", " if eranks:\n", " print(f\" consensus target erank: mean {np.mean(eranks):.1f} \"\n", " f\"min {min(eranks):.1f} max {max(eranks):.1f} of 768\")\n", " print(\" (v1's STUDENT read 23.6 — compare against this to tell 'student\")\n", " print(\" collapsed' from 'student faithfully matched a low-rank target')\")\n", " bk.push(force=True, msg=\"stage2 target ledger\")\n", " return want\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STUDENT\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " \"\"\"Standalone caption encoder. No experts at inference. No bank.\"\"\"\n", "\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\", grad_checkpointing=False):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.grad_checkpointing = grad_checkpointing\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " if self.grad_checkpointing and self.training:\n", " for layer in self.encoder.layers:\n", " x = torch.utils.checkpoint.checkpoint(\n", " layer, x, None, kpm, use_reentrant=False)\n", " else:\n", " x = self.encoder(x, src_key_padding_mask=kpm)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# LOSS / GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def infonce(a, b, temperature=0.07):\n", " logits = (a @ b.T) / temperature\n", " lab = torch.arange(logits.shape[0], device=logits.device)\n", " loss = (F.cross_entropy(logits, lab) + F.cross_entropy(logits.T, lab)) / 2\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == lab).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " d = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (d * d).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float32)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2\n", " f = math.factorial(V - 1)\n", " return ((-1.0) ** V) / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "def cv_loss(emb, target=0.084, n_samples=16):\n", " B = emb.shape[0]\n", " if B < 5:\n", " return torch.zeros((), device=emb.device)\n", " s = torch.stack([torch.sqrt(F.relu(cayley_menger_vol2(\n", " emb[torch.randperm(B, device=emb.device)[:5]].unsqueeze(0))[0]) + 1e-12)\n", " for _ in range(n_samples)])\n", " return (s.std() / (s.mean() + 1e-8) - target).abs()\n", "\n", "\n", "@torch.no_grad()\n", "def cv_metric(emb, n=200):\n", " v = [float(torch.sqrt(F.relu(cayley_menger_vol2(\n", " emb[torch.randperm(emb.shape[0], device=emb.device)[:5]].unsqueeze(0))[0])\n", " + 1e-12).item()) for _ in range(n)]\n", " a = np.array([x for x in v if x > 0])\n", " return float(a.std() / (a.mean() + 1e-8)) if len(a) >= 10 else 0.0\n", "\n", "\n", "@torch.no_grad()\n", "def frame_fit_gauge(E: torch.Tensor, T: torch.Tensor, n_pairs: int = 2500) -> Dict[str, float]:\n", " \"\"\"\n", " Standing rider: judge relational objectives with a frame fit or they read as false\n", " floors. MSE anchors the frame here and the consensus aligns to a REFERENCE MEMBER,\n", " so a rotation should buy ~nothing. If it buys a lot, the frame is NOT pinned and\n", " this model needs a shipped rotation after all. Held-out split, fp64.\n", " \"\"\"\n", " N = E.shape[0]\n", " k = min(n_pairs, N // 2)\n", " if k < 64:\n", " return {\"skipped\": True}\n", " perm = torch.randperm(N, generator=torch.Generator().manual_seed(0))\n", " fit_i, hold_i = perm[:k], perm[k:]\n", " U, _, Vt = torch.linalg.svd(E[fit_i].double().T @ T[fit_i].double(), full_matrices=False)\n", " Er = F.normalize((E.double() @ (U @ Vt)).float(), dim=-1)\n", " m = min(2000, len(hold_i))\n", " hi = hold_i[:m]\n", " sim = Er[hi] @ T[hi].T\n", " return {\"r1_after_rotation\": (sim.argmax(1) == torch.arange(m)).float().mean().item(),\n", " \"cos_after_rotation\": F.cosine_similarity(Er[hi], T[hi], dim=-1).mean().item(),\n", " \"n_heldout\": int(m)}\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class RamStore:\n", " \"\"\"\n", " Everything resident in system RAM: ragged uint16 tokens + fp16 targets.\n", "\n", " On the Pro+ box this is 48.8 GB of 176.9 — so the training loop does ZERO disk\n", " I/O and needs no DataLoader workers. Ragged storage (flat token buffer + offsets)\n", " keeps dynamic padding available at ~5.6 GB instead of the 14 GB a fixed 256-token\n", " matrix would cost, and captions average ~100 tokens against a 256 ceiling.\n", " \"\"\"\n", "\n", " def __init__(self, cfg, chunks: List[int], tokenizer, tag=\"\"):\n", " self.cfg, self.tok = cfg, tokenizer\n", " self.pad = tokenizer.pad_token_id\n", " flat, offs, tgts, total = [], [0], [], 0\n", " for c in chunks:\n", " caps = load_captions_chunk(cfg, c)\n", " t = torch.load(f\"{paths(cfg)['targets']}/consensus_{c:03d}.pt\",\n", " weights_only=True, map_location=\"cpu\")\n", " n = min(len(caps), t.shape[0])\n", " caps, t = caps[:n], t[:n]\n", " for i in range(0, n, 20000):\n", " enc = tokenizer(caps[i:i + 20000], max_length=cfg.max_tokens,\n", " truncation=True, padding=False)[\"input_ids\"]\n", " for ids in enc:\n", " flat.append(np.asarray(ids, dtype=np.uint16))\n", " total += len(ids)\n", " offs.append(total)\n", " tgts.append(t)\n", " print(f\" chunk {c:03d}: {n:,} rows | flat tokens {total/1e6:.1f}M\")\n", " del caps, t; gc.collect()\n", " self.flat = np.concatenate(flat) if flat else np.zeros(0, np.uint16)\n", " del flat; gc.collect()\n", " self.offs = np.asarray(offs, dtype=np.int64)\n", " self.tgt = torch.cat(tgts)\n", " del tgts; gc.collect()\n", " self.n = len(self.offs) - 1\n", " self.lens = (self.offs[1:] - self.offs[:-1]).astype(np.int32)\n", " gb = (self.flat.nbytes + self.offs.nbytes + self.tgt.numel() * 2) / 1e9\n", " mean_len = total / max(self.n, 1)\n", " q = np.percentile(self.lens, [50, 90, 99, 100]).astype(int)\n", " print(f\" RamStore{tag}: {self.n:,} rows | {gb:.1f} GB RAM | \"\n", " f\"mean {mean_len:.0f} tokens (ceiling {cfg.max_tokens})\")\n", " print(f\" length p50 {q[0]} | p90 {q[1]} | p99 {q[2]} | max {q[3]}\"\n", " f\" -- unbucketed, a batch pads to the BATCH MAX, i.e. ~{q[3]}\")\n", "\n", " def plan_batches(self, batch_size, seed, window_batches=64, bucket=True):\n", " \"\"\"\n", " Deterministic batch plan for one epoch. Returns a list of index arrays.\n", "\n", " With bucket=True: shuffle, cut into windows of window_batches*batch_size,\n", " sort each window by length, slice into batches, then shuffle the BATCH ORDER.\n", " Batches end up length-homogeneous (so padding is near-free) while batch\n", " composition stays random across the window and the model never sees the\n", " corpus in length order. Deterministic in (seed), so a resume mid-epoch\n", " regenerates the identical plan and the stored batch index stays valid.\n", " \"\"\"\n", " rng = np.random.default_rng(seed)\n", " perm = rng.permutation(self.n)\n", " if not bucket:\n", " n_full = self.n // batch_size\n", " return [perm[i * batch_size:(i + 1) * batch_size] for i in range(n_full)]\n", " W = batch_size * max(window_batches, 1)\n", " batches = []\n", " for i in range(0, self.n, W):\n", " win = perm[i:i + W]\n", " win = win[np.argsort(self.lens[win], kind=\"stable\")]\n", " for j in range(0, len(win) - batch_size + 1, batch_size):\n", " batches.append(win[j:j + batch_size])\n", " rng.shuffle(batches)\n", " return batches\n", "\n", " def __len__(self):\n", " return self.n\n", "\n", " def batch(self, idx: np.ndarray):\n", " \"\"\"Gather a batch with DYNAMIC padding to the batch max.\"\"\"\n", " seqs = [self.flat[self.offs[i]:self.offs[i + 1]] for i in idx]\n", " L = max(len(s) for s in seqs)\n", " ids = np.full((len(seqs), L), self.pad, dtype=np.int64)\n", " am = np.zeros((len(seqs), L), dtype=np.int64)\n", " for r, s in enumerate(seqs):\n", " ids[r, :len(s)] = s\n", " am[r, :len(s)] = 1\n", " return (torch.from_numpy(ids), torch.from_numpy(am),\n", " self.tgt[torch.from_numpy(idx)])\n", "\n", "\n", "class ChunkPairs(torch.utils.data.Dataset):\n", " \"\"\"Disk-streaming fallback when ram_resident=False.\"\"\"\n", " def __init__(self, cfg, chunk, tokenizer):\n", " self.caps = load_captions_chunk(cfg, chunk)\n", " self.tgt = torch.load(f\"{paths(cfg)['targets']}/consensus_{chunk:03d}.pt\",\n", " weights_only=True, map_location=\"cpu\")\n", " n = min(len(self.caps), self.tgt.shape[0])\n", " self.caps, self.tgt = self.caps[:n], self.tgt[:n]\n", " self.tok, self.max_tokens = tokenizer, cfg.max_tokens\n", "\n", " def __len__(self):\n", " return len(self.caps)\n", "\n", " def __getitem__(self, i):\n", " return self.caps[i], self.tgt[i]\n", "\n", " def collate(self, batch):\n", " texts, tg = zip(*batch)\n", " enc = self.tok(list(texts), max_length=self.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\") # DYNAMIC\n", " return enc[\"input_ids\"], enc[\"attention_mask\"], torch.stack(tg)\n", "\n", "\n", "@torch.no_grad()\n", "def evaluate(student, source, cap=5000, batch=512) -> Dict[str, float]:\n", " student.eval()\n", " E, T = [], []\n", " if isinstance(source, RamStore):\n", " for i in range(0, min(cap, len(source)), batch):\n", " ids, am, tg = source.batch(np.arange(i, min(i + batch, len(source))))\n", " E.append(student(ids.to(DEVICE), am.to(DEVICE)).float().cpu())\n", " T.append(tg.float())\n", " else:\n", " for ids, am, tg in source:\n", " E.append(student(ids.to(DEVICE), am.to(DEVICE)).float().cpu())\n", " T.append(tg.float())\n", " if sum(x.shape[0] for x in E) >= cap:\n", " break\n", " E, T = torch.cat(E), F.normalize(torch.cat(T), dim=-1)\n", " n = min(2000, E.shape[0])\n", " sim = E[:n] @ T[:n].T\n", " ss = E[:n] @ E[:n].T\n", " ss.fill_diagonal_(0)\n", " out = {\"mimicry_r1\": (sim.argmax(1) == torch.arange(n)).float().mean().item(),\n", " \"cos_to_target\": F.cosine_similarity(E, T, dim=-1).mean().item(),\n", " \"self_cos\": ss.mean().item(),\n", " \"erank\": effective_rank(E),\n", " \"cv\": cv_metric(E[:2000].to(DEVICE)),\n", " \"n\": int(E.shape[0])}\n", " out.update({f\"frame_{k}\": v for k, v in frame_fit_gauge(E, T).items()})\n", " student.train()\n", " return out\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE 3 — TRAIN (cull-proof)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def vram_probe(cfg, student):\n", " \"\"\"\n", " One forward+backward at the WORST case (full batch at the pad ceiling) before\n", " any data is loaded. With bucketing the longest bucket really is a full batch at\n", " max_tokens, so this is the case that decides whether the run survives -- and it\n", " is far cheaper to discover here than 20 minutes into a RamStore build.\n", " \"\"\"\n", " if DEVICE != \"cuda\":\n", " return\n", " line(\"VRAM PROBE - worst-case batch before spending time on data\")\n", " torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats()\n", " total = torch.cuda.get_device_properties(0).total_memory / 1e9\n", " ids = torch.randint(1, 30000, (cfg.batch_size, cfg.max_tokens), device=DEVICE)\n", " am = torch.ones_like(ids)\n", " tgt = F.normalize(torch.randn(cfg.batch_size, cfg.output_dim, device=DEVICE), dim=-1)\n", " opt = torch.optim.Adam(student.parameters(), lr=1e-9)\n", " try:\n", " student.train()\n", " with torch.amp.autocast(\"cuda\", enabled=cfg.amp):\n", " emb = student(ids, am)\n", " emb = emb.float()\n", " loss = infonce(emb, tgt, cfg.nce_temperature)[0] + F.mse_loss(emb, tgt)\n", " loss.backward()\n", " opt.zero_grad(set_to_none=True)\n", " peak = torch.cuda.max_memory_allocated() / 1e9\n", " print(f\" batch {cfg.batch_size} x L {cfg.max_tokens} \"\n", " f\"(checkpointing={cfg.grad_checkpointing}) -> peak {peak:.1f} GB \"\n", " f\"of {total:.1f} GB\")\n", " if peak > 0.85 * total:\n", " print(\" !! within 15% of the limit. Reduce batch_size or max_tokens,\")\n", " print(\" !! or set grad_checkpointing=True, before starting the run.\")\n", " else:\n", " ok = (total - peak)\n", " print(f\" PASS - {ok:.1f} GB headroom\")\n", " except torch.cuda.OutOfMemoryError:\n", " torch.cuda.empty_cache()\n", " raise RuntimeError(\n", " f\"VRAM PROBE FAILED at batch {cfg.batch_size} x L {cfg.max_tokens} \"\n", " f\"(checkpointing={cfg.grad_checkpointing}). Options, cheapest first: \"\n", " f\"grad_checkpointing=True; lower max_tokens (corpus mean is ~48); \"\n", " f\"halve batch_size (costs InfoNCE negatives). Nothing was loaded, so \"\n", " f\"changing the config and re-running is quick.\")\n", " finally:\n", " del ids, am, tgt, opt\n", " torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats()\n", "\n", "\n", "def save_state(cfg, path, student, opt, sched, scaler, step, epoch, chunk_i,\n", " order, best, n_rows=None):\n", " torch.save({\"run_name\": cfg.run_name, \"n_rows\": n_rows,\n", " \"model\": student.state_dict(), \"opt\": opt.state_dict(),\n", " \"sched\": sched.state_dict(), \"scaler\": scaler.state_dict(),\n", " \"step\": step, \"epoch\": epoch, \"chunk_i\": chunk_i, \"order\": order,\n", " \"best\": best, \"config\": asdict(cfg),\n", " \"rng\": {\"torch\": torch.get_rng_state(), \"np\": np.random.get_state(),\n", " \"py\": random.getstate()}}, path)\n", "\n", "\n", "def stage3_train(cfg, chunks: List[int], bk: \"Backup\"):\n", " from transformers import AutoTokenizer\n", " line(\"STAGE 3 — TRAIN\")\n", " P = paths(cfg)\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.ref_hf_name)\n", " json.dump(asdict(cfg), open(f\"{P['config']}/config.json\", \"w\"), indent=2, default=str)\n", "\n", " student = CaptionEncoder(\n", " vocab_size=tok.vocab_size, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=cfg.dropout,\n", " pad_token_id=tok.pad_token_id, pooling=cfg.pooling,\n", " grad_checkpointing=cfg.grad_checkpointing).to(DEVICE)\n", " n_par = sum(p.numel() for p in student.parameters())\n", " train_chunks = [c for c in chunks if c not in cfg.holdout_chunks]\n", " rows = len(train_chunks) * src0(cfg)[\"chunk_rows\"]\n", " spe = rows // cfg.batch_size\n", " total = spe * cfg.epochs\n", " print(f\" {cfg.run_name}: {n_par:,} params ({n_par/109_482_240:.2f}x bert-base)\")\n", " print(f\" {cfg.n_layers}L {cfg.d_model}d {cfg.n_heads}h ff{cfg.d_ff} pool={cfg.pooling}\")\n", " print(f\" {len(train_chunks)} chunks ≈ {rows:,} rows | {spe:,} steps/ep x \"\n", " f\"{cfg.epochs} = {total:,} steps @ batch {cfg.batch_size}\")\n", " print(f\" loss = {cfg.nce_weight}*InfoNCE(T={cfg.nce_temperature}) + \"\n", " f\"{cfg.mse_weight}*MSE + {cfg.cv_weight}*CV [champion consensus_nce_mse]\")\n", "\n", " if cfg.vram_probe:\n", " vram_probe(cfg, student)\n", "\n", " opt = torch.optim.Adam(student.parameters(), lr=cfg.lr) # pure Adam, no wd\n", " sched = torch.optim.lr_scheduler.SequentialLR(\n", " opt, [torch.optim.lr_scheduler.LinearLR(opt, 0.01, 1.0, cfg.warmup_steps),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " opt, T_max=max(total - cfg.warmup_steps, 1), eta_min=cfg.min_lr)],\n", " milestones=[cfg.warmup_steps])\n", " scaler = torch.amp.GradScaler(enabled=cfg.amp and DEVICE == \"cuda\")\n", " tb = SummaryWriter(log_dir=f\"{P['tb']}/{cfg.run_name}\")\n", " tb.add_text(\"config\", f\"```json\\n{json.dumps(asdict(cfg), indent=2, default=str)}\\n```\")\n", " if os.path.exists(f\"{P['maps']}/fit_report.json\"):\n", " tb.add_text(\"alignment/fit_report\",\n", " f\"```json\\n{open(f'{P['maps']}/fit_report.json').read()}\\n```\")\n", "\n", " # expected row count, known before any store is built: the identity gate\n", " # needs something to compare against and must run BEFORE the RamStore is\n", " # assembled (that build is the expensive part this failure wasted).\n", " n_rows_now = len(train_chunks) * src0(cfg)[\"chunk_rows\"]\n", "\n", " step, ep0, chunk_i0, best = 0, 0, 0, -1.0\n", " st, order = None, None\n", " sp = f\"{P['ckpt']}/state.pt\"\n", " if cfg.fresh_start:\n", " print(\" fresh_start=True -- ignoring any local or remote checkpoint\")\n", " if cfg.resume and not cfg.fresh_start:\n", " if not os.path.exists(sp):\n", " bk.pull_latest()\n", " alt = f\"{P['root']}/checkpoints/state.pt\"\n", " if os.path.exists(alt) and alt != sp:\n", " shutil.copy(alt, sp)\n", " if os.path.exists(sp):\n", " st = torch.load(sp, weights_only=False, map_location=DEVICE)\n", " # IDENTITY GATE. A checkpoint is only resumable into the job that\n", " # produced it. Missing fields mean a pre-gate checkpoint, which is\n", " # exactly the v2-into-B case this was written for -- reject it.\n", " why = []\n", " if st.get(\"run_name\") != cfg.run_name:\n", " why.append(f\"run_name {st.get('run_name')!r} != {cfg.run_name!r}\")\n", " if st.get(\"n_rows\") is None:\n", " why.append(\"checkpoint predates the identity gate (no n_rows)\")\n", " elif st[\"n_rows\"] != n_rows_now:\n", " why.append(f\"n_rows {st['n_rows']:,} != {n_rows_now:,}\")\n", " if st.get(\"epoch\", 0) >= cfg.epochs:\n", " why.append(f\"already finished ({st['epoch']}/{cfg.epochs} epochs) \"\n", " f\"-- resuming would train ZERO steps\")\n", " if why and not cfg.allow_mismatched_resume:\n", " print(\" RESUME REJECTED:\")\n", " for w in why:\n", " print(f\" - {w}\")\n", " print(\" Starting fresh. Set allow_mismatched_resume=True to override,\")\n", " print(\" or fresh_start=True to skip the check entirely.\")\n", " st = None\n", " if os.path.exists(sp) and st is not None:\n", " student.load_state_dict(st[\"model\"]); opt.load_state_dict(st[\"opt\"])\n", " sched.load_state_dict(st[\"sched\"]); scaler.load_state_dict(st[\"scaler\"])\n", " step, ep0, chunk_i0, best = st[\"step\"], st[\"epoch\"], st[\"chunk_i\"], st[\"best\"]\n", " order = st.get(\"order\")\n", " try:\n", " torch.set_rng_state(st[\"rng\"][\"torch\"].cpu())\n", " np.random.set_state(st[\"rng\"][\"np\"]); random.setstate(st[\"rng\"][\"py\"])\n", " except Exception:\n", " pass\n", " print(f\" RESUMED at step {step:,} epoch {ep0+1} chunk_i {chunk_i0}\")\n", "\n", " print(\" building val store...\")\n", " if cfg.ram_resident:\n", " val_src = RamStore(cfg, [cfg.holdout_chunks[-1]], tok, tag=\" [val]\")\n", " else:\n", " vds = ChunkPairs(cfg, cfg.holdout_chunks[-1], tok)\n", " val_src = torch.utils.data.DataLoader(\n", " vds, batch_size=cfg.batch_size, shuffle=False,\n", " num_workers=cfg.num_workers, collate_fn=vds.collate)\n", "\n", " if cfg.ram_resident:\n", " print(\" building train store (one pass, then zero disk I/O)...\")\n", " train_src = RamStore(cfg, train_chunks, tok, tag=\" [train]\")\n", " N = len(train_src)\n", " spe = N // cfg.batch_size\n", " total = spe * cfg.epochs\n", " print(f\" {N:,} rows resident | {spe:,} steps/ep x {cfg.epochs} = {total:,} steps\")\n", "\n", " if step >= total or ep0 >= cfg.epochs:\n", " raise RuntimeError(\n", " f\"NOTHING TO TRAIN: resumed at step {step:,}/{total:,}, epoch \"\n", " f\"{ep0}/{cfg.epochs}. The epoch loop would be empty and this job \"\n", " f\"would report the PREVIOUS run's metrics as its own. Set \"\n", " f\"fresh_start=True for a new corpus, or raise cfg.epochs to continue.\")\n", "\n", " t0 = last_ck = time.time()\n", " for ep in range(ep0, cfg.epochs):\n", " if cfg.ram_resident:\n", " # deterministic bucketed plan; chunk_i doubles as the batch index, so a\n", " # mid-epoch resume regenerates the identical plan and lands on the same batch\n", " plan = train_src.plan_batches(cfg.batch_size, cfg.seed + ep,\n", " cfg.bucket_window, cfg.length_bucketing)\n", " if ep == ep0:\n", " spe = len(plan); total = spe * cfg.epochs\n", " bl = np.array([train_src.lens[b].max() for b in plan[:200]])\n", " print(f\" batch plan: {spe:,} batches/epoch | padded length \"\n", " f\"p50 {int(np.percentile(bl,50))} p90 {int(np.percentile(bl,90))} \"\n", " f\"max {int(bl.max())} (bucketing={cfg.length_bucketing})\")\n", " for ci in range(chunk_i0 if ep == ep0 else 0, len(plan)):\n", " ids, am, tg = train_src.batch(plan[ci])\n", " ids = ids.to(DEVICE, non_blocking=True)\n", " am = am.to(DEVICE, non_blocking=True)\n", " tgt = F.normalize(tg.to(DEVICE, non_blocking=True).float(), dim=-1)\n", " with torch.amp.autocast(\"cuda\", enabled=cfg.amp and DEVICE == \"cuda\"):\n", " emb = student(ids, am)\n", " emb = emb.float()\n", " l_nce, acc = infonce(emb, tgt, cfg.nce_temperature)\n", " l_mse = F.mse_loss(emb, tgt)\n", " loss = cfg.nce_weight * l_nce + cfg.mse_weight * l_mse\n", " l_cv = torch.zeros((), device=emb.device)\n", " if cfg.cv_weight > 0:\n", " l_cv = cv_loss(emb, cfg.cv_target)\n", " loss = loss + cfg.cv_weight * l_cv\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(opt)\n", " gn = torch.nn.utils.clip_grad_norm_(student.parameters(), cfg.grad_clip)\n", " scaler.step(opt); scaler.update()\n", " opt.zero_grad(set_to_none=True); sched.step()\n", " step += 1\n", "\n", " if step % cfg.log_every == 0:\n", " lr = opt.param_groups[0][\"lr\"]\n", " tb.add_scalar(\"train/loss\", loss.item(), step)\n", " tb.add_scalar(\"train/nce\", l_nce.item(), step)\n", " tb.add_scalar(\"train/mse\", l_mse.item(), step)\n", " tb.add_scalar(\"train/cv\", float(l_cv), step)\n", " tb.add_scalar(\"train/batch_acc\", acc, step)\n", " tb.add_scalar(\"train/lr\", lr, step)\n", " tb.add_scalar(\"train/grad_norm\", float(gn), step)\n", " tb.add_scalar(\"train/tokens_per_seq\", ids.shape[1], step)\n", " print(f\" e{ep+1} {step:>7,}/{total:,} loss {loss.item():.4f} \"\n", " f\"nce {l_nce.item():.4f} mse {l_mse.item():.5f} acc {acc:.3f} \"\n", " f\"lr {lr:.2e} L{ids.shape[1]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.eval_every == 0:\n", " m = evaluate(student, val_src)\n", " for k, v in m.items():\n", " if isinstance(v, (int, float)):\n", " tb.add_scalar(f\"val/{k}\", v, step)\n", " for nm, p in student.named_parameters():\n", " if p.grad is not None and (\"output_proj\" in nm or \"token_emb\" in nm):\n", " tb.add_histogram(f\"grad/{nm}\", p.grad, step)\n", " tb.add_histogram(f\"weight/{nm}\", p, step)\n", " print(f\" VAL r1 {m['mimicry_r1']:.4f} cos {m['cos_to_target']:.4f} \"\n", " f\"self_cos {m['self_cos']:+.4f} erank {m['erank']:.1f} \"\n", " f\"cv {m['cv']:.4f} | frame r1 \"\n", " f\"{m.get('frame_r1_after_rotation', float('nan')):.4f}\")\n", " if m[\"cos_to_target\"] > best:\n", " best = m[\"cos_to_target\"]\n", " save_state(cfg, f\"{P['ckpt']}/best_state.pt\", student, opt,\n", " sched, scaler, step, ep, ci, order, best,\n", " n_rows=n_rows_now)\n", " torch.save(student.state_dict(), f\"{P['ckpt']}/best_model.pt\")\n", "\n", " if (time.time() - last_ck) / 60 >= cfg.ckpt_every_min:\n", " save_state(cfg, sp, student, opt, sched, scaler, step, ep, ci, order, best,\n", " n_rows=n_rows_now)\n", " torch.save(student.state_dict(), f\"{P['ckpt']}/model_s{step}.pt\")\n", " ck = sorted([f for f in os.listdir(P[\"ckpt\"]) if f.startswith(\"model_s\")],\n", " key=lambda f: int(f.split(\"_s\")[1].split(\".\")[0]))\n", " for old in ck[:-cfg.keep_local_ckpts]:\n", " os.remove(os.path.join(P[\"ckpt\"], old))\n", " tb.flush(); bk.push(msg=f\"step {step}\")\n", " last_ck = time.time()\n", " else:\n", " if order is None or ep != ep0:\n", " order = train_chunks[:]; random.shuffle(order)\n", " for ci in range(chunk_i0 if ep == ep0 else 0, len(order)):\n", " c = order[ci]\n", " ds = ChunkPairs(cfg, c, tok)\n", " dl = torch.utils.data.DataLoader(\n", " ds, batch_size=cfg.batch_size, shuffle=True, drop_last=True,\n", " num_workers=cfg.num_workers, collate_fn=ds.collate,\n", " pin_memory=(DEVICE == \"cuda\"))\n", " for ids, am, tg in dl:\n", " ids = ids.to(DEVICE, non_blocking=True)\n", " am = am.to(DEVICE, non_blocking=True)\n", " tgt = F.normalize(tg.to(DEVICE, non_blocking=True).float(), dim=-1)\n", " with torch.amp.autocast(\"cuda\", enabled=cfg.amp and DEVICE == \"cuda\"):\n", " emb = student(ids, am)\n", " emb = emb.float()\n", " l_nce, acc = infonce(emb, tgt, cfg.nce_temperature)\n", " l_mse = F.mse_loss(emb, tgt)\n", " loss = cfg.nce_weight * l_nce + cfg.mse_weight * l_mse\n", " l_cv = torch.zeros((), device=emb.device)\n", " if cfg.cv_weight > 0:\n", " l_cv = cv_loss(emb, cfg.cv_target)\n", " loss = loss + cfg.cv_weight * l_cv\n", " scaler.scale(loss).backward()\n", " scaler.unscale_(opt)\n", " gn = torch.nn.utils.clip_grad_norm_(student.parameters(), cfg.grad_clip)\n", " scaler.step(opt); scaler.update()\n", " opt.zero_grad(set_to_none=True); sched.step()\n", " step += 1\n", "\n", " if step % cfg.log_every == 0:\n", " lr = opt.param_groups[0][\"lr\"]\n", " tb.add_scalar(\"train/loss\", loss.item(), step)\n", " tb.add_scalar(\"train/nce\", l_nce.item(), step)\n", " tb.add_scalar(\"train/mse\", l_mse.item(), step)\n", " tb.add_scalar(\"train/cv\", float(l_cv), step)\n", " tb.add_scalar(\"train/batch_acc\", acc, step)\n", " tb.add_scalar(\"train/lr\", lr, step)\n", " tb.add_scalar(\"train/grad_norm\", float(gn), step)\n", " tb.add_scalar(\"train/tokens_per_seq\", ids.shape[1], step)\n", " print(f\" e{ep+1} {step:>7,}/{total:,} loss {loss.item():.4f} \"\n", " f\"nce {l_nce.item():.4f} mse {l_mse.item():.5f} acc {acc:.3f} \"\n", " f\"lr {lr:.2e} L{ids.shape[1]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.eval_every == 0:\n", " m = evaluate(student, val_src)\n", " for k, v in m.items():\n", " if isinstance(v, (int, float)):\n", " tb.add_scalar(f\"val/{k}\", v, step)\n", " for nm, p in student.named_parameters():\n", " if p.grad is not None and (\"output_proj\" in nm or \"token_emb\" in nm):\n", " tb.add_histogram(f\"grad/{nm}\", p.grad, step)\n", " tb.add_histogram(f\"weight/{nm}\", p, step)\n", " print(f\" VAL r1 {m['mimicry_r1']:.4f} cos {m['cos_to_target']:.4f} \"\n", " f\"self_cos {m['self_cos']:+.4f} erank {m['erank']:.1f} \"\n", " f\"cv {m['cv']:.4f} | frame r1 \"\n", " f\"{m.get('frame_r1_after_rotation', float('nan')):.4f}\")\n", " if m[\"cos_to_target\"] > best:\n", " best = m[\"cos_to_target\"]\n", " save_state(cfg, f\"{P['ckpt']}/best_state.pt\", student, opt,\n", " sched, scaler, step, ep, ci, order, best)\n", " torch.save(student.state_dict(), f\"{P['ckpt']}/best_model.pt\")\n", "\n", " if (time.time() - last_ck) / 60 >= cfg.ckpt_every_min:\n", " save_state(cfg, sp, student, opt, sched, scaler, step, ep, ci, order, best,\n", " n_rows=n_rows_now)\n", " torch.save(student.state_dict(), f\"{P['ckpt']}/model_s{step}.pt\")\n", " ck = sorted([f for f in os.listdir(P[\"ckpt\"]) if f.startswith(\"model_s\")],\n", " key=lambda f: int(f.split(\"_s\")[1].split(\".\")[0]))\n", " for old in ck[:-cfg.keep_local_ckpts]:\n", " os.remove(os.path.join(P[\"ckpt\"], old))\n", " tb.flush(); bk.push(msg=f\"step {step}\")\n", " last_ck = time.time()\n", " del ds, dl; gc.collect()\n", " chunk_i0 = 0\n", "\n", " save_state(cfg, sp, student, opt, sched, scaler, step, cfg.epochs, 0, order,\n", " best, n_rows=n_rows_now)\n", " torch.save(student.state_dict(), f\"{P['ckpt']}/final_model.pt\")\n", " tok.save_pretrained(f\"{P['ckpt']}/tokenizer\")\n", " m = evaluate(student, val_src)\n", " line(\"FINAL\")\n", " print(f\" mimicry R@1 (student->consensus, NOT capability): {m['mimicry_r1']:.4f}\")\n", " print(f\" cos to target : {m['cos_to_target']:.4f}\")\n", " print(f\" self_cos : {m['self_cos']:+.4f} <- isotropy; teachers .81-.98\")\n", " print(f\" effective rank: {m['erank']:.1f}/{cfg.output_dim}\")\n", " print(f\" CV : {m['cv']:.4f}\")\n", " print(f\" frame-fit R@1 : {m.get('frame_r1_after_rotation', float('nan')):.4f} \"\n", " f\"(should be ~mimicry: reference-member alignment pins the frame)\")\n", " print(\" CAPABILITY is decided by STS-B / SICK vs the five teachers, not here.\")\n", " json.dump({\"config\": asdict(cfg), \"final\": m}, open(f\"{P['ckpt']}/metrics.json\", \"w\"),\n", " indent=2, default=str)\n", " tb.flush(); tb.close(); bk.push(force=True, msg=\"final\")\n", " return student\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " # THE PROFILE IS APPLIED HERE, not by the caller. This cell auto-runs on\n", " # paste, so anything the caller sets afterwards arrives too late -- which is\n", " # exactly how a stage-1 refit fired twice against an explicit instruction\n", " # not to (2026-08-02). Derived flags, one source of truth.\n", " cfg = configure(cfg, cfg.profile)\n", " cfg = dataclasses.replace(cfg, profile=\"_applied\")\n", " print(\"=\" * 78)\n", " print(f\"{cfg.run_name.upper()} — CONSENSUS DISTILLATION, CC12M SCALE\")\n", " print(\"=\" * 78)\n", " paths(cfg)\n", " print(f\"device={DEVICE} work_dir={cfg.work_dir}\")\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " miss = src0(cfg).get(\"missing\", {})\n", " print(f\"chunks: {len(usable_chunks(cfg))} train + {len(cfg.holdout_chunks)} holdout \"\n", " f\"| excluded for missing experts: {miss}\")\n", " if not cfg.require_all_experts:\n", " print(\" !! require_all_experts=False -> 4-expert consensus on some chunks.\")\n", " print(\" !! The target definition then differs BETWEEN chunks. Discouraged.\")\n", " bk = Backup(cfg)\n", "\n", " if cfg.run_stage0:\n", " cfg.caption_field = stage0_parity(cfg)\n", " elif cfg.caption_field is None:\n", " raise RuntimeError(\"caption_field is None and stage 0 is disabled.\")\n", "\n", " ensure_artifacts(cfg)\n", " maps = stage1_fit(cfg, bk) if cfg.run_stage1 else torch.load(\n", " f\"{paths(cfg)['maps']}/alignment_maps.pt\", weights_only=False)\n", " chunks = stage2_targets(cfg, maps, bk) if cfg.run_stage2 else sorted(\n", " set(usable_chunks(cfg)) | set(cfg.holdout_chunks))\n", " if cfg.run_stage3:\n", " return stage3_train(cfg, chunks, bk)\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " STUDENT = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b07387a7f0194878b75763bc95aa32a4", "1b54fdf55da04df3a4bdf7e77cd191af", "e0dd698589fd468bb2dfb340127ecaa2", "326b7ff214e741c2b80139c631c487cd", "a3180277ed8149c39903aa9a7434aff7", "f09e5320347e424bb14668fc88b29a58", "ef8164114d0749be8aa5dbfc20c77956", "89a7e09810ab47f2bfe8a40a9370418c", "c0eb91e84e7d453894e974d6f3bab65f", "3878d2f4481e462cbbf67a64283f123d", "c8bfe386d94048049fc95aa6216537fa", "cb346c66bbc347459289041d37fe47c8", "78b1707db36741ba8429296e4bd526f4", "b528ccf534ac4d22a4417621438d9783", "ce619bf630904587af8f0727779d6848", "2a4202f38fef424bb0f317235584dcb8", "826e701ee7774e63a47c42e3ed64530f", "2d19b3c8dc8b4e96880a9c15be8452f7", "cabfe5c61b264cad887f5810cd97ffef", "c5705371d1d1423ab4fe03c6611a77fd", "21d615f10e40497ca4b5361ed0f99aff", "2e89c1da628f4f63812d119f4dba5066", "bb4ff9f1f28644f8b92098bbabd1a47b", "6bb862c1551348d8885fb94bdcf1021a", "bbebb76029fc41fb91d9fd81e03e9261", "8d5db3b2a7cf48718c225fb43771cd46", "ae6cf5dbc96c444493a0fb24ee4346aa", "aa8b09a2248b440ca261499a52f0e495", "fcfbdd26df5b4902b794bc63e4e21852", "c770f0bd5682425ebb994269369ed28e", "87ff67a2b47947f5b32151ab81c75349", "5e6af138b9f04615b0aee669376017a8", "3a778e7c7a554fce8526b43738967f9a", "90034b20a4f5489ab5367227dbfca4e6", "d3b14d4ed3944ce780ebfc2a1c0f7a19", "23466047c2f94b41a07bf14e544590e7", "4b968f559fcb4d21be7877ad49c25bc6", "1e39026029f04e07b7f02e586b886433", "ad1331a33d004e9994158df0f58fc58a", "62b9253017574e5ea59ed3951a5657df", "0f7311c64c77492a89723853df2e33e6", "b6ce4923d8b946b492184052ad651636", "41f1e901f9e34b2386d80dc9e942bb32", "84fb931a82154eab8c29868feac6f1da", "3032a31aa2cd45bba0825fac6dbe9c7a", "99f41a3397214d55989ca0804560b3fe", "d3bb83fb8e19465badf08e13a3ffa112", "4ee709f226aa42dbabc58ef672049973", "46cd61cf2e9049d686bf21cb5f5ca697", "9d073b248e084319bd666d11cfc76130", "bf6661c956c34207995a9228f9ef6d22", "3b654f2f0bcb4ba9903212a88800dde7", "19bb08c55c1d4e2089e2bef211942288", "fbac94fdc71045cf96b41e8035b31be2", "98af40c93b2348aab8d17896a468e81a", "2a06e4b834064073bf9235a3e9a8e262", "3d35ac55e8b64f679dac59012256c812", "952d966d7e9548478964e82c1fe34707", "bb2f00032230446fa37e001dd98fccb4", "bbc116deb69e4ce89d3215ad4a723ee2", "fbcad69b79e1406f85c923a89d523b9c", "57d03eceb2cd43cab4971e6a4050fa19", "978b1bf05e0546f0a80cb55cecf135d5", "8c59ef25233847b19bee4bb685410975", "53472b16a38649a8833249a254fd6143", "c17b899bb91541e1bf971a5d36efd397", "5b903234aee34b2186bd54bd12a6092f", "d4fecc4d95c247caaedcaf8638a33021", "ce38bbd2041f49878c70d422aa43e39a", "5b98f96e1a684ba7b425cffecd59caa9", "effe6f964aa34be3a49fd63d96d67c0c", "651228be6bad4d86af6e78adbe796454", "82594790efcb453e864df0dcfa69bac0", "bcee38a3cc3c41949f3fd8eb67b155ea", "71731080c4ce4bc5929ed40ec24d1f88", "a0affc94d7004326b825fef9d6d72833", "ff93626d197e420e91ca628a97ac7638", "960f1348a2e14cdf8540f85d5a835216", "3e306333f5fc49af8af99281a8f92a5e", "7be4f124909643d6a2f9f5baa6e27e9d", "5775803f0de24950a10c42e4449b6369", "5c9763130c444e62b39c81dffdefd7b9", "8a7567feebee4f2c9a2d57cc65735668", "a03df523e2e24190b34774073bf092a7", "91ddd0346b5243ed9e60b3a0212fa3f4", "c26b2a618e2b47c7a5c2bbb3ffd8b682", "b512751c486e4db59928bc3bfb36dc2f", "403f1d8298c74505825489920cf64b27", "3241982786ed4823a46c18ef6b317b30", "b896d0ed07d64d28a5b10253f656d54c", "2d9b9c2fd5334418b5abc72d23a7ae2a", "cc60e4a0d6104950a836be0aeb2e4469", "ec47c833faa549f3a1e02d5fb27e1d9a", "a2f5936bf5bf4991a4aadf87182f28d9", "8378decaf4c84a7da081a71f1a00fcd5", "6630694328614109b6c49c825fa68551", "4dd043321f9e47bca77ddbfc90097347", "99ba6e54cbf044b98e712394791affab", "743b08b358be4646843593bb9ff76357", "288730b325414d63ace89b85af992517", "d8383edc8e324d9e8450890e3317d9ee", "7d073ff386164d55ba19bc9738d0ea6e", "85482af5d31e4103b93f95f61498a062", "c4aa5664b2b245839ba86a116012e9f7", "f6f6b5ca78d948268eb0a9736a55c026", "9495c1fc5c454f45a1524e28e6834373", "95199eddfd0747708a5dd9a43b4b2d52", "4a0784152a334cc48e5a612505bdde93", "cf618d05ce7e43a3a1e8247fd887fbcc", "fbb7dde29b404dfab04f9a031db855ed", "ff1349de7f1149a39ab3727d4b44f09a", "2c50f58d3c1745b680736be5d1522eca", "166dc77c7b024316ae7f52461d43dfd1", "c403b0d43f224f6694e4eb65fea96f67", "32bfc085d89e404aa5a9bc1a1a36eb85", "6aac628edd8748bfadcaa924328a78bb", "885fb78db979469bb14f99d32f44ae61", "f51bca1563544249a56ed74d5cafa26b", "3292b2976be84451a6e860de74c5a1fb", "dd075db935aa4d0c947c776fe74c3ed2", "1ba453c7659445a2bbd5f88990065d6f", "5789388c5d3145ea8b6d760db726bc57", "dac3fa2f6ed74456a73254299febc5a8", "fc1382282a064322866ab89bd35d367e", "d68169c30a4842f2b1eda658fe71d81b", "6fc51a198bd04e13af983281aaaf77d6", "42ec5591b90c46c7b147f995f05b8e06", "574ea11cc58f4b96947d738456d5d5c0", "b421ee8a0f614b019ab4a68cbc6930eb", "4e852dd8145e4214b0e1c1b7a0ffc1b3", "047bbe3b8add455c9587bdf1bd02fc98", "90b9c6af9688483286dd52cb8c41026f", "0b8aae985f2945fa83ba56f86d6e506a", "aacf4a3c2691424388c2b762527df1c4", "5dfbfd82b0c14c9da683b545340c6343", "4eefdc2f453541958e829abe769d361a", "4ab0680e0a4340baa4d276b5818931b1", "d17000b2845746eaa323dea15ccbaa4b", "47806620657e406983b1d6af1703a138", "d6c0f0b8d7ce48989502d4fb73718410", 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"==============================================================================\n", "device=cuda work_dir=/content/cbv2\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "chunks: 64 train + 2 holdout | excluded for missing experts: {}\n", " [backup] -> AbstractPhil/captionbert-8192-v2-B (private)\n", "── ARTIFACTS ─────────────────────────────────────────────────────────────────\n", " maps: local /content/cbv2/maps/alignment_maps.pt\n", " (NOT refitting: every consensus target was built with THIS map;\n", " a new fit would sit in a different frame with no loss signal.)\n", " targets: 66 local + 0 fetched from AbstractPhil/captionbert-8192-v2-consensus\n", "\n", "── STAGE 3 — TRAIN ───────────────────────────────────────────────────────────\n", " captionbert-8192-b: 58,308,864 params (0.53x bert-base)\n", " 12L 512d 8h ff2048 pool=mean\n", " 64 chunks ≈ 32,000,000 rows | 15,625 steps/ep x 4 = 62,500 steps @ batch 2048\n", " loss = 1.0*InfoNCE(T=0.07) + 1.0*MSE + 0.0*CV [champion consensus_nce_mse]\n", "── VRAM PROBE - worst-case batch before spending time on data ────────────────\n", " batch 2048 x L 256 (checkpointing=True) -> peak 29.8 GB of 102.0 GB\n", " PASS - 72.1 GB headroom\n", " fresh_start=True -- ignoring any local or remote checkpoint\n", " building val store...\n", " chunk 061: 500,000 rows | flat tokens 23.5M\n", " RamStore [val]: 500,000 rows | 0.8 GB RAM | mean 47 tokens (ceiling 256)\n", " length p50 32 | p90 112 | p99 188 | max 256 -- unbucketed, a batch pads to the BATCH MAX, i.e. ~256\n", " building train store (one pass, then zero disk I/O)...\n", " chunk 000: 500,000 rows | flat tokens 25.8M\n", " chunk 001: 500,000 rows | flat tokens 51.7M\n", " chunk 002: 500,000 rows | flat tokens 77.5M\n", " chunk 003: 500,000 rows | flat tokens 102.8M\n", " chunk 004: 500,000 rows | flat tokens 126.3M\n", " chunk 005: 500,000 rows | flat tokens 149.8M\n", " chunk 006: 500,000 rows | flat tokens 173.3M\n" ] }, { "output_type": 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256)\n", " length p50 32 | p90 114 | p99 188 | max 256 -- unbucketed, a batch pads to the BATCH MAX, i.e. ~256\n", " 31,905,616 rows resident | 15,578 steps/ep x 4 = 62,312 steps\n", " batch plan: 15,578 batches/epoch | padded length p50 32 p90 105 max 256 (bucketing=True)\n", " e1 50/62,312 loss 6.7783 nce 6.7760 mse 0.00226 acc 0.009 lr 2.09e-05 L128 0m\n", " e1 100/62,312 loss 5.6434 nce 5.6414 mse 0.00199 acc 0.075 lr 3.57e-05 L94 1m\n", " e1 150/62,312 loss 5.2539 nce 5.2520 mse 0.00190 acc 0.125 lr 5.06e-05 L24 1m\n", " e1 200/62,312 loss 4.7161 nce 4.7143 mse 0.00173 acc 0.219 lr 6.54e-05 L29 1m\n", " e1 250/62,312 loss 4.9141 nce 4.9123 mse 0.00180 acc 0.177 lr 8.03e-05 L148 2m\n", " e1 300/62,312 loss 3.8227 nce 3.8212 mse 0.00151 acc 0.392 lr 9.51e-05 L30 2m\n", " e1 350/62,312 loss 3.6278 nce 3.6263 mse 0.00147 acc 0.440 lr 1.10e-04 L25 2m\n", " e1 400/62,312 loss 4.1081 nce 4.1065 mse 0.00160 acc 0.334 lr 1.25e-04 L15 2m\n", " e1 450/62,312 loss 2.8416 nce 2.8404 mse 0.00124 acc 0.627 lr 1.40e-04 L33 3m\n", " e1 500/62,312 loss 2.7474 nce 2.7462 mse 0.00125 acc 0.646 lr 1.55e-04 L24 3m\n", " e1 550/62,312 loss 2.6353 nce 2.6340 mse 0.00125 acc 0.646 lr 1.69e-04 L43 3m\n", " e1 600/62,312 loss 3.3651 nce 3.3637 mse 0.00142 acc 0.492 lr 1.84e-04 L14 4m\n", " e1 650/62,312 loss 1.9817 nce 1.9806 mse 0.00105 acc 0.844 lr 1.99e-04 L31 4m\n", " e1 700/62,312 loss 1.7750 nce 1.7740 mse 0.00100 acc 0.874 lr 2.14e-04 L37 4m\n", " e1 750/62,312 loss 1.6972 nce 1.6962 mse 0.00100 acc 0.898 lr 2.29e-04 L25 4m\n", " e1 800/62,312 loss 2.1381 nce 2.1370 mse 0.00110 acc 0.868 lr 2.44e-04 L116 5m\n", " e1 850/62,312 loss 1.4632 nce 1.4623 mse 0.00097 acc 0.918 lr 2.58e-04 L23 5m\n", " e1 900/62,312 loss 1.2304 nce 1.2295 mse 0.00091 acc 0.958 lr 2.73e-04 L34 5m\n", " e1 950/62,312 loss 1.4507 nce 1.4498 mse 0.00093 acc 0.961 lr 2.88e-04 L93 6m\n", " e1 1,000/62,312 loss 0.8624 nce 0.8616 mse 0.00078 acc 0.985 lr 3.03e-04 L33 6m\n", " VAL r1 0.9235 cos 0.6626 self_cos +0.0225 erank 51.0 cv 0.1176 | frame r1 0.9550\n", " e1 1,050/62,312 loss 0.8916 nce 0.8908 mse 0.00079 acc 0.982 lr 3.18e-04 L27 6m\n", " e1 1,100/62,312 loss 0.7561 nce 0.7553 mse 0.00076 acc 0.995 lr 3.33e-04 L33 7m\n", " e1 1,150/62,312 loss 0.6988 nce 0.6980 mse 0.00073 acc 0.994 lr 3.48e-04 L36 7m\n", " e1 1,200/62,312 loss 0.7601 nce 0.7594 mse 0.00071 acc 0.989 lr 3.62e-04 L45 7m\n", " e1 1,250/62,312 loss 0.5923 nce 0.5916 mse 0.00070 acc 0.991 lr 3.77e-04 L27 7m\n", " e1 1,300/62,312 loss 0.5354 nce 0.5347 mse 0.00068 acc 0.998 lr 3.92e-04 L39 8m\n", " e1 1,350/62,312 loss 0.5355 nce 0.5348 mse 0.00068 acc 0.996 lr 4.07e-04 L43 8m\n", " e1 1,400/62,312 loss 1.0063 nce 1.0055 mse 0.00086 acc 0.998 lr 4.22e-04 L136 8m\n", " e1 1,450/62,312 loss 0.8274 nce 0.8266 mse 0.00082 acc 0.998 lr 4.37e-04 L105 9m\n", " e1 1,500/62,312 loss 0.9265 nce 0.9257 mse 0.00079 acc 0.996 lr 4.51e-04 L136 9m\n", " e1 1,550/62,312 loss 0.4720 nce 0.4714 mse 0.00066 acc 0.999 lr 4.66e-04 L38 9m\n", " e1 1,600/62,312 loss 0.4266 nce 0.4260 mse 0.00064 acc 0.999 lr 4.81e-04 L29 10m\n", " e1 1,650/62,312 loss 0.5810 nce 0.5803 mse 0.00065 acc 0.998 lr 4.96e-04 L58 10m\n", " e1 1,700/62,312 loss 0.3521 nce 0.3515 mse 0.00059 acc 1.000 lr 5.11e-04 L32 10m\n", " e1 1,750/62,312 loss 0.5728 nce 0.5722 mse 0.00064 acc 0.997 lr 5.26e-04 L64 10m\n", " e1 1,800/62,312 loss 0.7431 nce 0.7423 mse 0.00075 acc 1.000 lr 5.41e-04 L116 11m\n", " e1 1,850/62,312 loss 0.3716 nce 0.3709 mse 0.00061 acc 1.000 lr 5.55e-04 L29 11m\n", " e1 1,900/62,312 loss 0.3717 nce 0.3711 mse 0.00061 acc 0.999 lr 5.70e-04 L29 12m\n", " e1 1,950/62,312 loss 0.4997 nce 0.4991 mse 0.00059 acc 0.998 lr 5.85e-04 L53 12m\n", " e1 2,000/62,312 loss 0.3068 nce 0.3063 mse 0.00056 acc 0.999 lr 6.00e-04 L38 12m\n", " VAL r1 0.9805 cos 0.7536 self_cos +0.0123 erank 75.7 cv 0.0926 | frame r1 0.9945\n", " e1 2,050/62,312 loss 0.4294 nce 0.4288 mse 0.00062 acc 0.997 lr 6.00e-04 L70 13m\n", " e1 2,100/62,312 loss 0.3290 nce 0.3284 mse 0.00058 acc 0.998 lr 6.00e-04 L27 13m\n", " e1 2,150/62,312 loss 0.3363 nce 0.3357 mse 0.00058 acc 0.998 lr 6.00e-04 L43 13m\n", " e1 2,200/62,312 loss 0.7046 nce 0.7038 mse 0.00080 acc 0.998 lr 6.00e-04 L137 13m\n", " e1 2,250/62,312 loss 0.4170 nce 0.4164 mse 0.00064 acc 0.997 lr 6.00e-04 L20 14m\n", " e1 2,300/62,312 loss 0.2732 nce 0.2727 mse 0.00054 acc 0.997 lr 6.00e-04 L26 14m\n", " e1 2,350/62,312 loss 0.4309 nce 0.4302 mse 0.00063 acc 0.999 lr 6.00e-04 L76 14m\n", " e1 2,400/62,312 loss 0.4164 nce 0.4158 mse 0.00058 acc 0.998 lr 6.00e-04 L58 15m\n", " e1 2,450/62,312 loss 0.2590 nce 0.2585 mse 0.00052 acc 1.000 lr 6.00e-04 L37 15m\n", " e1 2,500/62,312 loss 0.4690 nce 0.4683 mse 0.00067 acc 1.000 lr 6.00e-04 L104 15m\n", " e1 2,550/62,312 loss 0.8380 nce 0.8371 mse 0.00087 acc 0.954 lr 6.00e-04 L11 16m\n", " e1 2,600/62,312 loss 0.2570 nce 0.2565 mse 0.00052 acc 1.000 lr 6.00e-04 L37 16m\n", " e1 2,650/62,312 loss 0.6590 nce 0.6582 mse 0.00079 acc 1.000 lr 6.00e-04 L147 16m\n", " e1 2,700/62,312 loss 0.2537 nce 0.2531 mse 0.00054 acc 1.000 lr 6.00e-04 L26 16m\n", " e1 2,750/62,312 loss 0.2560 nce 0.2555 mse 0.00053 acc 0.999 lr 6.00e-04 L26 17m\n", " e1 2,800/62,312 loss 0.3500 nce 0.3494 mse 0.00056 acc 1.000 lr 6.00e-04 L59 17m\n", " e1 2,850/62,312 loss 0.2573 nce 0.2567 mse 0.00055 acc 0.999 lr 6.00e-04 L24 17m\n", " e1 2,900/62,312 loss 0.5750 nce 0.5743 mse 0.00076 acc 0.983 lr 6.00e-04 L14 18m\n", " e1 2,950/62,312 loss 0.8964 nce 0.8955 mse 0.00087 acc 0.997 lr 6.00e-04 L256 18m\n", " e1 3,000/62,312 loss 0.4489 nce 0.4483 mse 0.00064 acc 1.000 lr 6.00e-04 L111 18m\n", " VAL r1 0.9910 cos 0.7790 self_cos +0.0116 erank 81.6 cv 0.0794 | frame r1 0.9965\n", " e1 3,050/62,312 loss 0.2196 nce 0.2191 mse 0.00049 acc 1.000 lr 6.00e-04 L31 19m\n", " e1 3,100/62,312 loss 0.4590 nce 0.4583 mse 0.00066 acc 1.000 lr 6.00e-04 L111 19m\n", " e1 3,150/62,312 loss 0.1968 nce 0.1963 mse 0.00048 acc 1.000 lr 5.99e-04 L34 19m\n", " e1 3,200/62,312 loss 0.2353 nce 0.2347 mse 0.00051 acc 0.998 lr 5.99e-04 L23 20m\n", " e1 3,250/62,312 loss 0.2880 nce 0.2875 mse 0.00055 acc 1.000 lr 5.99e-04 L70 20m\n", " [backup] pushed (step 3280)\n", " e1 3,300/62,312 loss 0.5732 nce 0.5724 mse 0.00076 acc 1.000 lr 5.99e-04 L148 21m\n", " e1 3,350/62,312 loss 0.2384 nce 0.2379 mse 0.00052 acc 0.999 lr 5.99e-04 L24 21m\n", " e1 3,400/62,312 loss 0.4356 nce 0.4350 mse 0.00064 acc 1.000 lr 5.99e-04 L110 21m\n", " e1 3,450/62,312 loss 0.2107 nce 0.2103 mse 0.00047 acc 1.000 lr 5.99e-04 L32 22m\n", " e1 3,500/62,312 loss 0.2422 nce 0.2416 mse 0.00055 acc 1.000 lr 5.99e-04 L20 22m\n", " e1 3,550/62,312 loss 0.2047 nce 0.2042 mse 0.00049 acc 1.000 lr 5.99e-04 L35 22m\n", " e1 3,600/62,312 loss 0.1904 nce 0.1899 mse 0.00048 acc 1.000 lr 5.99e-04 L26 23m\n", " e1 3,650/62,312 loss 0.5094 nce 0.5087 mse 0.00072 acc 1.000 lr 5.99e-04 L148 23m\n", " e1 3,700/62,312 loss 0.2090 nce 0.2085 mse 0.00049 acc 1.000 lr 5.99e-04 L31 23m\n", " e1 3,750/62,312 loss 0.2119 nce 0.2114 mse 0.00051 acc 1.000 lr 5.99e-04 L24 24m\n", " e1 3,800/62,312 loss 0.3270 nce 0.3264 mse 0.00059 acc 1.000 lr 5.99e-04 L88 24m\n", " e1 3,850/62,312 loss 0.2054 nce 0.2050 mse 0.00048 acc 1.000 lr 5.99e-04 L36 24m\n", " e1 3,900/62,312 loss 0.3647 nce 0.3641 mse 0.00062 acc 1.000 lr 5.99e-04 L100 24m\n", " e1 3,950/62,312 loss 0.2071 nce 0.2067 mse 0.00047 acc 1.000 lr 5.98e-04 L37 25m\n", " e1 4,000/62,312 loss 0.5061 nce 0.5053 mse 0.00072 acc 1.000 lr 5.98e-04 L148 25m\n", " VAL r1 0.9915 cos 0.7923 self_cos +0.0057 erank 85.1 cv 0.0763 | frame r1 0.9965\n", " e1 4,050/62,312 loss 0.2039 nce 0.2034 mse 0.00050 acc 1.000 lr 5.98e-04 L23 25m\n", " e1 4,100/62,312 loss 0.4849 nce 0.4842 mse 0.00074 acc 0.985 lr 5.98e-04 L12 26m\n", " e1 4,150/62,312 loss 0.5034 nce 0.5026 mse 0.00075 acc 0.985 lr 5.98e-04 L12 26m\n", " e1 4,200/62,312 loss 0.2267 nce 0.2262 mse 0.00054 acc 0.997 lr 5.98e-04 L22 26m\n", " e1 4,250/62,312 loss 0.4401 nce 0.4394 mse 0.00068 acc 0.995 lr 5.98e-04 L15 27m\n", " e1 4,300/62,312 loss 0.1698 nce 0.1694 mse 0.00045 acc 1.000 lr 5.98e-04 L33 27m\n", " e1 4,350/62,312 loss 0.4210 nce 0.4204 mse 0.00068 acc 1.000 lr 5.98e-04 L128 27m\n", " e1 4,400/62,312 loss 0.1702 nce 0.1698 mse 0.00044 acc 0.999 lr 5.98e-04 L35 28m\n", " e1 4,450/62,312 loss 0.3533 nce 0.3526 mse 0.00066 acc 0.994 lr 5.98e-04 L15 28m\n", " e1 4,500/62,312 loss 0.1733 nce 0.1728 mse 0.00045 acc 1.000 lr 5.97e-04 L27 28m\n", " e1 4,550/62,312 loss 0.3202 nce 0.3197 mse 0.00058 acc 1.000 lr 5.97e-04 L99 28m\n", " e1 4,600/62,312 loss 0.1736 nce 0.1731 mse 0.00046 acc 1.000 lr 5.97e-04 L25 29m\n", " e1 4,650/62,312 loss 0.1682 nce 0.1677 mse 0.00044 acc 1.000 lr 5.97e-04 L33 29m\n", " e1 4,700/62,312 loss 0.1539 nce 0.1535 mse 0.00044 acc 1.000 lr 5.97e-04 L29 29m\n", " e1 4,750/62,312 loss 0.4091 nce 0.4084 mse 0.00070 acc 0.988 lr 5.97e-04 L12 30m\n", " e1 4,800/62,312 loss 0.3661 nce 0.3655 mse 0.00064 acc 1.000 lr 5.97e-04 L116 30m\n", " e1 4,850/62,312 loss 0.6641 nce 0.6633 mse 0.00078 acc 0.999 lr 5.97e-04 L256 30m\n", " e1 4,900/62,312 loss 0.2764 nce 0.2758 mse 0.00057 acc 1.000 lr 5.97e-04 L89 31m\n", " e1 4,950/62,312 loss 0.2676 nce 0.2671 mse 0.00055 acc 1.000 lr 5.96e-04 L88 31m\n", " e1 5,000/62,312 loss 0.1653 nce 0.1649 mse 0.00046 acc 0.999 lr 5.96e-04 L27 31m\n", " VAL r1 0.9930 cos 0.7874 self_cos +0.0151 erank 88.2 cv 0.0909 | frame r1 0.9970\n", " e1 5,050/62,312 loss 0.5038 nce 0.5030 mse 0.00074 acc 1.000 lr 5.96e-04 L147 32m\n", " e1 5,100/62,312 loss 0.2440 nce 0.2434 mse 0.00058 acc 0.998 lr 5.96e-04 L18 32m\n", " e1 5,150/62,312 loss 0.3375 nce 0.3369 mse 0.00059 acc 1.000 lr 5.96e-04 L89 32m\n", " e1 5,200/62,312 loss 0.1802 nce 0.1797 mse 0.00049 acc 0.999 lr 5.96e-04 L23 33m\n", " e1 5,250/62,312 loss 0.1724 nce 0.1720 mse 0.00044 acc 1.000 lr 5.96e-04 L35 33m\n", " e1 5,300/62,312 loss 0.1680 nce 0.1676 mse 0.00042 acc 1.000 lr 5.96e-04 L34 33m\n", " e1 5,350/62,312 loss 0.2211 nce 0.2206 mse 0.00054 acc 0.998 lr 5.95e-04 L19 34m\n", " e1 5,400/62,312 loss 0.1855 nce 0.1851 mse 0.00047 acc 0.998 lr 5.95e-04 L24 34m\n", " e1 5,450/62,312 loss 0.1734 nce 0.1730 mse 0.00045 acc 0.999 lr 5.95e-04 L24 34m\n", " e1 5,500/62,312 loss 0.2538 nce 0.2533 mse 0.00052 acc 1.000 lr 5.95e-04 L88 34m\n", " e1 5,550/62,312 loss 0.2004 nce 0.1999 mse 0.00047 acc 1.000 lr 5.95e-04 L49 35m\n", " e1 5,600/62,312 loss 0.9109 nce 0.9100 mse 0.00090 acc 0.902 lr 5.95e-04 L9 35m\n", " e1 5,650/62,312 loss 0.4148 nce 0.4142 mse 0.00067 acc 1.000 lr 5.95e-04 L136 35m\n", " e1 5,700/62,312 loss 0.1589 nce 0.1585 mse 0.00042 acc 1.000 lr 5.94e-04 L33 36m\n", " e1 5,750/62,312 loss 0.1474 nce 0.1470 mse 0.00042 acc 1.000 lr 5.94e-04 L34 36m\n", " e1 5,800/62,312 loss 0.3974 nce 0.3967 mse 0.00066 acc 0.988 lr 5.94e-04 L13 36m\n", " e1 5,850/62,312 loss 0.1452 nce 0.1448 mse 0.00042 acc 1.000 lr 5.94e-04 L28 36m\n", " e1 5,900/62,312 loss 0.1637 nce 0.1633 mse 0.00046 acc 1.000 lr 5.94e-04 L25 37m\n", " e1 5,950/62,312 loss 0.1551 nce 0.1547 mse 0.00043 acc 1.000 lr 5.94e-04 L33 37m\n", " e1 6,000/62,312 loss 0.2570 nce 0.2564 mse 0.00055 acc 1.000 lr 5.94e-04 L88 37m\n", " VAL r1 0.9950 cos 0.7993 self_cos +0.0065 erank 90.6 cv 0.0824 | frame r1 0.9965\n", " e1 6,050/62,312 loss 0.3795 nce 0.3788 mse 0.00066 acc 0.989 lr 5.93e-04 L13 38m\n", " e1 6,100/62,312 loss 0.1575 nce 0.1570 mse 0.00043 acc 0.997 lr 5.93e-04 L25 38m\n", " e1 6,150/62,312 loss 0.1414 nce 0.1410 mse 0.00042 acc 1.000 lr 5.93e-04 L33 38m\n", " e1 6,200/62,312 loss 0.2135 nce 0.2130 mse 0.00051 acc 1.000 lr 5.93e-04 L77 38m\n", " e1 6,250/62,312 loss 0.2352 nce 0.2346 mse 0.00057 acc 0.999 lr 5.93e-04 L17 39m\n", " e1 6,300/62,312 loss 0.3794 nce 0.3788 mse 0.00065 acc 1.000 lr 5.93e-04 L136 39m\n", " e1 6,350/62,312 loss 0.1458 nce 0.1454 mse 0.00043 acc 1.000 lr 5.92e-04 L28 39m\n", " e1 6,400/62,312 loss 0.1439 nce 0.1435 mse 0.00042 acc 1.000 lr 5.92e-04 L41 40m\n", " e1 6,450/62,312 loss 0.2421 nce 0.2416 mse 0.00048 acc 1.000 lr 5.92e-04 L53 40m\n", " e1 6,500/62,312 loss 0.1667 nce 0.1662 mse 0.00048 acc 0.998 lr 5.92e-04 L21 40m\n", " e1 6,550/62,312 loss 0.1381 nce 0.1377 mse 0.00042 acc 1.000 lr 5.92e-04 L37 41m\n", " e1 6,600/62,312 loss 0.3207 nce 0.3201 mse 0.00058 acc 1.000 lr 5.91e-04 L100 41m\n", " e1 6,650/62,312 loss 0.1435 nce 0.1431 mse 0.00041 acc 1.000 lr 5.91e-04 L28 41m\n", " e1 6,700/62,312 loss 0.1475 nce 0.1471 mse 0.00044 acc 0.999 lr 5.91e-04 L24 41m\n", " e1 6,750/62,312 loss 0.2825 nce 0.2819 mse 0.00060 acc 0.997 lr 5.91e-04 L16 42m\n", " e1 6,800/62,312 loss 0.1541 nce 0.1536 mse 0.00044 acc 1.000 lr 5.91e-04 L37 42m\n", " e1 6,850/62,312 loss 0.4261 nce 0.4254 mse 0.00071 acc 0.973 lr 5.91e-04 L11 42m\n", " e1 6,900/62,312 loss 0.2907 nce 0.2901 mse 0.00060 acc 0.997 lr 5.90e-04 L15 43m\n", " e1 6,950/62,312 loss 0.2520 nce 0.2515 mse 0.00055 acc 1.000 lr 5.90e-04 L88 43m\n", " e1 7,000/62,312 loss 0.2858 nce 0.2852 mse 0.00057 acc 1.000 lr 5.90e-04 L94 43m\n", " VAL r1 0.9880 cos 0.7860 self_cos +0.0369 erank 88.4 cv 0.0848 | frame r1 0.9950\n", " e1 7,050/62,312 loss 0.2117 nce 0.2112 mse 0.00050 acc 1.000 lr 5.90e-04 L70 44m\n", " e1 7,100/62,312 loss 0.2285 nce 0.2280 mse 0.00051 acc 1.000 lr 5.90e-04 L53 44m\n", " e1 7,150/62,312 loss 0.6084 nce 0.6076 mse 0.00075 acc 1.000 lr 5.89e-04 L256 45m\n", " e1 7,200/62,312 loss 0.2076 nce 0.2071 mse 0.00053 acc 0.999 lr 5.89e-04 L77 45m\n", " e1 7,250/62,312 loss 0.1948 nce 0.1943 mse 0.00052 acc 0.997 lr 5.89e-04 L18 45m\n", " e1 7,300/62,312 loss 0.1563 nce 0.1558 mse 0.00046 acc 0.999 lr 5.89e-04 L21 46m\n", " e1 7,350/62,312 loss 0.1301 nce 0.1297 mse 0.00041 acc 1.000 lr 5.89e-04 L29 46m\n", " e1 7,400/62,312 loss 0.5890 nce 0.5883 mse 0.00075 acc 0.999 lr 5.88e-04 L256 46m\n", " e1 7,450/62,312 loss 0.1300 nce 0.1296 mse 0.00039 acc 1.000 lr 5.88e-04 L32 46m\n", " e1 7,500/62,312 loss 0.1359 nce 0.1354 mse 0.00043 acc 1.000 lr 5.88e-04 L25 47m\n", " e1 7,550/62,312 loss 0.1921 nce 0.1915 mse 0.00053 acc 0.995 lr 5.88e-04 L18 47m\n", " e1 7,600/62,312 loss 0.1282 nce 0.1278 mse 0.00042 acc 1.000 lr 5.87e-04 L36 47m\n", " e1 7,650/62,312 loss 0.1568 nce 0.1563 mse 0.00047 acc 0.999 lr 5.87e-04 L21 48m\n", " e1 7,700/62,312 loss 0.3183 nce 0.3177 mse 0.00061 acc 1.000 lr 5.87e-04 L121 48m\n", " e1 7,750/62,312 loss 0.1545 nce 0.1540 mse 0.00047 acc 0.999 lr 5.87e-04 L22 48m\n", " e1 7,800/62,312 loss 0.4603 nce 0.4596 mse 0.00071 acc 1.000 lr 5.87e-04 L171 49m\n", " e1 7,850/62,312 loss 0.2459 nce 0.2454 mse 0.00054 acc 1.000 lr 5.86e-04 L94 49m\n", " e1 7,900/62,312 loss 0.1294 nce 0.1290 mse 0.00039 acc 1.000 lr 5.86e-04 L27 49m\n", " e1 7,950/62,312 loss 0.2010 nce 0.2005 mse 0.00055 acc 0.999 lr 5.86e-04 L16 50m\n", " e1 8,000/62,312 loss 0.2629 nce 0.2623 mse 0.00058 acc 0.997 lr 5.86e-04 L15 50m\n", " VAL r1 0.9960 cos 0.8124 self_cos +0.0079 erank 92.8 cv 0.0826 | frame r1 0.9980\n", " e1 8,050/62,312 loss 0.1283 nce 0.1279 mse 0.00040 acc 1.000 lr 5.85e-04 L32 50m\n", " e1 8,100/62,312 loss 0.1261 nce 0.1257 mse 0.00040 acc 1.000 lr 5.85e-04 L41 50m\n", " e1 8,150/62,312 loss 0.2084 nce 0.2079 mse 0.00049 acc 1.000 lr 5.85e-04 L58 51m\n", " e1 8,200/62,312 loss 0.1352 nce 0.1348 mse 0.00040 acc 1.000 lr 5.85e-04 L39 51m\n", " e1 8,250/62,312 loss 0.5830 nce 0.5823 mse 0.00075 acc 0.928 lr 5.84e-04 L10 51m\n", " e1 8,300/62,312 loss 0.3037 nce 0.3031 mse 0.00060 acc 1.000 lr 5.84e-04 L106 52m\n", " e1 8,350/62,312 loss 0.2259 nce 0.2254 mse 0.00053 acc 1.000 lr 5.84e-04 L89 52m\n", " e1 8,400/62,312 loss 0.2578 nce 0.2573 mse 0.00056 acc 1.000 lr 5.84e-04 L99 52m\n", " e1 8,450/62,312 loss 0.4226 nce 0.4219 mse 0.00068 acc 1.000 lr 5.83e-04 L169 53m\n", " e1 8,500/62,312 loss 0.4279 nce 0.4272 mse 0.00071 acc 0.966 lr 5.83e-04 L11 53m\n", " e1 8,550/62,312 loss 0.1425 nce 0.1421 mse 0.00044 acc 1.000 lr 5.83e-04 L32 53m\n", " e1 8,600/62,312 loss 0.1279 nce 0.1275 mse 0.00039 acc 1.000 lr 5.83e-04 L33 53m\n", " e1 8,650/62,312 loss 0.1238 nce 0.1234 mse 0.00040 acc 1.000 lr 5.82e-04 L30 54m\n", " e1 8,700/62,312 loss 0.1679 nce 0.1675 mse 0.00044 acc 1.000 lr 5.82e-04 L49 54m\n", " e1 8,750/62,312 loss 0.3071 nce 0.3065 mse 0.00061 acc 0.990 lr 5.82e-04 L14 54m\n", " e1 8,800/62,312 loss 0.1624 nce 0.1620 mse 0.00049 acc 0.997 lr 5.82e-04 L19 55m\n", " e1 8,850/62,312 loss 0.1375 nce 0.1371 mse 0.00042 acc 1.000 lr 5.81e-04 L30 55m\n", " e1 8,900/62,312 loss 0.4015 nce 0.4008 mse 0.00068 acc 1.000 lr 5.81e-04 L137 55m\n", " e1 8,950/62,312 loss 0.7404 nce 0.7396 mse 0.00087 acc 0.956 lr 5.81e-04 L8 56m\n", " e1 9,000/62,312 loss 0.1290 nce 0.1287 mse 0.00039 acc 1.000 lr 5.80e-04 L34 56m\n", " VAL r1 0.9965 cos 0.8110 self_cos +0.0083 erank 94.0 cv 0.0921 | frame r1 0.9985\n", " e1 9,050/62,312 loss 0.3016 nce 0.3010 mse 0.00064 acc 0.991 lr 5.80e-04 L12 56m\n", " e1 9,100/62,312 loss 0.1252 nce 0.1248 mse 0.00039 acc 1.000 lr 5.80e-04 L29 57m\n", " e1 9,150/62,312 loss 0.1331 nce 0.1326 mse 0.00043 acc 0.999 lr 5.80e-04 L23 57m\n", " e1 9,200/62,312 loss 0.5372 nce 0.5365 mse 0.00074 acc 1.000 lr 5.79e-04 L256 57m\n", " e1 9,250/62,312 loss 0.1310 nce 0.1306 mse 0.00038 acc 1.000 lr 5.79e-04 L36 57m\n", " e1 9,300/62,312 loss 0.1199 nce 0.1195 mse 0.00038 acc 1.000 lr 5.79e-04 L37 58m\n", " e1 9,350/62,312 loss 0.1187 nce 0.1183 mse 0.00038 acc 1.000 lr 5.78e-04 L27 58m\n", " e1 9,400/62,312 loss 0.2451 nce 0.2445 mse 0.00057 acc 0.996 lr 5.78e-04 L16 58m\n", " e1 9,450/62,312 loss 0.1227 nce 0.1224 mse 0.00039 acc 1.000 lr 5.78e-04 L37 59m\n", " e1 9,500/62,312 loss 0.2061 nce 0.2056 mse 0.00054 acc 0.997 lr 5.78e-04 L16 59m\n", " e1 9,550/62,312 loss 0.1672 nce 0.1667 mse 0.00050 acc 0.997 lr 5.77e-04 L18 59m\n", " e1 9,600/62,312 loss 0.1414 nce 0.1410 mse 0.00041 acc 1.000 lr 5.77e-04 L31 59m\n", " e1 9,650/62,312 loss 0.2462 nce 0.2457 mse 0.00053 acc 1.000 lr 5.77e-04 L94 60m\n", " e1 9,700/62,312 loss 0.1133 nce 0.1129 mse 0.00038 acc 1.000 lr 5.76e-04 L42 60m\n", " e1 9,750/62,312 loss 0.1200 nce 0.1196 mse 0.00039 acc 1.000 lr 5.76e-04 L29 61m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 9756)\n", " e1 9,800/62,312 loss 0.1296 nce 0.1291 mse 0.00044 acc 1.000 lr 5.76e-04 L22 61m\n", " e1 9,850/62,312 loss 0.1795 nce 0.1790 mse 0.00048 acc 1.000 lr 5.75e-04 L63 62m\n", " e1 9,900/62,312 loss 0.1175 nce 0.1171 mse 0.00039 acc 1.000 lr 5.75e-04 L26 62m\n", " e1 9,950/62,312 loss 0.1266 nce 0.1263 mse 0.00038 acc 1.000 lr 5.75e-04 L33 62m\n", " e1 10,000/62,312 loss 0.2060 nce 0.2055 mse 0.00050 acc 1.000 lr 5.75e-04 L88 62m\n", " VAL r1 0.9945 cos 0.8180 self_cos +0.0071 erank 95.5 cv 0.0846 | frame r1 0.9975\n", " e1 10,050/62,312 loss 0.1737 nce 0.1732 mse 0.00047 acc 0.999 lr 5.74e-04 L58 63m\n", " e1 10,100/62,312 loss 0.1653 nce 0.1649 mse 0.00045 acc 1.000 lr 5.74e-04 L53 63m\n", " e1 10,150/62,312 loss 0.1184 nce 0.1180 mse 0.00039 acc 1.000 lr 5.74e-04 L27 63m\n", " e1 10,200/62,312 loss 0.1105 nce 0.1101 mse 0.00036 acc 1.000 lr 5.73e-04 L34 64m\n", " e1 10,250/62,312 loss 0.1750 nce 0.1744 mse 0.00051 acc 0.999 lr 5.73e-04 L17 64m\n", " e1 10,300/62,312 loss 0.1253 nce 0.1250 mse 0.00038 acc 1.000 lr 5.73e-04 L30 64m\n", " e1 10,350/62,312 loss 0.3458 nce 0.3451 mse 0.00065 acc 1.000 lr 5.72e-04 L136 65m\n", " e1 10,400/62,312 loss 0.3095 nce 0.3089 mse 0.00061 acc 1.000 lr 5.72e-04 L116 65m\n", " e1 10,450/62,312 loss 0.2267 nce 0.2262 mse 0.00055 acc 0.993 lr 5.72e-04 L15 65m\n", " e1 10,500/62,312 loss 0.2858 nce 0.2851 mse 0.00063 acc 0.991 lr 5.71e-04 L12 65m\n", " e1 10,550/62,312 loss 0.1687 nce 0.1682 mse 0.00047 acc 1.000 lr 5.71e-04 L70 66m\n", " e1 10,600/62,312 loss 0.1831 nce 0.1827 mse 0.00049 acc 1.000 lr 5.71e-04 L65 66m\n", " e1 10,650/62,312 loss 0.1303 nce 0.1299 mse 0.00041 acc 1.000 lr 5.70e-04 L25 66m\n", " e1 10,700/62,312 loss 0.1113 nce 0.1109 mse 0.00038 acc 1.000 lr 5.70e-04 L27 67m\n", " e1 10,750/62,312 loss 0.1225 nce 0.1221 mse 0.00038 acc 1.000 lr 5.70e-04 L27 67m\n", " e1 10,800/62,312 loss 0.1186 nce 0.1182 mse 0.00039 acc 0.999 lr 5.69e-04 L27 67m\n", " e1 10,850/62,312 loss 0.5747 nce 0.5739 mse 0.00078 acc 1.000 lr 5.69e-04 L256 67m\n", " e1 10,900/62,312 loss 0.1968 nce 0.1963 mse 0.00053 acc 0.998 lr 5.69e-04 L17 68m\n", " e1 10,950/62,312 loss 0.1808 nce 0.1803 mse 0.00046 acc 1.000 lr 5.68e-04 L58 68m\n", " e1 11,000/62,312 loss 0.3003 nce 0.2996 mse 0.00064 acc 0.987 lr 5.68e-04 L12 68m\n", " VAL r1 0.9965 cos 0.8214 self_cos +0.0047 erank 95.4 cv 0.0741 | frame r1 0.9985\n", " e1 11,050/62,312 loss 0.1272 nce 0.1268 mse 0.00043 acc 1.000 lr 5.68e-04 L22 69m\n", " e1 11,100/62,312 loss 0.1139 nce 0.1136 mse 0.00038 acc 1.000 lr 5.67e-04 L25 69m\n", " e1 11,150/62,312 loss 0.7619 nce 0.7611 mse 0.00078 acc 0.892 lr 5.67e-04 L9 69m\n", " e1 11,200/62,312 loss 0.1209 nce 0.1205 mse 0.00038 acc 1.000 lr 5.66e-04 L41 69m\n", " e1 11,250/62,312 loss 0.1729 nce 0.1724 mse 0.00044 acc 1.000 lr 5.66e-04 L59 70m\n", " e1 11,300/62,312 loss 0.2223 nce 0.2218 mse 0.00054 acc 1.000 lr 5.66e-04 L94 70m\n", " e1 11,350/62,312 loss 0.1234 nce 0.1230 mse 0.00039 acc 0.999 lr 5.65e-04 L24 70m\n", " e1 11,400/62,312 loss 0.5239 nce 0.5232 mse 0.00070 acc 0.937 lr 5.65e-04 L10 71m\n", " e1 11,450/62,312 loss 0.1297 nce 0.1292 mse 0.00042 acc 0.999 lr 5.65e-04 L23 71m\n", " e1 11,500/62,312 loss 0.1274 nce 0.1270 mse 0.00039 acc 0.999 lr 5.64e-04 L37 71m\n", " e1 11,550/62,312 loss 0.1155 nce 0.1151 mse 0.00037 acc 1.000 lr 5.64e-04 L31 71m\n", " e1 11,600/62,312 loss 0.1239 nce 0.1235 mse 0.00041 acc 1.000 lr 5.64e-04 L31 72m\n", " e1 11,650/62,312 loss 0.1207 nce 0.1203 mse 0.00041 acc 1.000 lr 5.63e-04 L24 72m\n", " e1 11,700/62,312 loss 0.1446 nce 0.1442 mse 0.00043 acc 0.997 lr 5.63e-04 L22 72m\n", " e1 11,750/62,312 loss 0.1208 nce 0.1204 mse 0.00038 acc 1.000 lr 5.62e-04 L29 73m\n", " e1 11,800/62,312 loss 0.3846 nce 0.3839 mse 0.00067 acc 1.000 lr 5.62e-04 L147 73m\n", " e1 11,850/62,312 loss 0.2683 nce 0.2677 mse 0.00058 acc 0.990 lr 5.62e-04 L14 73m\n", " e1 11,900/62,312 loss 0.1170 nce 0.1166 mse 0.00038 acc 0.999 lr 5.61e-04 L26 74m\n", " e1 11,950/62,312 loss 0.2077 nce 0.2071 mse 0.00053 acc 1.000 lr 5.61e-04 L88 74m\n", " e1 12,000/62,312 loss 0.1226 nce 0.1223 mse 0.00037 acc 1.000 lr 5.61e-04 L45 74m\n", " VAL r1 0.9955 cos 0.8237 self_cos +0.0056 erank 95.5 cv 0.0894 | frame r1 0.9975\n", " e1 12,050/62,312 loss 0.1103 nce 0.1100 mse 0.00036 acc 1.000 lr 5.60e-04 L39 74m\n", " e1 12,100/62,312 loss 0.1069 nce 0.1065 mse 0.00038 acc 1.000 lr 5.60e-04 L26 75m\n", " e1 12,150/62,312 loss 0.2645 nce 0.2639 mse 0.00061 acc 0.994 lr 5.59e-04 L13 75m\n", " e1 12,200/62,312 loss 0.1150 nce 0.1147 mse 0.00036 acc 1.000 lr 5.59e-04 L38 75m\n", " e1 12,250/62,312 loss 0.3176 nce 0.3170 mse 0.00062 acc 1.000 lr 5.59e-04 L128 76m\n", " e1 12,300/62,312 loss 0.4966 nce 0.4959 mse 0.00070 acc 0.939 lr 5.58e-04 L10 76m\n", " e1 12,350/62,312 loss 0.1230 nce 0.1226 mse 0.00042 acc 0.998 lr 5.58e-04 L21 76m\n", " e1 12,400/62,312 loss 0.2921 nce 0.2915 mse 0.00060 acc 1.000 lr 5.57e-04 L110 77m\n", " e1 12,450/62,312 loss 0.6595 nce 0.6587 mse 0.00082 acc 0.999 lr 5.57e-04 L256 77m\n", " e1 12,500/62,312 loss 0.1118 nce 0.1114 mse 0.00038 acc 1.000 lr 5.57e-04 L31 77m\n", " e1 12,550/62,312 loss 0.1156 nce 0.1153 mse 0.00040 acc 1.000 lr 5.56e-04 L24 77m\n", " e1 12,600/62,312 loss 0.1541 nce 0.1537 mse 0.00042 acc 1.000 lr 5.56e-04 L49 78m\n", " e1 12,650/62,312 loss 0.1244 nce 0.1240 mse 0.00044 acc 1.000 lr 5.55e-04 L21 78m\n", " e1 12,700/62,312 loss 0.1148 nce 0.1144 mse 0.00036 acc 1.000 lr 5.55e-04 L31 78m\n", " e1 12,750/62,312 loss 0.1195 nce 0.1191 mse 0.00038 acc 0.999 lr 5.55e-04 L26 79m\n", " e1 12,800/62,312 loss 0.1155 nce 0.1151 mse 0.00036 acc 1.000 lr 5.54e-04 L35 79m\n", " e1 12,850/62,312 loss 0.2625 nce 0.2619 mse 0.00060 acc 0.994 lr 5.54e-04 L13 79m\n", " e1 12,900/62,312 loss 0.1182 nce 0.1178 mse 0.00037 acc 1.000 lr 5.53e-04 L34 79m\n", " e1 12,950/62,312 loss 0.1154 nce 0.1150 mse 0.00040 acc 1.000 lr 5.53e-04 L28 80m\n", " e1 13,000/62,312 loss 0.1199 nce 0.1195 mse 0.00038 acc 1.000 lr 5.52e-04 L43 80m\n", " VAL r1 0.9940 cos 0.8184 self_cos +0.0081 erank 94.1 cv 0.0759 | frame r1 0.9985\n", " e1 13,050/62,312 loss 0.1899 nce 0.1893 mse 0.00052 acc 0.998 lr 5.52e-04 L16 80m\n", " e1 13,100/62,312 loss 0.1171 nce 0.1167 mse 0.00039 acc 1.000 lr 5.52e-04 L23 81m\n", " e1 13,150/62,312 loss 0.1069 nce 0.1065 mse 0.00035 acc 1.000 lr 5.51e-04 L36 81m\n", " e1 13,200/62,312 loss 0.2556 nce 0.2550 mse 0.00056 acc 1.000 lr 5.51e-04 L105 81m\n", " e1 13,250/62,312 loss 0.4021 nce 0.4015 mse 0.00067 acc 1.000 lr 5.50e-04 L171 82m\n", " e1 13,300/62,312 loss 0.1532 nce 0.1527 mse 0.00048 acc 0.999 lr 5.50e-04 L18 82m\n", " e1 13,350/62,312 loss 0.3920 nce 0.3914 mse 0.00067 acc 1.000 lr 5.49e-04 L169 82m\n", " e1 13,400/62,312 loss 0.1058 nce 0.1054 mse 0.00036 acc 1.000 lr 5.49e-04 L31 83m\n", " e1 13,450/62,312 loss 0.1048 nce 0.1044 mse 0.00036 acc 1.000 lr 5.49e-04 L31 83m\n", " e1 13,500/62,312 loss 0.3792 nce 0.3785 mse 0.00066 acc 1.000 lr 5.48e-04 L171 83m\n", " e1 13,550/62,312 loss 0.1424 nce 0.1419 mse 0.00041 acc 0.999 lr 5.48e-04 L49 83m\n", " e1 13,600/62,312 loss 0.3481 nce 0.3475 mse 0.00065 acc 1.000 lr 5.47e-04 L148 84m\n", " e1 13,650/62,312 loss 0.2536 nce 0.2530 mse 0.00056 acc 1.000 lr 5.47e-04 L110 84m\n", " e1 13,700/62,312 loss 0.1100 nce 0.1097 mse 0.00036 acc 1.000 lr 5.46e-04 L38 84m\n", " e1 13,750/62,312 loss 0.1041 nce 0.1037 mse 0.00036 acc 1.000 lr 5.46e-04 L35 85m\n", " e1 13,800/62,312 loss 0.1128 nce 0.1124 mse 0.00040 acc 0.998 lr 5.46e-04 L22 85m\n", " e1 13,850/62,312 loss 0.1129 nce 0.1126 mse 0.00037 acc 1.000 lr 5.45e-04 L30 85m\n", " e1 13,900/62,312 loss 0.1462 nce 0.1457 mse 0.00046 acc 0.999 lr 5.45e-04 L76 86m\n", " e1 13,950/62,312 loss 0.1185 nce 0.1181 mse 0.00041 acc 0.999 lr 5.44e-04 L23 86m\n", " e1 14,000/62,312 loss 0.1352 nce 0.1347 mse 0.00047 acc 0.999 lr 5.44e-04 L18 86m\n", " VAL r1 0.9970 cos 0.8247 self_cos +0.0054 erank 97.5 cv 0.0798 | frame r1 0.9985\n", " e1 14,050/62,312 loss 0.1775 nce 0.1770 mse 0.00048 acc 0.999 lr 5.43e-04 L83 87m\n", " e1 14,100/62,312 loss 0.5130 nce 0.5122 mse 0.00074 acc 1.000 lr 5.43e-04 L256 87m\n", " e1 14,150/62,312 loss 0.1866 nce 0.1861 mse 0.00049 acc 0.998 lr 5.42e-04 L59 87m\n", " e1 14,200/62,312 loss 0.1375 nce 0.1371 mse 0.00040 acc 1.000 lr 5.42e-04 L49 88m\n", " e1 14,250/62,312 loss 0.5022 nce 0.5015 mse 0.00073 acc 1.000 lr 5.41e-04 L256 88m\n", " e1 14,300/62,312 loss 0.3383 nce 0.3376 mse 0.00063 acc 1.000 lr 5.41e-04 L128 88m\n", " e1 14,350/62,312 loss 0.3896 nce 0.3889 mse 0.00068 acc 1.000 lr 5.40e-04 L172 89m\n", " e1 14,400/62,312 loss 0.1277 nce 0.1273 mse 0.00040 acc 1.000 lr 5.40e-04 L48 89m\n", " e1 14,450/62,312 loss 0.1123 nce 0.1119 mse 0.00037 acc 1.000 lr 5.40e-04 L37 89m\n", " e1 14,500/62,312 loss 0.1033 nce 0.1029 mse 0.00037 acc 0.999 lr 5.39e-04 L30 89m\n", " e1 14,550/62,312 loss 0.7764 nce 0.7756 mse 0.00077 acc 0.886 lr 5.39e-04 L9 90m\n", " e1 14,600/62,312 loss 0.1062 nce 0.1058 mse 0.00037 acc 1.000 lr 5.38e-04 L26 90m\n", " e1 14,650/62,312 loss 0.1300 nce 0.1296 mse 0.00042 acc 0.999 lr 5.38e-04 L49 90m\n", " e1 14,700/62,312 loss 0.2562 nce 0.2556 mse 0.00055 acc 1.000 lr 5.37e-04 L110 91m\n", " e1 14,750/62,312 loss 0.1048 nce 0.1044 mse 0.00036 acc 1.000 lr 5.37e-04 L35 91m\n", " e1 14,800/62,312 loss 0.1005 nce 0.1002 mse 0.00036 acc 1.000 lr 5.36e-04 L31 91m\n", " e1 14,850/62,312 loss 0.1212 nce 0.1208 mse 0.00043 acc 1.000 lr 5.36e-04 L21 92m\n", " e1 14,900/62,312 loss 0.1627 nce 0.1622 mse 0.00045 acc 1.000 lr 5.35e-04 L70 92m\n", " e1 14,950/62,312 loss 0.1053 nce 0.1049 mse 0.00037 acc 1.000 lr 5.35e-04 L29 92m\n", " e1 15,000/62,312 loss 0.2913 nce 0.2907 mse 0.00061 acc 1.000 lr 5.34e-04 L137 92m\n", " VAL r1 0.9975 cos 0.8279 self_cos +0.0058 erank 97.2 cv 0.0822 | frame r1 0.9980\n", " e1 15,050/62,312 loss 0.1040 nce 0.1037 mse 0.00035 acc 1.000 lr 5.34e-04 L36 93m\n", " e1 15,100/62,312 loss 0.2326 nce 0.2321 mse 0.00060 acc 0.992 lr 5.33e-04 L12 93m\n", " e1 15,150/62,312 loss 0.1134 nce 0.1130 mse 0.00042 acc 0.999 lr 5.33e-04 L21 93m\n", " e1 15,200/62,312 loss 0.1043 nce 0.1039 mse 0.00036 acc 1.000 lr 5.32e-04 L30 94m\n", " e1 15,250/62,312 loss 0.2178 nce 0.2173 mse 0.00051 acc 1.000 lr 5.32e-04 L95 94m\n", " e1 15,300/62,312 loss 0.1077 nce 0.1074 mse 0.00036 acc 1.000 lr 5.31e-04 L37 94m\n", " e1 15,350/62,312 loss 0.6267 nce 0.6258 mse 0.00084 acc 0.962 lr 5.31e-04 L8 94m\n", " e1 15,400/62,312 loss 0.3619 nce 0.3613 mse 0.00064 acc 1.000 lr 5.30e-04 L137 95m\n", " e1 15,450/62,312 loss 0.2624 nce 0.2618 mse 0.00057 acc 1.000 lr 5.30e-04 L100 95m\n", " e1 15,500/62,312 loss 0.1587 nce 0.1583 mse 0.00043 acc 1.000 lr 5.29e-04 L53 95m\n", " e1 15,550/62,312 loss 0.2366 nce 0.2360 mse 0.00060 acc 0.993 lr 5.29e-04 L12 95m\n", " e2 15,600/62,312 loss 0.1296 nce 0.1292 mse 0.00045 acc 0.999 lr 5.28e-04 L20 96m\n", " e2 15,650/62,312 loss 0.1022 nce 0.1018 mse 0.00035 acc 1.000 lr 5.28e-04 L41 96m\n", " e2 15,700/62,312 loss 0.1039 nce 0.1036 mse 0.00035 acc 1.000 lr 5.27e-04 L38 96m\n", " e2 15,750/62,312 loss 0.1041 nce 0.1038 mse 0.00036 acc 1.000 lr 5.27e-04 L33 97m\n", " e2 15,800/62,312 loss 0.4631 nce 0.4625 mse 0.00068 acc 0.935 lr 5.26e-04 L10 97m\n", " e2 15,850/62,312 loss 0.1625 nce 0.1621 mse 0.00046 acc 0.999 lr 5.26e-04 L82 97m\n", " e2 15,900/62,312 loss 0.1034 nce 0.1031 mse 0.00035 acc 1.000 lr 5.25e-04 L30 98m\n", " e2 15,950/62,312 loss 0.2984 nce 0.2978 mse 0.00063 acc 1.000 lr 5.25e-04 L128 98m\n", " e2 16,000/62,312 loss 0.1028 nce 0.1025 mse 0.00036 acc 1.000 lr 5.24e-04 L29 98m\n", " VAL r1 0.9970 cos 0.8281 self_cos +0.0075 erank 96.9 cv 0.0839 | frame r1 0.9980\n", " e2 16,050/62,312 loss 0.2502 nce 0.2496 mse 0.00058 acc 0.995 lr 5.24e-04 L13 99m\n", " e2 16,100/62,312 loss 0.1633 nce 0.1629 mse 0.00049 acc 1.000 lr 5.23e-04 L83 99m\n", " e2 16,150/62,312 loss 0.1066 nce 0.1062 mse 0.00038 acc 1.000 lr 5.23e-04 L25 99m\n", " e2 16,200/62,312 loss 0.1058 nce 0.1054 mse 0.00036 acc 1.000 lr 5.22e-04 L31 99m\n", " e2 16,250/62,312 loss 0.1037 nce 0.1033 mse 0.00036 acc 1.000 lr 5.22e-04 L31 100m\n", " e2 16,300/62,312 loss 0.0969 nce 0.0965 mse 0.00036 acc 1.000 lr 5.21e-04 L42 100m\n", " e2 16,350/62,312 loss 0.1039 nce 0.1036 mse 0.00035 acc 0.999 lr 5.21e-04 L35 100m\n", " e2 16,400/62,312 loss 0.1087 nce 0.1083 mse 0.00037 acc 1.000 lr 5.20e-04 L28 101m\n", " e2 16,450/62,312 loss 0.2264 nce 0.2258 mse 0.00055 acc 0.995 lr 5.20e-04 L14 101m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 16479)\n", " e2 16,500/62,312 loss 0.1230 nce 0.1226 mse 0.00042 acc 0.998 lr 5.19e-04 L21 102m\n", " e2 16,550/62,312 loss 0.2578 nce 0.2572 mse 0.00056 acc 1.000 lr 5.19e-04 L110 102m\n", " e2 16,600/62,312 loss 0.1126 nce 0.1122 mse 0.00041 acc 1.000 lr 5.18e-04 L21 102m\n", " e2 16,650/62,312 loss 0.1125 nce 0.1121 mse 0.00041 acc 1.000 lr 5.17e-04 L22 103m\n", " e2 16,700/62,312 loss 0.1498 nce 0.1494 mse 0.00047 acc 0.998 lr 5.17e-04 L17 103m\n", " e2 16,750/62,312 loss 0.1104 nce 0.1100 mse 0.00040 acc 0.999 lr 5.16e-04 L22 103m\n", " e2 16,800/62,312 loss 0.2742 nce 0.2736 mse 0.00059 acc 0.992 lr 5.16e-04 L13 104m\n", " e2 16,850/62,312 loss 0.2054 nce 0.2049 mse 0.00053 acc 0.996 lr 5.15e-04 L15 104m\n", " e2 16,900/62,312 loss 0.3070 nce 0.3064 mse 0.00063 acc 0.975 lr 5.15e-04 L11 104m\n", " e2 16,950/62,312 loss 0.0950 nce 0.0946 mse 0.00034 acc 1.000 lr 5.14e-04 L38 104m\n", " e2 17,000/62,312 loss 0.1165 nce 0.1161 mse 0.00036 acc 1.000 lr 5.14e-04 L34 105m\n", " VAL r1 0.9975 cos 0.8242 self_cos +0.0066 erank 97.7 cv 0.0745 | frame r1 0.9985\n", " e2 17,050/62,312 loss 0.1067 nce 0.1063 mse 0.00038 acc 1.000 lr 5.13e-04 L23 105m\n", " e2 17,100/62,312 loss 0.1039 nce 0.1035 mse 0.00036 acc 1.000 lr 5.13e-04 L32 106m\n", " e2 17,150/62,312 loss 0.2509 nce 0.2503 mse 0.00058 acc 1.000 lr 5.12e-04 L116 106m\n", " e2 17,200/62,312 loss 0.2242 nce 0.2237 mse 0.00053 acc 1.000 lr 5.11e-04 L101 106m\n", " e2 17,250/62,312 loss 0.1486 nce 0.1481 mse 0.00043 acc 0.999 lr 5.11e-04 L65 107m\n", " e2 17,300/62,312 loss 0.0994 nce 0.0990 mse 0.00035 acc 1.000 lr 5.10e-04 L26 107m\n", " e2 17,350/62,312 loss 0.2255 nce 0.2250 mse 0.00053 acc 1.000 lr 5.10e-04 L105 107m\n", " e2 17,400/62,312 loss 0.7389 nce 0.7382 mse 0.00076 acc 0.883 lr 5.09e-04 L9 107m\n", " e2 17,450/62,312 loss 0.1819 nce 0.1814 mse 0.00051 acc 0.993 lr 5.09e-04 L16 108m\n", " e2 17,500/62,312 loss 0.1052 nce 0.1048 mse 0.00037 acc 1.000 lr 5.08e-04 L27 108m\n", " e2 17,550/62,312 loss 0.2969 nce 0.2963 mse 0.00062 acc 1.000 lr 5.08e-04 L136 108m\n", " e2 17,600/62,312 loss 0.1047 nce 0.1043 mse 0.00036 acc 0.999 lr 5.07e-04 L27 109m\n", " e2 17,650/62,312 loss 0.1151 nce 0.1148 mse 0.00039 acc 0.997 lr 5.06e-04 L23 109m\n", " e2 17,700/62,312 loss 0.1027 nce 0.1024 mse 0.00035 acc 1.000 lr 5.06e-04 L37 109m\n", " e2 17,750/62,312 loss 0.1093 nce 0.1090 mse 0.00037 acc 0.999 lr 5.05e-04 L28 110m\n", " e2 17,800/62,312 loss 0.1145 nce 0.1141 mse 0.00039 acc 0.999 lr 5.05e-04 L23 110m\n", " e2 17,850/62,312 loss 0.3160 nce 0.3153 mse 0.00063 acc 0.968 lr 5.04e-04 L11 110m\n", " e2 17,900/62,312 loss 0.3298 nce 0.3291 mse 0.00064 acc 1.000 lr 5.04e-04 L169 110m\n", " e2 17,950/62,312 loss 0.1002 nce 0.0999 mse 0.00034 acc 1.000 lr 5.03e-04 L31 111m\n", " e2 18,000/62,312 loss 0.1026 nce 0.1022 mse 0.00036 acc 0.999 lr 5.02e-04 L27 111m\n", " VAL r1 0.9975 cos 0.8292 self_cos +0.0052 erank 99.9 cv 0.0811 | frame r1 0.9980\n", " e2 18,050/62,312 loss 0.1002 nce 0.0998 mse 0.00035 acc 1.000 lr 5.02e-04 L34 111m\n", " e2 18,100/62,312 loss 0.3113 nce 0.3107 mse 0.00062 acc 0.968 lr 5.01e-04 L11 112m\n", " e2 18,150/62,312 loss 0.2689 nce 0.2683 mse 0.00058 acc 1.000 lr 5.01e-04 L121 112m\n", " e2 18,200/62,312 loss 0.0920 nce 0.0917 mse 0.00034 acc 1.000 lr 5.00e-04 L41 112m\n", " e2 18,250/62,312 loss 0.0986 nce 0.0983 mse 0.00036 acc 1.000 lr 5.00e-04 L26 113m\n", " e2 18,300/62,312 loss 0.1021 nce 0.1017 mse 0.00036 acc 1.000 lr 4.99e-04 L27 113m\n", " e2 18,350/62,312 loss 0.1462 nce 0.1457 mse 0.00044 acc 1.000 lr 4.98e-04 L70 113m\n", " e2 18,400/62,312 loss 0.1078 nce 0.1074 mse 0.00035 acc 1.000 lr 4.98e-04 L32 113m\n", " e2 18,450/62,312 loss 0.1448 nce 0.1443 mse 0.00043 acc 0.998 lr 4.97e-04 L53 114m\n", " e2 18,500/62,312 loss 0.0916 nce 0.0912 mse 0.00035 acc 1.000 lr 4.97e-04 L30 114m\n", " e2 18,550/62,312 loss 0.1071 nce 0.1067 mse 0.00039 acc 1.000 lr 4.96e-04 L24 114m\n", " e2 18,600/62,312 loss 0.0970 nce 0.0966 mse 0.00034 acc 1.000 lr 4.95e-04 L29 115m\n", " e2 18,650/62,312 loss 0.0994 nce 0.0991 mse 0.00034 acc 0.999 lr 4.95e-04 L36 115m\n", " e2 18,700/62,312 loss 0.4719 nce 0.4712 mse 0.00073 acc 1.000 lr 4.94e-04 L256 115m\n", " e2 18,750/62,312 loss 0.3039 nce 0.3033 mse 0.00060 acc 1.000 lr 4.94e-04 L136 116m\n", " e2 18,800/62,312 loss 0.2767 nce 0.2761 mse 0.00057 acc 1.000 lr 4.93e-04 L122 116m\n", " e2 18,850/62,312 loss 0.1071 nce 0.1067 mse 0.00036 acc 1.000 lr 4.92e-04 L34 116m\n", " e2 18,900/62,312 loss 0.2560 nce 0.2554 mse 0.00056 acc 1.000 lr 4.92e-04 L105 117m\n", " e2 18,950/62,312 loss 0.1383 nce 0.1378 mse 0.00048 acc 0.998 lr 4.91e-04 L17 117m\n", " e2 19,000/62,312 loss 0.1485 nce 0.1481 mse 0.00049 acc 0.998 lr 4.91e-04 L16 117m\n", " VAL r1 0.9955 cos 0.8264 self_cos +0.0042 erank 98.6 cv 0.0700 | frame r1 0.9980\n", " e2 19,050/62,312 loss 0.1017 nce 0.1014 mse 0.00036 acc 1.000 lr 4.90e-04 L25 117m\n", " e2 19,100/62,312 loss 0.1566 nce 0.1562 mse 0.00046 acc 1.000 lr 4.89e-04 L82 118m\n", " e2 19,150/62,312 loss 0.1005 nce 0.1002 mse 0.00035 acc 1.000 lr 4.89e-04 L26 118m\n", " e2 19,200/62,312 loss 0.1418 nce 0.1413 mse 0.00045 acc 1.000 lr 4.88e-04 L76 118m\n", " e2 19,250/62,312 loss 0.1364 nce 0.1359 mse 0.00043 acc 0.999 lr 4.88e-04 L69 119m\n", " e2 19,300/62,312 loss 0.3085 nce 0.3078 mse 0.00063 acc 0.964 lr 4.87e-04 L11 119m\n", " e2 19,350/62,312 loss 0.1408 nce 0.1403 mse 0.00048 acc 1.000 lr 4.86e-04 L17 119m\n", " e2 19,400/62,312 loss 0.2109 nce 0.2104 mse 0.00052 acc 1.000 lr 4.86e-04 L100 119m\n", " e2 19,450/62,312 loss 0.0905 nce 0.0901 mse 0.00035 acc 1.000 lr 4.85e-04 L24 120m\n", " e2 19,500/62,312 loss 0.1028 nce 0.1025 mse 0.00035 acc 1.000 lr 4.85e-04 L35 120m\n", " e2 19,550/62,312 loss 0.0949 nce 0.0946 mse 0.00036 acc 1.000 lr 4.84e-04 L24 120m\n", " e2 19,600/62,312 loss 0.0982 nce 0.0978 mse 0.00035 acc 1.000 lr 4.83e-04 L35 121m\n", " e2 19,650/62,312 loss 0.0970 nce 0.0966 mse 0.00036 acc 1.000 lr 4.83e-04 L25 121m\n", " e2 19,700/62,312 loss 0.1146 nce 0.1142 mse 0.00036 acc 1.000 lr 4.82e-04 L26 121m\n", " e2 19,750/62,312 loss 0.1023 nce 0.1019 mse 0.00041 acc 0.999 lr 4.82e-04 L21 121m\n", " e2 19,800/62,312 loss 0.2832 nce 0.2826 mse 0.00059 acc 1.000 lr 4.81e-04 L115 122m\n", " e2 19,850/62,312 loss 0.2734 nce 0.2729 mse 0.00058 acc 1.000 lr 4.80e-04 L122 122m\n", " e2 19,900/62,312 loss 0.1235 nce 0.1231 mse 0.00039 acc 1.000 lr 4.80e-04 L33 122m\n", " e2 19,950/62,312 loss 0.1079 nce 0.1075 mse 0.00037 acc 1.000 lr 4.79e-04 L27 123m\n", " e2 20,000/62,312 loss 0.1001 nce 0.0997 mse 0.00035 acc 1.000 lr 4.78e-04 L37 123m\n", " VAL r1 0.9960 cos 0.8302 self_cos +0.0042 erank 98.3 cv 0.0712 | frame r1 0.9985\n", " e2 20,050/62,312 loss 0.1063 nce 0.1060 mse 0.00035 acc 1.000 lr 4.78e-04 L29 123m\n", " e2 20,100/62,312 loss 0.2899 nce 0.2893 mse 0.00060 acc 1.000 lr 4.77e-04 L128 124m\n", " e2 20,150/62,312 loss 0.7165 nce 0.7157 mse 0.00075 acc 0.896 lr 4.77e-04 L9 124m\n", " e2 20,200/62,312 loss 0.2458 nce 0.2453 mse 0.00054 acc 1.000 lr 4.76e-04 L110 124m\n", " e2 20,250/62,312 loss 0.2314 nce 0.2309 mse 0.00055 acc 1.000 lr 4.75e-04 L110 125m\n", " e2 20,300/62,312 loss 0.2971 nce 0.2965 mse 0.00060 acc 1.000 lr 4.75e-04 L137 125m\n", " e2 20,350/62,312 loss 0.5285 nce 0.5277 mse 0.00080 acc 0.972 lr 4.74e-04 L8 125m\n", " e2 20,400/62,312 loss 0.2546 nce 0.2541 mse 0.00057 acc 1.000 lr 4.73e-04 L116 125m\n", " e2 20,450/62,312 loss 0.1024 nce 0.1021 mse 0.00035 acc 1.000 lr 4.73e-04 L36 126m\n", " e2 20,500/62,312 loss 0.1518 nce 0.1513 mse 0.00046 acc 0.999 lr 4.72e-04 L82 126m\n", " e2 20,550/62,312 loss 0.2510 nce 0.2504 mse 0.00058 acc 1.000 lr 4.71e-04 L122 126m\n", " e2 20,600/62,312 loss 0.1031 nce 0.1027 mse 0.00035 acc 1.000 lr 4.71e-04 L38 126m\n", " e2 20,650/62,312 loss 0.1009 nce 0.1006 mse 0.00035 acc 1.000 lr 4.70e-04 L25 127m\n", " e2 20,700/62,312 loss 0.2422 nce 0.2416 mse 0.00054 acc 1.000 lr 4.70e-04 L110 127m\n", " e2 20,750/62,312 loss 0.1017 nce 0.1014 mse 0.00035 acc 1.000 lr 4.69e-04 L28 127m\n", " e2 20,800/62,312 loss 0.2444 nce 0.2439 mse 0.00054 acc 1.000 lr 4.68e-04 L105 128m\n", " e2 20,850/62,312 loss 0.2319 nce 0.2312 mse 0.00061 acc 0.992 lr 4.68e-04 L12 128m\n", " e2 20,900/62,312 loss 0.2888 nce 0.2882 mse 0.00059 acc 1.000 lr 4.67e-04 L128 128m\n", " e2 20,950/62,312 loss 0.1015 nce 0.1012 mse 0.00035 acc 1.000 lr 4.66e-04 L33 129m\n", " e2 21,000/62,312 loss 0.1157 nce 0.1153 mse 0.00043 acc 0.998 lr 4.66e-04 L19 129m\n", " VAL r1 0.9970 cos 0.8332 self_cos +0.0071 erank 99.9 cv 0.0772 | frame r1 0.9985\n", " e2 21,050/62,312 loss 0.1035 nce 0.1031 mse 0.00036 acc 1.000 lr 4.65e-04 L32 129m\n", " e2 21,100/62,312 loss 0.1412 nce 0.1407 mse 0.00045 acc 1.000 lr 4.64e-04 L76 130m\n", " e2 21,150/62,312 loss 0.1167 nce 0.1163 mse 0.00038 acc 1.000 lr 4.64e-04 L49 130m\n", " e2 21,200/62,312 loss 0.0939 nce 0.0936 mse 0.00035 acc 1.000 lr 4.63e-04 L26 130m\n", " e2 21,250/62,312 loss 0.0939 nce 0.0936 mse 0.00034 acc 1.000 lr 4.62e-04 L31 130m\n", " e2 21,300/62,312 loss 0.0953 nce 0.0950 mse 0.00034 acc 1.000 lr 4.62e-04 L36 131m\n", " e2 21,350/62,312 loss 0.2468 nce 0.2462 mse 0.00056 acc 1.000 lr 4.61e-04 L116 131m\n", " e2 21,400/62,312 loss 0.1033 nce 0.1030 mse 0.00035 acc 1.000 lr 4.60e-04 L29 131m\n", " e2 21,450/62,312 loss 0.2318 nce 0.2312 mse 0.00054 acc 1.000 lr 4.60e-04 L105 132m\n", " e2 21,500/62,312 loss 0.1913 nce 0.1908 mse 0.00053 acc 0.997 lr 4.59e-04 L15 132m\n", " e2 21,550/62,312 loss 0.1008 nce 0.1005 mse 0.00035 acc 1.000 lr 4.58e-04 L37 132m\n", " e2 21,600/62,312 loss 0.1382 nce 0.1378 mse 0.00041 acc 1.000 lr 4.58e-04 L53 133m\n", " e2 21,650/62,312 loss 0.0919 nce 0.0916 mse 0.00034 acc 0.999 lr 4.57e-04 L39 133m\n", " e2 21,700/62,312 loss 0.1012 nce 0.1009 mse 0.00034 acc 1.000 lr 4.56e-04 L35 133m\n", " e2 21,750/62,312 loss 0.2153 nce 0.2147 mse 0.00053 acc 1.000 lr 4.56e-04 L100 134m\n", " e2 21,800/62,312 loss 0.0966 nce 0.0963 mse 0.00034 acc 1.000 lr 4.55e-04 L32 134m\n", " e2 21,850/62,312 loss 0.2747 nce 0.2742 mse 0.00059 acc 1.000 lr 4.54e-04 L128 134m\n", " e2 21,900/62,312 loss 0.1705 nce 0.1700 mse 0.00048 acc 1.000 lr 4.54e-04 L89 135m\n", " e2 21,950/62,312 loss 0.0926 nce 0.0922 mse 0.00034 acc 1.000 lr 4.53e-04 L28 135m\n", " e2 22,000/62,312 loss 0.1622 nce 0.1617 mse 0.00047 acc 1.000 lr 4.52e-04 L83 135m\n", " VAL r1 0.9975 cos 0.8314 self_cos +0.0066 erank 98.6 cv 0.0663 | frame r1 0.9980\n", " e2 22,050/62,312 loss 0.0954 nce 0.0950 mse 0.00033 acc 1.000 lr 4.52e-04 L38 136m\n", " e2 22,100/62,312 loss 0.1028 nce 0.1024 mse 0.00035 acc 1.000 lr 4.51e-04 L37 136m\n", " e2 22,150/62,312 loss 0.1908 nce 0.1903 mse 0.00052 acc 1.000 lr 4.50e-04 L94 136m\n", " e2 22,200/62,312 loss 0.1017 nce 0.1013 mse 0.00037 acc 1.000 lr 4.50e-04 L25 137m\n", " e2 22,250/62,312 loss 0.0991 nce 0.0987 mse 0.00035 acc 1.000 lr 4.49e-04 L29 137m\n", " e2 22,300/62,312 loss 0.0952 nce 0.0949 mse 0.00034 acc 1.000 lr 4.48e-04 L43 137m\n", " e2 22,350/62,312 loss 0.2377 nce 0.2372 mse 0.00058 acc 0.988 lr 4.48e-04 L12 137m\n", " e2 22,400/62,312 loss 0.1495 nce 0.1490 mse 0.00045 acc 1.000 lr 4.47e-04 L82 138m\n", " e2 22,450/62,312 loss 0.1471 nce 0.1466 mse 0.00048 acc 0.997 lr 4.46e-04 L16 138m\n", " e2 22,500/62,312 loss 0.1257 nce 0.1253 mse 0.00045 acc 1.000 lr 4.46e-04 L18 138m\n", " e2 22,550/62,312 loss 0.0934 nce 0.0931 mse 0.00034 acc 1.000 lr 4.45e-04 L30 139m\n", " e2 22,600/62,312 loss 0.2068 nce 0.2063 mse 0.00051 acc 1.000 lr 4.44e-04 L100 139m\n", " e2 22,650/62,312 loss 0.1109 nce 0.1105 mse 0.00042 acc 1.000 lr 4.44e-04 L20 139m\n", " e2 22,700/62,312 loss 0.1041 nce 0.1038 mse 0.00036 acc 1.000 lr 4.43e-04 L27 140m\n", " e2 22,750/62,312 loss 0.7783 nce 0.7775 mse 0.00079 acc 0.885 lr 4.42e-04 L9 140m\n", " e2 22,800/62,312 loss 0.3299 nce 0.3293 mse 0.00066 acc 1.000 lr 4.42e-04 L170 140m\n", " e2 22,850/62,312 loss 0.1001 nce 0.0997 mse 0.00035 acc 0.999 lr 4.41e-04 L25 141m\n", " e2 22,900/62,312 loss 0.2741 nce 0.2735 mse 0.00060 acc 1.000 lr 4.40e-04 L128 141m\n", " e2 22,950/62,312 loss 0.1182 nce 0.1178 mse 0.00045 acc 0.998 lr 4.40e-04 L18 141m\n", " e2 23,000/62,312 loss 0.2122 nce 0.2117 mse 0.00052 acc 1.000 lr 4.39e-04 L99 142m\n", " VAL r1 0.9965 cos 0.8348 self_cos +0.0034 erank 100.2 cv 0.0598 | frame r1 0.9985\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 23000)\n", " e2 23,050/62,312 loss 0.0946 nce 0.0942 mse 0.00034 acc 1.000 lr 4.38e-04 L32 142m\n", " e2 23,100/62,312 loss 0.1386 nce 0.1382 mse 0.00042 acc 1.000 lr 4.38e-04 L53 143m\n", " e2 23,150/62,312 loss 0.2291 nce 0.2285 mse 0.00059 acc 0.992 lr 4.37e-04 L12 143m\n", " e2 23,200/62,312 loss 0.0958 nce 0.0955 mse 0.00035 acc 1.000 lr 4.36e-04 L31 143m\n", " e2 23,250/62,312 loss 0.1405 nce 0.1401 mse 0.00043 acc 1.000 lr 4.35e-04 L70 144m\n", " e2 23,300/62,312 loss 0.0935 nce 0.0931 mse 0.00034 acc 1.000 lr 4.35e-04 L28 144m\n", " e2 23,350/62,312 loss 0.1872 nce 0.1867 mse 0.00050 acc 0.999 lr 4.34e-04 L88 144m\n", " e2 23,400/62,312 loss 0.0905 nce 0.0902 mse 0.00034 acc 1.000 lr 4.33e-04 L39 144m\n", " e2 23,450/62,312 loss 0.0938 nce 0.0934 mse 0.00034 acc 1.000 lr 4.33e-04 L33 145m\n", " e2 23,500/62,312 loss 0.2773 nce 0.2767 mse 0.00059 acc 1.000 lr 4.32e-04 L136 145m\n", " e2 23,550/62,312 loss 0.0983 nce 0.0979 mse 0.00035 acc 0.999 lr 4.31e-04 L45 145m\n", " e2 23,600/62,312 loss 0.1016 nce 0.1012 mse 0.00034 acc 1.000 lr 4.31e-04 L33 146m\n", " e2 23,650/62,312 loss 0.2847 nce 0.2841 mse 0.00059 acc 1.000 lr 4.30e-04 L136 146m\n", " e2 23,700/62,312 loss 0.0965 nce 0.0961 mse 0.00035 acc 1.000 lr 4.29e-04 L25 146m\n", " e2 23,750/62,312 loss 0.0911 nce 0.0908 mse 0.00034 acc 0.999 lr 4.28e-04 L42 146m\n", " e2 23,800/62,312 loss 0.0933 nce 0.0930 mse 0.00034 acc 1.000 lr 4.28e-04 L36 147m\n", " e2 23,850/62,312 loss 0.1025 nce 0.1020 mse 0.00041 acc 0.999 lr 4.27e-04 L20 147m\n", " e2 23,900/62,312 loss 0.0916 nce 0.0913 mse 0.00034 acc 0.999 lr 4.26e-04 L29 147m\n", " e2 23,950/62,312 loss 0.0886 nce 0.0883 mse 0.00033 acc 1.000 lr 4.26e-04 L39 148m\n", " e2 24,000/62,312 loss 0.1630 nce 0.1626 mse 0.00047 acc 1.000 lr 4.25e-04 L88 148m\n", " VAL r1 0.9980 cos 0.8336 self_cos +0.0041 erank 100.1 cv 0.0667 | frame r1 0.9985\n", " e2 24,050/62,312 loss 0.0920 nce 0.0916 mse 0.00034 acc 1.000 lr 4.24e-04 L28 148m\n", " e2 24,100/62,312 loss 0.4530 nce 0.4523 mse 0.00067 acc 0.930 lr 4.24e-04 L10 148m\n", " e2 24,150/62,312 loss 0.1059 nce 0.1055 mse 0.00038 acc 1.000 lr 4.23e-04 L27 149m\n", " e2 24,200/62,312 loss 0.0829 nce 0.0826 mse 0.00034 acc 1.000 lr 4.22e-04 L42 149m\n", " e2 24,250/62,312 loss 0.1043 nce 0.1040 mse 0.00035 acc 1.000 lr 4.21e-04 L32 149m\n", " e2 24,300/62,312 loss 0.1477 nce 0.1472 mse 0.00048 acc 0.997 lr 4.21e-04 L16 150m\n", " e2 24,350/62,312 loss 0.1973 nce 0.1968 mse 0.00052 acc 1.000 lr 4.20e-04 L94 150m\n", " e2 24,400/62,312 loss 0.2751 nce 0.2745 mse 0.00063 acc 0.976 lr 4.19e-04 L11 150m\n", " e2 24,450/62,312 loss 0.2088 nce 0.2083 mse 0.00052 acc 1.000 lr 4.19e-04 L99 151m\n", " e2 24,500/62,312 loss 0.2427 nce 0.2421 mse 0.00056 acc 1.000 lr 4.18e-04 L116 151m\n", " e2 24,550/62,312 loss 0.1056 nce 0.1052 mse 0.00041 acc 1.000 lr 4.17e-04 L20 151m\n", " e2 24,600/62,312 loss 0.1355 nce 0.1351 mse 0.00043 acc 1.000 lr 4.16e-04 L70 152m\n", " e2 24,650/62,312 loss 0.1945 nce 0.1940 mse 0.00053 acc 0.995 lr 4.16e-04 L14 152m\n", " e2 24,700/62,312 loss 0.0963 nce 0.0960 mse 0.00034 acc 1.000 lr 4.15e-04 L37 152m\n", " e2 24,750/62,312 loss 0.0904 nce 0.0901 mse 0.00034 acc 1.000 lr 4.14e-04 L32 152m\n", " e2 24,800/62,312 loss 0.1031 nce 0.1027 mse 0.00035 acc 1.000 lr 4.13e-04 L32 153m\n", " e2 24,850/62,312 loss 0.1038 nce 0.1034 mse 0.00037 acc 1.000 lr 4.13e-04 L24 153m\n", " e2 24,900/62,312 loss 0.2171 nce 0.2165 mse 0.00057 acc 0.992 lr 4.12e-04 L12 153m\n", " e2 24,950/62,312 loss 0.0891 nce 0.0888 mse 0.00034 acc 1.000 lr 4.11e-04 L43 154m\n", " e2 25,000/62,312 loss 0.2012 nce 0.2006 mse 0.00054 acc 0.998 lr 4.11e-04 L14 154m\n", " VAL r1 0.9960 cos 0.8302 self_cos +0.0125 erank 100.9 cv 0.0750 | frame r1 0.9985\n", " e2 25,050/62,312 loss 0.1462 nce 0.1457 mse 0.00046 acc 1.000 lr 4.10e-04 L76 154m\n", " e2 25,100/62,312 loss 0.2064 nce 0.2059 mse 0.00052 acc 0.997 lr 4.09e-04 L14 155m\n", " e2 25,150/62,312 loss 0.0994 nce 0.0991 mse 0.00036 acc 1.000 lr 4.08e-04 L36 155m\n", " e2 25,200/62,312 loss 0.1914 nce 0.1909 mse 0.00051 acc 1.000 lr 4.08e-04 L100 155m\n", " e2 25,250/62,312 loss 0.0989 nce 0.0986 mse 0.00036 acc 1.000 lr 4.07e-04 L27 155m\n", " e2 25,300/62,312 loss 0.0994 nce 0.0990 mse 0.00034 acc 1.000 lr 4.06e-04 L32 156m\n", " e2 25,350/62,312 loss 0.4640 nce 0.4633 mse 0.00067 acc 0.932 lr 4.06e-04 L10 156m\n", " e2 25,400/62,312 loss 0.1137 nce 0.1133 mse 0.00038 acc 1.000 lr 4.05e-04 L49 156m\n", " e2 25,450/62,312 loss 0.1135 nce 0.1131 mse 0.00044 acc 0.999 lr 4.04e-04 L19 157m\n", " e2 25,500/62,312 loss 0.0962 nce 0.0959 mse 0.00035 acc 0.999 lr 4.03e-04 L24 157m\n", " e2 25,550/62,312 loss 0.2002 nce 0.1996 mse 0.00057 acc 0.993 lr 4.03e-04 L12 157m\n", " e2 25,600/62,312 loss 0.1177 nce 0.1174 mse 0.00037 acc 1.000 lr 4.02e-04 L49 158m\n", " e2 25,650/62,312 loss 0.1954 nce 0.1949 mse 0.00051 acc 1.000 lr 4.01e-04 L100 158m\n", " e2 25,700/62,312 loss 0.2324 nce 0.2318 mse 0.00058 acc 0.986 lr 4.00e-04 L12 158m\n", " e2 25,750/62,312 loss 0.0894 nce 0.0891 mse 0.00033 acc 1.000 lr 4.00e-04 L30 159m\n", " e2 25,800/62,312 loss 0.0956 nce 0.0952 mse 0.00037 acc 1.000 lr 3.99e-04 L23 159m\n", " e2 25,850/62,312 loss 0.1031 nce 0.1027 mse 0.00040 acc 0.999 lr 3.98e-04 L21 159m\n", " e2 25,900/62,312 loss 0.1018 nce 0.1014 mse 0.00039 acc 0.999 lr 3.97e-04 L22 159m\n", " e2 25,950/62,312 loss 0.0961 nce 0.0957 mse 0.00035 acc 1.000 lr 3.97e-04 L29 160m\n", " e2 26,000/62,312 loss 0.5098 nce 0.5089 mse 0.00080 acc 0.972 lr 3.96e-04 L8 160m\n", " VAL r1 0.9960 cos 0.8313 self_cos +0.0100 erank 100.6 cv 0.0732 | frame r1 0.9980\n", " e2 26,050/62,312 loss 0.0946 nce 0.0943 mse 0.00035 acc 1.000 lr 3.95e-04 L24 160m\n", " e2 26,100/62,312 loss 0.0945 nce 0.0941 mse 0.00033 acc 1.000 lr 3.95e-04 L37 161m\n", " e2 26,150/62,312 loss 0.2207 nce 0.2201 mse 0.00053 acc 1.000 lr 3.94e-04 L105 161m\n", " e2 26,200/62,312 loss 0.1894 nce 0.1889 mse 0.00053 acc 0.994 lr 3.93e-04 L14 161m\n", " e2 26,250/62,312 loss 0.2445 nce 0.2439 mse 0.00056 acc 1.000 lr 3.92e-04 L116 162m\n", " e2 26,300/62,312 loss 0.1607 nce 0.1602 mse 0.00046 acc 1.000 lr 3.92e-04 L88 162m\n", " e2 26,350/62,312 loss 0.1018 nce 0.1014 mse 0.00042 acc 1.000 lr 3.91e-04 L19 162m\n", " e2 26,400/62,312 loss 0.1315 nce 0.1311 mse 0.00041 acc 1.000 lr 3.90e-04 L59 162m\n", " e2 26,450/62,312 loss 0.4249 nce 0.4242 mse 0.00072 acc 1.000 lr 3.89e-04 L256 163m\n", " e2 26,500/62,312 loss 0.1214 nce 0.1210 mse 0.00040 acc 1.000 lr 3.89e-04 L52 163m\n", " e2 26,550/62,312 loss 0.0965 nce 0.0962 mse 0.00034 acc 1.000 lr 3.88e-04 L27 163m\n", " e2 26,600/62,312 loss 0.0920 nce 0.0917 mse 0.00034 acc 1.000 lr 3.87e-04 L28 164m\n", " e2 26,650/62,312 loss 0.1333 nce 0.1328 mse 0.00042 acc 0.999 lr 3.86e-04 L64 164m\n", " e2 26,700/62,312 loss 0.2468 nce 0.2462 mse 0.00056 acc 1.000 lr 3.86e-04 L116 164m\n", " e2 26,750/62,312 loss 0.0949 nce 0.0946 mse 0.00035 acc 1.000 lr 3.85e-04 L26 164m\n", " e2 26,800/62,312 loss 0.0846 nce 0.0842 mse 0.00034 acc 1.000 lr 3.84e-04 L42 165m\n", " e2 26,850/62,312 loss 0.1011 nce 0.1007 mse 0.00036 acc 1.000 lr 3.83e-04 L25 165m\n", " e2 26,900/62,312 loss 0.0948 nce 0.0944 mse 0.00035 acc 1.000 lr 3.83e-04 L28 165m\n", " e2 26,950/62,312 loss 0.0923 nce 0.0920 mse 0.00036 acc 1.000 lr 3.82e-04 L24 166m\n", " e2 27,000/62,312 loss 0.3078 nce 0.3072 mse 0.00061 acc 1.000 lr 3.81e-04 L147 166m\n", " VAL r1 0.9980 cos 0.8375 self_cos +0.0050 erank 99.8 cv 0.0802 | frame r1 0.9985\n", " e2 27,050/62,312 loss 0.1419 nce 0.1414 mse 0.00045 acc 1.000 lr 3.80e-04 L82 166m\n", " e2 27,100/62,312 loss 0.2755 nce 0.2749 mse 0.00060 acc 1.000 lr 3.80e-04 L137 167m\n", " e2 27,150/62,312 loss 0.0943 nce 0.0940 mse 0.00034 acc 1.000 lr 3.79e-04 L31 167m\n", " e2 27,200/62,312 loss 0.1882 nce 0.1877 mse 0.00050 acc 1.000 lr 3.78e-04 L93 167m\n", " e2 27,250/62,312 loss 0.0880 nce 0.0877 mse 0.00034 acc 1.000 lr 3.77e-04 L26 167m\n", " e2 27,300/62,312 loss 0.2702 nce 0.2696 mse 0.00059 acc 1.000 lr 3.77e-04 L128 168m\n", " e2 27,350/62,312 loss 0.3532 nce 0.3525 mse 0.00067 acc 1.000 lr 3.76e-04 L170 168m\n", " e2 27,400/62,312 loss 0.0945 nce 0.0941 mse 0.00034 acc 1.000 lr 3.75e-04 L35 168m\n", " e2 27,450/62,312 loss 0.0973 nce 0.0969 mse 0.00036 acc 0.997 lr 3.74e-04 L24 169m\n", " e2 27,500/62,312 loss 0.0949 nce 0.0946 mse 0.00034 acc 1.000 lr 3.74e-04 L27 169m\n", " e2 27,550/62,312 loss 0.0972 nce 0.0969 mse 0.00035 acc 0.999 lr 3.73e-04 L45 169m\n", " e2 27,600/62,312 loss 0.0954 nce 0.0951 mse 0.00033 acc 1.000 lr 3.72e-04 L34 169m\n", " e2 27,650/62,312 loss 0.1003 nce 0.0999 mse 0.00038 acc 0.998 lr 3.71e-04 L22 170m\n", " e2 27,700/62,312 loss 0.2501 nce 0.2495 mse 0.00058 acc 1.000 lr 3.71e-04 L116 170m\n", " e2 27,750/62,312 loss 0.0947 nce 0.0944 mse 0.00033 acc 1.000 lr 3.70e-04 L32 170m\n", " e2 27,800/62,312 loss 0.2120 nce 0.2115 mse 0.00053 acc 1.000 lr 3.69e-04 L105 171m\n", " e2 27,850/62,312 loss 0.4051 nce 0.4044 mse 0.00065 acc 0.943 lr 3.68e-04 L10 171m\n", " e2 27,900/62,312 loss 0.1254 nce 0.1249 mse 0.00046 acc 0.999 lr 3.68e-04 L17 171m\n", " e2 27,950/62,312 loss 0.0901 nce 0.0897 mse 0.00034 acc 1.000 lr 3.67e-04 L26 172m\n", " e2 28,000/62,312 loss 0.1649 nce 0.1644 mse 0.00047 acc 1.000 lr 3.66e-04 L88 172m\n", " VAL r1 0.9975 cos 0.8353 self_cos +0.0040 erank 101.6 cv 0.0831 | frame r1 0.9985\n", " e2 28,050/62,312 loss 0.0973 nce 0.0969 mse 0.00035 acc 1.000 lr 3.65e-04 L26 172m\n", " e2 28,100/62,312 loss 0.2166 nce 0.2161 mse 0.00052 acc 1.000 lr 3.65e-04 L105 173m\n", " e2 28,150/62,312 loss 0.0900 nce 0.0897 mse 0.00033 acc 1.000 lr 3.64e-04 L30 173m\n", " e2 28,200/62,312 loss 0.1040 nce 0.1037 mse 0.00033 acc 0.999 lr 3.63e-04 L36 173m\n", " e2 28,250/62,312 loss 0.0998 nce 0.0995 mse 0.00034 acc 1.000 lr 3.62e-04 L31 174m\n", " e2 28,300/62,312 loss 0.0929 nce 0.0925 mse 0.00033 acc 1.000 lr 3.62e-04 L34 174m\n", " e2 28,350/62,312 loss 0.0843 nce 0.0840 mse 0.00033 acc 1.000 lr 3.61e-04 L41 174m\n", " e2 28,400/62,312 loss 0.3274 nce 0.3268 mse 0.00064 acc 1.000 lr 3.60e-04 L170 174m\n", " e2 28,450/62,312 loss 0.2080 nce 0.2074 mse 0.00058 acc 0.987 lr 3.59e-04 L12 175m\n", " e2 28,500/62,312 loss 0.0916 nce 0.0913 mse 0.00033 acc 1.000 lr 3.58e-04 L29 175m\n", " e2 28,550/62,312 loss 0.0922 nce 0.0919 mse 0.00034 acc 0.999 lr 3.58e-04 L25 175m\n", " e2 28,600/62,312 loss 0.1220 nce 0.1215 mse 0.00045 acc 0.999 lr 3.57e-04 L17 176m\n", " e2 28,650/62,312 loss 0.0899 nce 0.0896 mse 0.00033 acc 1.000 lr 3.56e-04 L30 176m\n", " e2 28,700/62,312 loss 0.0872 nce 0.0869 mse 0.00033 acc 1.000 lr 3.55e-04 L38 176m\n", " e2 28,750/62,312 loss 0.2708 nce 0.2702 mse 0.00060 acc 0.970 lr 3.55e-04 L11 176m\n", " e2 28,800/62,312 loss 0.3206 nce 0.3200 mse 0.00065 acc 1.000 lr 3.54e-04 L168 177m\n", " e2 28,850/62,312 loss 0.0950 nce 0.0947 mse 0.00034 acc 1.000 lr 3.53e-04 L31 177m\n", " e2 28,900/62,312 loss 0.1888 nce 0.1883 mse 0.00052 acc 0.998 lr 3.52e-04 L14 177m\n", " e2 28,950/62,312 loss 0.2412 nce 0.2406 mse 0.00057 acc 1.000 lr 3.52e-04 L116 178m\n", " e2 29,000/62,312 loss 0.2970 nce 0.2964 mse 0.00062 acc 1.000 lr 3.51e-04 L147 178m\n", " VAL r1 0.9975 cos 0.8348 self_cos +0.0069 erank 101.4 cv 0.0712 | frame r1 0.9985\n", " e2 29,050/62,312 loss 0.1160 nce 0.1156 mse 0.00038 acc 0.999 lr 3.50e-04 L48 178m\n", " e2 29,100/62,312 loss 0.0979 nce 0.0976 mse 0.00034 acc 1.000 lr 3.49e-04 L32 179m\n", " e2 29,150/62,312 loss 0.1086 nce 0.1082 mse 0.00043 acc 0.999 lr 3.49e-04 L18 179m\n", " e2 29,200/62,312 loss 0.1335 nce 0.1331 mse 0.00042 acc 1.000 lr 3.48e-04 L63 179m\n", " e2 29,250/62,312 loss 0.0848 nce 0.0844 mse 0.00034 acc 1.000 lr 3.47e-04 L43 180m\n", " e2 29,300/62,312 loss 0.1729 nce 0.1724 mse 0.00049 acc 0.996 lr 3.46e-04 L15 180m\n", " e2 29,350/62,312 loss 0.2243 nce 0.2238 mse 0.00055 acc 1.000 lr 3.45e-04 L111 180m\n", " e2 29,400/62,312 loss 0.0931 nce 0.0928 mse 0.00034 acc 1.000 lr 3.45e-04 L29 180m\n", " e2 29,450/62,312 loss 0.2542 nce 0.2536 mse 0.00056 acc 1.000 lr 3.44e-04 L121 181m\n", " e2 29,500/62,312 loss 0.0938 nce 0.0935 mse 0.00034 acc 1.000 lr 3.43e-04 L28 181m\n", " e2 29,550/62,312 loss 0.0963 nce 0.0959 mse 0.00035 acc 1.000 lr 3.42e-04 L25 181m\n", " e2 29,600/62,312 loss 0.1127 nce 0.1123 mse 0.00044 acc 0.998 lr 3.42e-04 L18 182m\n", " e2 29,650/62,312 loss 0.0904 nce 0.0900 mse 0.00033 acc 1.000 lr 3.41e-04 L38 182m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 29675)\n", " e2 29,700/62,312 loss 0.1697 nce 0.1692 mse 0.00050 acc 0.993 lr 3.40e-04 L15 183m\n", " e2 29,750/62,312 loss 0.1349 nce 0.1344 mse 0.00048 acc 0.999 lr 3.39e-04 L16 183m\n", " e2 29,800/62,312 loss 0.5120 nce 0.5112 mse 0.00079 acc 0.967 lr 3.39e-04 L8 183m\n", " e2 29,850/62,312 loss 0.0929 nce 0.0925 mse 0.00034 acc 1.000 lr 3.38e-04 L35 184m\n", " e2 29,900/62,312 loss 0.0965 nce 0.0961 mse 0.00040 acc 1.000 lr 3.37e-04 L20 184m\n", " e2 29,950/62,312 loss 0.2791 nce 0.2785 mse 0.00059 acc 1.000 lr 3.36e-04 L136 184m\n", " e2 30,000/62,312 loss 0.2503 nce 0.2497 mse 0.00058 acc 1.000 lr 3.35e-04 L128 184m\n", " VAL r1 0.9970 cos 0.8365 self_cos +0.0037 erank 101.7 cv 0.0729 | frame r1 0.9985\n", " e2 30,050/62,312 loss 0.1121 nce 0.1117 mse 0.00043 acc 0.999 lr 3.35e-04 L18 185m\n", " e2 30,100/62,312 loss 0.0931 nce 0.0927 mse 0.00038 acc 1.000 lr 3.34e-04 L21 185m\n", " e2 30,150/62,312 loss 0.3899 nce 0.3893 mse 0.00064 acc 0.944 lr 3.33e-04 L10 185m\n", " e2 30,200/62,312 loss 0.0907 nce 0.0904 mse 0.00035 acc 1.000 lr 3.32e-04 L45 186m\n", " e2 30,250/62,312 loss 0.0992 nce 0.0988 mse 0.00034 acc 1.000 lr 3.32e-04 L37 186m\n", " e2 30,300/62,312 loss 0.2963 nce 0.2957 mse 0.00060 acc 0.962 lr 3.31e-04 L11 186m\n", " e2 30,350/62,312 loss 0.2755 nce 0.2749 mse 0.00060 acc 1.000 lr 3.30e-04 L129 187m\n", " e2 30,400/62,312 loss 0.0892 nce 0.0889 mse 0.00033 acc 1.000 lr 3.29e-04 L29 187m\n", " e2 30,450/62,312 loss 0.1786 nce 0.1781 mse 0.00050 acc 1.000 lr 3.28e-04 L94 187m\n", " e2 30,500/62,312 loss 0.0960 nce 0.0957 mse 0.00033 acc 1.000 lr 3.28e-04 L33 188m\n", " e2 30,550/62,312 loss 0.1337 nce 0.1333 mse 0.00039 acc 1.000 lr 3.27e-04 L54 188m\n", " e2 30,600/62,312 loss 0.2851 nce 0.2845 mse 0.00061 acc 1.000 lr 3.26e-04 L147 188m\n", " e2 30,650/62,312 loss 0.1096 nce 0.1092 mse 0.00042 acc 0.998 lr 3.25e-04 L18 189m\n", " e2 30,700/62,312 loss 0.0956 nce 0.0952 mse 0.00039 acc 0.999 lr 3.25e-04 L20 189m\n", " e2 30,750/62,312 loss 0.1007 nce 0.1003 mse 0.00035 acc 1.000 lr 3.24e-04 L34 189m\n", " e2 30,800/62,312 loss 0.4889 nce 0.4881 mse 0.00078 acc 0.968 lr 3.23e-04 L8 190m\n", " e2 30,850/62,312 loss 0.0895 nce 0.0891 mse 0.00033 acc 1.000 lr 3.22e-04 L33 190m\n", " e2 30,900/62,312 loss 0.0871 nce 0.0868 mse 0.00033 acc 1.000 lr 3.21e-04 L34 190m\n", " e2 30,950/62,312 loss 0.1354 nce 0.1349 mse 0.00044 acc 1.000 lr 3.21e-04 L83 190m\n", " e2 31,000/62,312 loss 0.2165 nce 0.2160 mse 0.00054 acc 1.000 lr 3.20e-04 L115 191m\n", " VAL r1 0.9980 cos 0.8382 self_cos +0.0038 erank 101.0 cv 0.0755 | frame r1 0.9985\n", " e2 31,050/62,312 loss 0.5106 nce 0.5098 mse 0.00078 acc 0.966 lr 3.19e-04 L8 191m\n", " e2 31,100/62,312 loss 0.0892 nce 0.0889 mse 0.00033 acc 1.000 lr 3.18e-04 L30 191m\n", " e2 31,150/62,312 loss 0.0900 nce 0.0897 mse 0.00033 acc 1.000 lr 3.18e-04 L29 192m\n", " e3 31,200/62,312 loss 0.2051 nce 0.2046 mse 0.00052 acc 1.000 lr 3.17e-04 L105 192m\n", " e3 31,250/62,312 loss 0.1235 nce 0.1231 mse 0.00042 acc 1.000 lr 3.16e-04 L70 192m\n", " e3 31,300/62,312 loss 0.1320 nce 0.1315 mse 0.00043 acc 1.000 lr 3.15e-04 L70 193m\n", " e3 31,350/62,312 loss 0.0917 nce 0.0913 mse 0.00034 acc 1.000 lr 3.14e-04 L45 193m\n", " e3 31,400/62,312 loss 0.0940 nce 0.0937 mse 0.00033 acc 1.000 lr 3.14e-04 L29 193m\n", " e3 31,450/62,312 loss 0.0902 nce 0.0899 mse 0.00035 acc 1.000 lr 3.13e-04 L45 193m\n", " e3 31,500/62,312 loss 0.0873 nce 0.0869 mse 0.00032 acc 1.000 lr 3.12e-04 L37 194m\n", " e3 31,550/62,312 loss 0.1993 nce 0.1988 mse 0.00052 acc 0.999 lr 3.11e-04 L105 194m\n", " e3 31,600/62,312 loss 0.2785 nce 0.2779 mse 0.00061 acc 1.000 lr 3.11e-04 L136 194m\n", " e3 31,650/62,312 loss 0.0890 nce 0.0886 mse 0.00032 acc 1.000 lr 3.10e-04 L35 195m\n", " e3 31,700/62,312 loss 0.1278 nce 0.1274 mse 0.00042 acc 0.999 lr 3.09e-04 L64 195m\n", " e3 31,750/62,312 loss 0.0931 nce 0.0928 mse 0.00034 acc 1.000 lr 3.08e-04 L25 195m\n", " e3 31,800/62,312 loss 0.1277 nce 0.1273 mse 0.00041 acc 1.000 lr 3.07e-04 L53 195m\n", " e3 31,850/62,312 loss 0.0927 nce 0.0924 mse 0.00033 acc 1.000 lr 3.07e-04 L33 196m\n", " e3 31,900/62,312 loss 0.0925 nce 0.0921 mse 0.00036 acc 1.000 lr 3.06e-04 L23 196m\n", " e3 31,950/62,312 loss 0.1215 nce 0.1211 mse 0.00039 acc 1.000 lr 3.05e-04 L54 196m\n", " e3 32,000/62,312 loss 0.0952 nce 0.0949 mse 0.00035 acc 0.999 lr 3.04e-04 L24 196m\n", " VAL r1 0.9975 cos 0.8347 self_cos +0.0041 erank 100.6 cv 0.0776 | frame r1 0.9985\n", " e3 32,050/62,312 loss 0.4754 nce 0.4747 mse 0.00068 acc 0.929 lr 3.04e-04 L10 197m\n", " e3 32,100/62,312 loss 0.0914 nce 0.0910 mse 0.00033 acc 1.000 lr 3.03e-04 L28 197m\n", " e3 32,150/62,312 loss 0.3990 nce 0.3983 mse 0.00070 acc 1.000 lr 3.02e-04 L256 197m\n", " e3 32,200/62,312 loss 0.1415 nce 0.1411 mse 0.00044 acc 1.000 lr 3.01e-04 L83 198m\n", " e3 32,250/62,312 loss 0.1937 nce 0.1932 mse 0.00051 acc 1.000 lr 3.00e-04 L100 198m\n", " e3 32,300/62,312 loss 0.0853 nce 0.0850 mse 0.00033 acc 1.000 lr 3.00e-04 L39 198m\n", " e3 32,350/62,312 loss 0.1843 nce 0.1838 mse 0.00049 acc 1.000 lr 2.99e-04 L94 199m\n", " e3 32,400/62,312 loss 0.2137 nce 0.2131 mse 0.00054 acc 1.000 lr 2.98e-04 L105 199m\n", " e3 32,450/62,312 loss 0.0966 nce 0.0963 mse 0.00033 acc 1.000 lr 2.97e-04 L34 199m\n", " e3 32,500/62,312 loss 0.0957 nce 0.0953 mse 0.00038 acc 0.998 lr 2.97e-04 L21 200m\n", " e3 32,550/62,312 loss 0.2118 nce 0.2112 mse 0.00054 acc 0.996 lr 2.96e-04 L13 200m\n", " e3 32,600/62,312 loss 0.0965 nce 0.0962 mse 0.00034 acc 1.000 lr 2.95e-04 L45 200m\n", " e3 32,650/62,312 loss 0.0860 nce 0.0857 mse 0.00032 acc 1.000 lr 2.94e-04 L37 201m\n", " e3 32,700/62,312 loss 0.4035 nce 0.4028 mse 0.00069 acc 0.999 lr 2.94e-04 L256 201m\n", " e3 32,750/62,312 loss 0.0891 nce 0.0887 mse 0.00032 acc 1.000 lr 2.93e-04 L37 201m\n", " e3 32,800/62,312 loss 0.1525 nce 0.1520 mse 0.00045 acc 1.000 lr 2.92e-04 L88 202m\n", " e3 32,850/62,312 loss 0.0870 nce 0.0866 mse 0.00034 acc 0.999 lr 2.91e-04 L28 202m\n", " e3 32,900/62,312 loss 0.0959 nce 0.0955 mse 0.00038 acc 0.997 lr 2.90e-04 L21 202m\n", " e3 32,950/62,312 loss 0.0970 nce 0.0966 mse 0.00033 acc 1.000 lr 2.90e-04 L34 202m\n", " e3 33,000/62,312 loss 0.2224 nce 0.2218 mse 0.00054 acc 1.000 lr 2.89e-04 L105 203m\n", " VAL r1 0.9975 cos 0.8388 self_cos +0.0045 erank 100.2 cv 0.0713 | frame r1 0.9985\n", " e3 33,050/62,312 loss 0.0853 nce 0.0849 mse 0.00034 acc 1.000 lr 2.88e-04 L26 203m\n", " e3 33,100/62,312 loss 0.0877 nce 0.0874 mse 0.00032 acc 1.000 lr 2.87e-04 L29 203m\n", " e3 33,150/62,312 loss 0.0906 nce 0.0903 mse 0.00038 acc 1.000 lr 2.87e-04 L21 204m\n", " e3 33,200/62,312 loss 0.1929 nce 0.1924 mse 0.00052 acc 0.998 lr 2.86e-04 L14 204m\n", " e3 33,250/62,312 loss 0.0817 nce 0.0813 mse 0.00032 acc 1.000 lr 2.85e-04 L40 204m\n", " e3 33,300/62,312 loss 0.1399 nce 0.1395 mse 0.00045 acc 1.000 lr 2.84e-04 L81 205m\n", " e3 33,350/62,312 loss 0.0947 nce 0.0944 mse 0.00032 acc 1.000 lr 2.83e-04 L36 205m\n", " e3 33,400/62,312 loss 0.1207 nce 0.1203 mse 0.00041 acc 1.000 lr 2.83e-04 L71 205m\n", " e3 33,450/62,312 loss 0.0908 nce 0.0905 mse 0.00034 acc 1.000 lr 2.82e-04 L26 205m\n", " e3 33,500/62,312 loss 0.1107 nce 0.1103 mse 0.00037 acc 1.000 lr 2.81e-04 L48 206m\n", " e3 33,550/62,312 loss 0.2514 nce 0.2508 mse 0.00058 acc 1.000 lr 2.80e-04 L128 206m\n", " e3 33,600/62,312 loss 0.0941 nce 0.0938 mse 0.00034 acc 0.999 lr 2.80e-04 L27 206m\n", " e3 33,650/62,312 loss 0.1147 nce 0.1143 mse 0.00037 acc 0.999 lr 2.79e-04 L48 207m\n", " e3 33,700/62,312 loss 0.1003 nce 0.0999 mse 0.00034 acc 1.000 lr 2.78e-04 L34 207m\n", " e3 33,750/62,312 loss 0.0869 nce 0.0866 mse 0.00033 acc 1.000 lr 2.77e-04 L31 207m\n", " e3 33,800/62,312 loss 0.0930 nce 0.0927 mse 0.00033 acc 1.000 lr 2.76e-04 L35 207m\n", " e3 33,850/62,312 loss 0.1530 nce 0.1526 mse 0.00045 acc 1.000 lr 2.76e-04 L88 208m\n", " e3 33,900/62,312 loss 0.0900 nce 0.0897 mse 0.00033 acc 1.000 lr 2.75e-04 L34 208m\n", " e3 33,950/62,312 loss 0.0917 nce 0.0913 mse 0.00035 acc 1.000 lr 2.74e-04 L24 208m\n", " e3 34,000/62,312 loss 0.0886 nce 0.0882 mse 0.00032 acc 1.000 lr 2.73e-04 L31 209m\n", " VAL r1 0.9980 cos 0.8389 self_cos +0.0031 erank 101.8 cv 0.0830 | frame r1 0.9985\n", " e3 34,050/62,312 loss 0.1927 nce 0.1922 mse 0.00051 acc 0.996 lr 2.73e-04 L14 209m\n", " e3 34,100/62,312 loss 0.0926 nce 0.0922 mse 0.00034 acc 0.999 lr 2.72e-04 L25 209m\n", " e3 34,150/62,312 loss 0.0957 nce 0.0954 mse 0.00033 acc 1.000 lr 2.71e-04 L32 209m\n", " e3 34,200/62,312 loss 0.1304 nce 0.1300 mse 0.00043 acc 0.999 lr 2.70e-04 L76 210m\n", " e3 34,250/62,312 loss 0.2759 nce 0.2753 mse 0.00061 acc 1.000 lr 2.69e-04 L147 210m\n", " e3 34,300/62,312 loss 0.0875 nce 0.0872 mse 0.00033 acc 1.000 lr 2.69e-04 L30 210m\n", " e3 34,350/62,312 loss 0.1005 nce 0.1001 mse 0.00041 acc 0.999 lr 2.68e-04 L19 211m\n", " e3 34,400/62,312 loss 0.0903 nce 0.0900 mse 0.00033 acc 1.000 lr 2.67e-04 L28 211m\n", " e3 34,450/62,312 loss 0.0916 nce 0.0912 mse 0.00033 acc 1.000 lr 2.66e-04 L38 211m\n", " e3 34,500/62,312 loss 0.2080 nce 0.2075 mse 0.00054 acc 0.997 lr 2.66e-04 L13 212m\n", " e3 34,550/62,312 loss 0.1584 nce 0.1579 mse 0.00047 acc 1.000 lr 2.65e-04 L89 212m\n", " e3 34,600/62,312 loss 0.0799 nce 0.0796 mse 0.00032 acc 1.000 lr 2.64e-04 L37 212m\n", " e3 34,650/62,312 loss 0.0929 nce 0.0926 mse 0.00033 acc 1.000 lr 2.63e-04 L32 212m\n", " e3 34,700/62,312 loss 0.1297 nce 0.1292 mse 0.00047 acc 1.000 lr 2.62e-04 L16 213m\n", " e3 34,750/62,312 loss 0.0844 nce 0.0841 mse 0.00032 acc 1.000 lr 2.62e-04 L38 213m\n", " e3 34,800/62,312 loss 0.1109 nce 0.1105 mse 0.00043 acc 0.999 lr 2.61e-04 L18 213m\n", " e3 34,850/62,312 loss 0.4080 nce 0.4074 mse 0.00065 acc 0.943 lr 2.60e-04 L10 214m\n", " e3 34,900/62,312 loss 0.0893 nce 0.0890 mse 0.00033 acc 1.000 lr 2.59e-04 L29 214m\n", " e3 34,950/62,312 loss 0.0957 nce 0.0954 mse 0.00033 acc 1.000 lr 2.59e-04 L35 214m\n", " e3 35,000/62,312 loss 0.0935 nce 0.0931 mse 0.00033 acc 1.000 lr 2.58e-04 L27 215m\n", " VAL r1 0.9975 cos 0.8395 self_cos +0.0047 erank 102.6 cv 0.0756 | frame r1 0.9985\n", " e3 35,050/62,312 loss 0.0866 nce 0.0863 mse 0.00032 acc 1.000 lr 2.57e-04 L32 215m\n", " e3 35,100/62,312 loss 0.4544 nce 0.4537 mse 0.00078 acc 0.976 lr 2.56e-04 L8 215m\n", " e3 35,150/62,312 loss 0.1233 nce 0.1228 mse 0.00042 acc 1.000 lr 2.56e-04 L70 215m\n", " e3 35,200/62,312 loss 0.0870 nce 0.0867 mse 0.00033 acc 1.000 lr 2.55e-04 L30 216m\n", " e3 35,250/62,312 loss 0.0969 nce 0.0966 mse 0.00033 acc 1.000 lr 2.54e-04 L35 216m\n", " e3 35,300/62,312 loss 0.0915 nce 0.0912 mse 0.00032 acc 1.000 lr 2.53e-04 L34 216m\n", " e3 35,350/62,312 loss 0.0985 nce 0.0981 mse 0.00035 acc 0.999 lr 2.52e-04 L24 217m\n", " e3 35,400/62,312 loss 0.0923 nce 0.0919 mse 0.00034 acc 1.000 lr 2.52e-04 L24 217m\n", " e3 35,450/62,312 loss 0.0925 nce 0.0921 mse 0.00033 acc 1.000 lr 2.51e-04 L29 217m\n", " e3 35,500/62,312 loss 0.0841 nce 0.0837 mse 0.00033 acc 1.000 lr 2.50e-04 L28 217m\n", " e3 35,550/62,312 loss 0.0892 nce 0.0889 mse 0.00034 acc 1.000 lr 2.49e-04 L27 218m\n", " e3 35,600/62,312 loss 0.1308 nce 0.1304 mse 0.00043 acc 1.000 lr 2.49e-04 L76 218m\n", " e3 35,650/62,312 loss 0.2507 nce 0.2501 mse 0.00058 acc 1.000 lr 2.48e-04 L128 218m\n", " e3 35,700/62,312 loss 0.1093 nce 0.1088 mse 0.00043 acc 0.999 lr 2.47e-04 L18 219m\n", " e3 35,750/62,312 loss 0.0912 nce 0.0908 mse 0.00034 acc 0.999 lr 2.46e-04 L45 219m\n", " e3 35,800/62,312 loss 0.0898 nce 0.0895 mse 0.00033 acc 1.000 lr 2.46e-04 L37 219m\n", " e3 35,850/62,312 loss 0.0919 nce 0.0915 mse 0.00035 acc 0.998 lr 2.45e-04 L24 220m\n", " e3 35,900/62,312 loss 0.1206 nce 0.1202 mse 0.00042 acc 1.000 lr 2.44e-04 L70 220m\n", " e3 35,950/62,312 loss 0.3143 nce 0.3137 mse 0.00064 acc 1.000 lr 2.43e-04 L171 220m\n", " e3 36,000/62,312 loss 0.2088 nce 0.2082 mse 0.00052 acc 1.000 lr 2.43e-04 L105 221m\n", " VAL r1 0.9980 cos 0.8388 self_cos +0.0030 erank 102.7 cv 0.0648 | frame r1 0.9985\n", " e3 36,050/62,312 loss 0.6494 nce 0.6487 mse 0.00072 acc 0.900 lr 2.42e-04 L9 221m\n", " e3 36,100/62,312 loss 0.2077 nce 0.2071 mse 0.00054 acc 1.000 lr 2.41e-04 L110 221m\n", " e3 36,150/62,312 loss 0.0949 nce 0.0945 mse 0.00039 acc 1.000 lr 2.40e-04 L20 221m\n", " e3 36,200/62,312 loss 0.1567 nce 0.1562 mse 0.00048 acc 0.994 lr 2.39e-04 L15 222m\n", " e3 36,250/62,312 loss 0.0849 nce 0.0845 mse 0.00032 acc 1.000 lr 2.39e-04 L31 222m\n", " e3 36,300/62,312 loss 0.0900 nce 0.0896 mse 0.00035 acc 0.999 lr 2.38e-04 L23 222m\n", " e3 36,350/62,312 loss 0.0895 nce 0.0891 mse 0.00033 acc 1.000 lr 2.37e-04 L30 223m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 36351)\n", " e3 36,400/62,312 loss 0.0860 nce 0.0857 mse 0.00033 acc 1.000 lr 2.36e-04 L30 223m\n", " e3 36,450/62,312 loss 0.1622 nce 0.1617 mse 0.00046 acc 0.999 lr 2.36e-04 L88 224m\n", " e3 36,500/62,312 loss 0.2900 nce 0.2894 mse 0.00061 acc 1.000 lr 2.35e-04 L148 224m\n", " e3 36,550/62,312 loss 0.0824 nce 0.0820 mse 0.00032 acc 0.999 lr 2.34e-04 L41 224m\n", " e3 36,600/62,312 loss 0.0866 nce 0.0863 mse 0.00032 acc 1.000 lr 2.33e-04 L33 224m\n", " e3 36,650/62,312 loss 0.2338 nce 0.2333 mse 0.00055 acc 1.000 lr 2.33e-04 L116 225m\n", " e3 36,700/62,312 loss 0.0865 nce 0.0862 mse 0.00034 acc 1.000 lr 2.32e-04 L26 225m\n", " e3 36,750/62,312 loss 0.1051 nce 0.1047 mse 0.00037 acc 1.000 lr 2.31e-04 L49 225m\n", " e3 36,800/62,312 loss 0.0944 nce 0.0940 mse 0.00035 acc 1.000 lr 2.30e-04 L24 226m\n", " e3 36,850/62,312 loss 0.1236 nce 0.1232 mse 0.00046 acc 0.998 lr 2.30e-04 L16 226m\n", " e3 36,900/62,312 loss 0.0887 nce 0.0884 mse 0.00033 acc 0.999 lr 2.29e-04 L41 226m\n", " e3 36,950/62,312 loss 0.1636 nce 0.1631 mse 0.00046 acc 1.000 lr 2.28e-04 L88 227m\n", " e3 37,000/62,312 loss 0.0920 nce 0.0916 mse 0.00034 acc 1.000 lr 2.27e-04 L26 227m\n", " VAL r1 0.9985 cos 0.8375 self_cos +0.0054 erank 102.0 cv 0.0727 | frame r1 0.9985\n", " e3 37,050/62,312 loss 0.0894 nce 0.0890 mse 0.00036 acc 0.999 lr 2.27e-04 L22 227m\n", " e3 37,100/62,312 loss 0.2196 nce 0.2190 mse 0.00055 acc 0.995 lr 2.26e-04 L13 228m\n", " e3 37,150/62,312 loss 0.2543 nce 0.2538 mse 0.00057 acc 1.000 lr 2.25e-04 L128 228m\n", " e3 37,200/62,312 loss 0.2140 nce 0.2135 mse 0.00052 acc 1.000 lr 2.24e-04 L105 228m\n", " e3 37,250/62,312 loss 0.1103 nce 0.1099 mse 0.00044 acc 0.999 lr 2.24e-04 L17 228m\n", " e3 37,300/62,312 loss 0.1843 nce 0.1838 mse 0.00051 acc 1.000 lr 2.23e-04 L99 229m\n", " e3 37,350/62,312 loss 0.0896 nce 0.0893 mse 0.00033 acc 1.000 lr 2.22e-04 L36 229m\n", " e3 37,400/62,312 loss 0.0846 nce 0.0843 mse 0.00032 acc 1.000 lr 2.21e-04 L36 229m\n", " e3 37,450/62,312 loss 0.2321 nce 0.2315 mse 0.00055 acc 1.000 lr 2.21e-04 L110 230m\n", " e3 37,500/62,312 loss 0.0903 nce 0.0899 mse 0.00035 acc 0.999 lr 2.20e-04 L24 230m\n", " e3 37,550/62,312 loss 0.0902 nce 0.0899 mse 0.00033 acc 1.000 lr 2.19e-04 L38 230m\n", " e3 37,600/62,312 loss 0.0769 nce 0.0766 mse 0.00032 acc 1.000 lr 2.18e-04 L41 230m\n", " e3 37,650/62,312 loss 0.2616 nce 0.2611 mse 0.00059 acc 1.000 lr 2.18e-04 L136 231m\n", " e3 37,700/62,312 loss 0.1517 nce 0.1513 mse 0.00046 acc 1.000 lr 2.17e-04 L89 231m\n", " e3 37,750/62,312 loss 0.0885 nce 0.0881 mse 0.00032 acc 1.000 lr 2.16e-04 L30 231m\n", " e3 37,800/62,312 loss 0.0809 nce 0.0806 mse 0.00032 acc 0.999 lr 2.15e-04 L41 232m\n", " e3 37,850/62,312 loss 0.0868 nce 0.0864 mse 0.00035 acc 0.999 lr 2.15e-04 L24 232m\n", " e3 37,900/62,312 loss 0.0872 nce 0.0868 mse 0.00033 acc 1.000 lr 2.14e-04 L38 232m\n", " e3 37,950/62,312 loss 0.0899 nce 0.0896 mse 0.00032 acc 1.000 lr 2.13e-04 L33 233m\n", " e3 38,000/62,312 loss 0.1225 nce 0.1221 mse 0.00039 acc 1.000 lr 2.12e-04 L53 233m\n", " VAL r1 0.9975 cos 0.8408 self_cos +0.0038 erank 102.0 cv 0.0769 | frame r1 0.9985\n", " e3 38,050/62,312 loss 0.1283 nce 0.1279 mse 0.00041 acc 1.000 lr 2.12e-04 L59 233m\n", " e3 38,100/62,312 loss 0.2137 nce 0.2131 mse 0.00053 acc 1.000 lr 2.11e-04 L104 234m\n", " e3 38,150/62,312 loss 0.0895 nce 0.0891 mse 0.00036 acc 1.000 lr 2.10e-04 L23 234m\n", " e3 38,200/62,312 loss 0.1214 nce 0.1210 mse 0.00039 acc 1.000 lr 2.09e-04 L53 234m\n", " e3 38,250/62,312 loss 0.1968 nce 0.1962 mse 0.00053 acc 0.996 lr 2.09e-04 L13 235m\n", " e3 38,300/62,312 loss 0.0976 nce 0.0973 mse 0.00034 acc 0.999 lr 2.08e-04 L45 235m\n", " e3 38,350/62,312 loss 0.2631 nce 0.2625 mse 0.00058 acc 1.000 lr 2.07e-04 L137 235m\n", " e3 38,400/62,312 loss 0.0966 nce 0.0963 mse 0.00034 acc 0.999 lr 2.06e-04 L26 236m\n", " e3 38,450/62,312 loss 0.0897 nce 0.0894 mse 0.00032 acc 1.000 lr 2.06e-04 L30 236m\n", " e3 38,500/62,312 loss 0.0919 nce 0.0916 mse 0.00035 acc 0.999 lr 2.05e-04 L24 236m\n", " e3 38,550/62,312 loss 0.0945 nce 0.0942 mse 0.00033 acc 1.000 lr 2.04e-04 L35 236m\n", " e3 38,600/62,312 loss 0.2435 nce 0.2430 mse 0.00056 acc 1.000 lr 2.04e-04 L121 237m\n", " e3 38,650/62,312 loss 0.0883 nce 0.0880 mse 0.00032 acc 1.000 lr 2.03e-04 L33 237m\n", " e3 38,700/62,312 loss 0.1280 nce 0.1275 mse 0.00042 acc 0.999 lr 2.02e-04 L76 237m\n", " e3 38,750/62,312 loss 0.2088 nce 0.2083 mse 0.00053 acc 1.000 lr 2.01e-04 L110 238m\n", " e3 38,800/62,312 loss 0.4906 nce 0.4898 mse 0.00078 acc 0.967 lr 2.01e-04 L8 238m\n", " e3 38,850/62,312 loss 0.0926 nce 0.0923 mse 0.00033 acc 0.999 lr 2.00e-04 L28 238m\n", " e3 38,900/62,312 loss 0.1236 nce 0.1232 mse 0.00039 acc 1.000 lr 1.99e-04 L59 238m\n", " e3 38,950/62,312 loss 0.1250 nce 0.1246 mse 0.00041 acc 1.000 lr 1.98e-04 L58 239m\n", " e3 39,000/62,312 loss 0.0860 nce 0.0857 mse 0.00032 acc 1.000 lr 1.98e-04 L30 239m\n", " VAL r1 0.9980 cos 0.8393 self_cos +0.0036 erank 102.9 cv 0.0663 | frame r1 0.9985\n", " e3 39,050/62,312 loss 0.1679 nce 0.1675 mse 0.00049 acc 0.997 lr 1.97e-04 L15 239m\n", " e3 39,100/62,312 loss 0.1358 nce 0.1354 mse 0.00044 acc 1.000 lr 1.96e-04 L82 240m\n", " e3 39,150/62,312 loss 0.1277 nce 0.1272 mse 0.00047 acc 0.999 lr 1.95e-04 L16 240m\n", " e3 39,200/62,312 loss 0.0906 nce 0.0902 mse 0.00035 acc 1.000 lr 1.95e-04 L23 240m\n", " e3 39,250/62,312 loss 0.0930 nce 0.0927 mse 0.00032 acc 1.000 lr 1.94e-04 L34 241m\n", " e3 39,300/62,312 loss 0.0936 nce 0.0933 mse 0.00034 acc 0.999 lr 1.93e-04 L25 241m\n", " e3 39,350/62,312 loss 0.0793 nce 0.0789 mse 0.00033 acc 1.000 lr 1.93e-04 L42 241m\n", " e3 39,400/62,312 loss 0.1887 nce 0.1882 mse 0.00050 acc 1.000 lr 1.92e-04 L100 242m\n", " e3 39,450/62,312 loss 0.1559 nce 0.1555 mse 0.00046 acc 1.000 lr 1.91e-04 L88 242m\n", " e3 39,500/62,312 loss 0.0849 nce 0.0846 mse 0.00033 acc 0.999 lr 1.90e-04 L39 242m\n", " e3 39,550/62,312 loss 0.0877 nce 0.0874 mse 0.00032 acc 1.000 lr 1.90e-04 L37 243m\n", " e3 39,600/62,312 loss 0.1210 nce 0.1206 mse 0.00041 acc 1.000 lr 1.89e-04 L70 243m\n", " e3 39,650/62,312 loss 0.0898 nce 0.0895 mse 0.00034 acc 1.000 lr 1.88e-04 L25 243m\n", " e3 39,700/62,312 loss 0.0855 nce 0.0852 mse 0.00032 acc 1.000 lr 1.88e-04 L31 243m\n", " e3 39,750/62,312 loss 0.1300 nce 0.1295 mse 0.00042 acc 0.999 lr 1.87e-04 L70 244m\n", " e3 39,800/62,312 loss 0.7293 nce 0.7286 mse 0.00074 acc 0.884 lr 1.86e-04 L9 244m\n", " e3 39,850/62,312 loss 0.0871 nce 0.0867 mse 0.00033 acc 1.000 lr 1.85e-04 L28 244m\n", " e3 39,900/62,312 loss 0.2506 nce 0.2500 mse 0.00059 acc 1.000 lr 1.85e-04 L129 245m\n", " e3 39,950/62,312 loss 0.0851 nce 0.0848 mse 0.00033 acc 0.998 lr 1.84e-04 L43 245m\n", " e3 40,000/62,312 loss 0.2845 nce 0.2839 mse 0.00061 acc 1.000 lr 1.83e-04 L148 245m\n", " VAL r1 0.9975 cos 0.8403 self_cos +0.0030 erank 102.3 cv 0.0721 | frame r1 0.9985\n", " e3 40,050/62,312 loss 0.2525 nce 0.2520 mse 0.00056 acc 1.000 lr 1.82e-04 L121 246m\n", " e3 40,100/62,312 loss 0.0893 nce 0.0889 mse 0.00033 acc 0.999 lr 1.82e-04 L27 246m\n", " e3 40,150/62,312 loss 0.0840 nce 0.0836 mse 0.00033 acc 1.000 lr 1.81e-04 L26 246m\n", " e3 40,200/62,312 loss 0.1546 nce 0.1542 mse 0.00045 acc 1.000 lr 1.80e-04 L88 246m\n", " e3 40,250/62,312 loss 0.2620 nce 0.2614 mse 0.00058 acc 1.000 lr 1.80e-04 L128 247m\n", " e3 40,300/62,312 loss 0.1282 nce 0.1278 mse 0.00042 acc 1.000 lr 1.79e-04 L77 247m\n", " e3 40,350/62,312 loss 0.2702 nce 0.2696 mse 0.00059 acc 1.000 lr 1.78e-04 L136 247m\n", " e3 40,400/62,312 loss 0.1519 nce 0.1514 mse 0.00049 acc 0.999 lr 1.77e-04 L15 248m\n", " e3 40,450/62,312 loss 0.0910 nce 0.0907 mse 0.00034 acc 0.999 lr 1.77e-04 L25 248m\n", " e3 40,500/62,312 loss 0.0897 nce 0.0894 mse 0.00033 acc 1.000 lr 1.76e-04 L32 248m\n", " e3 40,550/62,312 loss 0.0915 nce 0.0911 mse 0.00032 acc 1.000 lr 1.75e-04 L29 249m\n", " e3 40,600/62,312 loss 0.0882 nce 0.0879 mse 0.00032 acc 1.000 lr 1.75e-04 L31 249m\n", " e3 40,650/62,312 loss 0.1266 nce 0.1262 mse 0.00041 acc 1.000 lr 1.74e-04 L59 249m\n", " e3 40,700/62,312 loss 0.3927 nce 0.3920 mse 0.00070 acc 1.000 lr 1.73e-04 L256 249m\n", " e3 40,750/62,312 loss 0.6690 nce 0.6683 mse 0.00073 acc 0.896 lr 1.73e-04 L9 250m\n", " e3 40,800/62,312 loss 0.2221 nce 0.2216 mse 0.00054 acc 1.000 lr 1.72e-04 L116 250m\n", " e3 40,850/62,312 loss 0.1536 nce 0.1531 mse 0.00046 acc 1.000 lr 1.71e-04 L89 250m\n", " e3 40,900/62,312 loss 0.0943 nce 0.0939 mse 0.00039 acc 0.997 lr 1.70e-04 L19 251m\n", " e3 40,950/62,312 loss 0.0880 nce 0.0877 mse 0.00032 acc 1.000 lr 1.70e-04 L36 251m\n", " e3 41,000/62,312 loss 0.3177 nce 0.3171 mse 0.00064 acc 1.000 lr 1.69e-04 L170 251m\n", " VAL r1 0.9975 cos 0.8414 self_cos +0.0033 erank 101.4 cv 0.0845 | frame r1 0.9985\n", " e3 41,050/62,312 loss 0.2069 nce 0.2064 mse 0.00053 acc 1.000 lr 1.68e-04 L105 252m\n", " e3 41,100/62,312 loss 0.2891 nce 0.2885 mse 0.00061 acc 1.000 lr 1.68e-04 L147 252m\n", " e3 41,150/62,312 loss 0.0916 nce 0.0913 mse 0.00034 acc 0.999 lr 1.67e-04 L24 252m\n", " e3 41,200/62,312 loss 0.0994 nce 0.0990 mse 0.00033 acc 1.000 lr 1.66e-04 L33 253m\n", " e3 41,250/62,312 loss 0.0777 nce 0.0774 mse 0.00032 acc 0.999 lr 1.66e-04 L41 253m\n", " e3 41,300/62,312 loss 0.1352 nce 0.1348 mse 0.00046 acc 0.997 lr 1.65e-04 L16 253m\n", " e3 41,350/62,312 loss 0.0884 nce 0.0881 mse 0.00034 acc 0.999 lr 1.64e-04 L24 253m\n", " e3 41,400/62,312 loss 0.1250 nce 0.1246 mse 0.00041 acc 1.000 lr 1.63e-04 L65 254m\n", " e3 41,450/62,312 loss 0.1974 nce 0.1969 mse 0.00054 acc 0.996 lr 1.63e-04 L13 254m\n", " e3 41,500/62,312 loss 0.1895 nce 0.1890 mse 0.00050 acc 1.000 lr 1.62e-04 L99 254m\n", " e3 41,550/62,312 loss 0.1444 nce 0.1439 mse 0.00048 acc 0.999 lr 1.61e-04 L15 254m\n", " e3 41,600/62,312 loss 0.0813 nce 0.0810 mse 0.00033 acc 1.000 lr 1.61e-04 L44 255m\n", " e3 41,650/62,312 loss 0.1861 nce 0.1856 mse 0.00050 acc 1.000 lr 1.60e-04 L100 255m\n", " e3 41,700/62,312 loss 0.0871 nce 0.0868 mse 0.00033 acc 1.000 lr 1.59e-04 L25 256m\n", " e3 41,750/62,312 loss 0.1146 nce 0.1142 mse 0.00045 acc 1.000 lr 1.59e-04 L17 256m\n", " e3 41,800/62,312 loss 0.0847 nce 0.0844 mse 0.00032 acc 0.999 lr 1.58e-04 L29 256m\n", " e3 41,850/62,312 loss 0.0818 nce 0.0814 mse 0.00032 acc 1.000 lr 1.57e-04 L28 257m\n", " e3 41,900/62,312 loss 0.2568 nce 0.2563 mse 0.00058 acc 0.967 lr 1.57e-04 L11 257m\n", " e3 41,950/62,312 loss 0.0987 nce 0.0983 mse 0.00040 acc 0.999 lr 1.56e-04 L19 257m\n", " e3 42,000/62,312 loss 0.0877 nce 0.0873 mse 0.00039 acc 1.000 lr 1.55e-04 L20 258m\n", " VAL r1 0.9980 cos 0.8400 self_cos +0.0034 erank 102.5 cv 0.0771 | frame r1 0.9985\n", " e3 42,050/62,312 loss 0.1197 nce 0.1194 mse 0.00039 acc 1.000 lr 1.55e-04 L54 258m\n", " e3 42,100/62,312 loss 0.1338 nce 0.1334 mse 0.00044 acc 1.000 lr 1.54e-04 L82 258m\n", " e3 42,150/62,312 loss 0.0932 nce 0.0929 mse 0.00032 acc 1.000 lr 1.53e-04 L36 258m\n", " e3 42,200/62,312 loss 0.2994 nce 0.2988 mse 0.00063 acc 1.000 lr 1.53e-04 L170 259m\n", " e3 42,250/62,312 loss 0.0911 nce 0.0907 mse 0.00038 acc 1.000 lr 1.52e-04 L20 259m\n", " e3 42,300/62,312 loss 0.0901 nce 0.0898 mse 0.00032 acc 1.000 lr 1.51e-04 L32 259m\n", " e3 42,350/62,312 loss 0.0875 nce 0.0872 mse 0.00032 acc 1.000 lr 1.51e-04 L29 260m\n", " e3 42,400/62,312 loss 0.2035 nce 0.2029 mse 0.00055 acc 0.990 lr 1.50e-04 L12 260m\n", " e3 42,450/62,312 loss 0.0828 nce 0.0825 mse 0.00032 acc 1.000 lr 1.49e-04 L30 260m\n", " e3 42,500/62,312 loss 0.1195 nce 0.1190 mse 0.00041 acc 1.000 lr 1.49e-04 L71 261m\n", " e3 42,550/62,312 loss 0.0779 nce 0.0776 mse 0.00032 acc 1.000 lr 1.48e-04 L42 261m\n", " e3 42,600/62,312 loss 0.0864 nce 0.0861 mse 0.00033 acc 1.000 lr 1.47e-04 L27 261m\n", " e3 42,650/62,312 loss 0.0813 nce 0.0809 mse 0.00032 acc 1.000 lr 1.47e-04 L37 262m\n", " e3 42,700/62,312 loss 0.2483 nce 0.2477 mse 0.00058 acc 1.000 lr 1.46e-04 L136 262m\n", " e3 42,750/62,312 loss 0.1285 nce 0.1281 mse 0.00042 acc 0.999 lr 1.45e-04 L76 262m\n", " e3 42,800/62,312 loss 0.0976 nce 0.0972 mse 0.00033 acc 1.000 lr 1.45e-04 L34 263m\n", " e3 42,850/62,312 loss 0.0900 nce 0.0897 mse 0.00032 acc 1.000 lr 1.44e-04 L36 263m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 42895)\n", " e3 42,900/62,312 loss 0.1239 nce 0.1235 mse 0.00041 acc 1.000 lr 1.43e-04 L64 264m\n", " e3 42,950/62,312 loss 0.0908 nce 0.0904 mse 0.00036 acc 0.999 lr 1.43e-04 L22 264m\n", " e3 43,000/62,312 loss 0.0879 nce 0.0876 mse 0.00037 acc 0.999 lr 1.42e-04 L21 264m\n", " VAL r1 0.9975 cos 0.8409 self_cos +0.0034 erank 102.8 cv 0.0729 | frame r1 0.9985\n", " e3 43,050/62,312 loss 0.2389 nce 0.2384 mse 0.00056 acc 1.000 lr 1.41e-04 L122 265m\n", " e3 43,100/62,312 loss 0.0774 nce 0.0771 mse 0.00032 acc 1.000 lr 1.41e-04 L39 265m\n", " e3 43,150/62,312 loss 0.0869 nce 0.0866 mse 0.00036 acc 0.999 lr 1.40e-04 L22 265m\n", " e3 43,200/62,312 loss 0.0833 nce 0.0830 mse 0.00033 acc 1.000 lr 1.39e-04 L28 265m\n", " e3 43,250/62,312 loss 0.1563 nce 0.1559 mse 0.00045 acc 1.000 lr 1.39e-04 L88 266m\n", " e3 43,300/62,312 loss 0.2436 nce 0.2431 mse 0.00057 acc 1.000 lr 1.38e-04 L128 266m\n", " e3 43,350/62,312 loss 0.0794 nce 0.0791 mse 0.00032 acc 1.000 lr 1.37e-04 L39 266m\n", " e3 43,400/62,312 loss 0.1282 nce 0.1277 mse 0.00042 acc 1.000 lr 1.37e-04 L70 267m\n", " e3 43,450/62,312 loss 0.1750 nce 0.1745 mse 0.00051 acc 0.999 lr 1.36e-04 L14 267m\n", " e3 43,500/62,312 loss 0.0814 nce 0.0811 mse 0.00032 acc 1.000 lr 1.35e-04 L38 267m\n", " e3 43,550/62,312 loss 0.2590 nce 0.2585 mse 0.00059 acc 1.000 lr 1.35e-04 L137 268m\n", " e3 43,600/62,312 loss 0.0912 nce 0.0909 mse 0.00032 acc 1.000 lr 1.34e-04 L36 268m\n", " e3 43,650/62,312 loss 0.1540 nce 0.1535 mse 0.00046 acc 1.000 lr 1.33e-04 L88 268m\n", " e3 43,700/62,312 loss 0.1229 nce 0.1225 mse 0.00042 acc 1.000 lr 1.33e-04 L69 269m\n", " e3 43,750/62,312 loss 0.0815 nce 0.0812 mse 0.00032 acc 1.000 lr 1.32e-04 L29 269m\n", " e3 43,800/62,312 loss 0.0872 nce 0.0869 mse 0.00032 acc 1.000 lr 1.31e-04 L33 269m\n", " e3 43,850/62,312 loss 0.6628 nce 0.6621 mse 0.00072 acc 0.892 lr 1.31e-04 L9 269m\n", " e3 43,900/62,312 loss 0.0943 nce 0.0939 mse 0.00035 acc 1.000 lr 1.30e-04 L48 270m\n", " e3 43,950/62,312 loss 0.1261 nce 0.1257 mse 0.00041 acc 0.999 lr 1.30e-04 L64 270m\n", " e3 44,000/62,312 loss 0.0857 nce 0.0853 mse 0.00038 acc 0.999 lr 1.29e-04 L20 270m\n", " VAL r1 0.9980 cos 0.8423 self_cos +0.0032 erank 102.2 cv 0.0811 | frame r1 0.9985\n", " e3 44,050/62,312 loss 0.0799 nce 0.0796 mse 0.00032 acc 1.000 lr 1.28e-04 L43 271m\n", " e3 44,100/62,312 loss 0.0912 nce 0.0909 mse 0.00033 acc 1.000 lr 1.28e-04 L26 271m\n", " e3 44,150/62,312 loss 0.1968 nce 0.1963 mse 0.00052 acc 1.000 lr 1.27e-04 L105 271m\n", " e3 44,200/62,312 loss 0.0891 nce 0.0888 mse 0.00032 acc 1.000 lr 1.26e-04 L29 272m\n", " e3 44,250/62,312 loss 0.2843 nce 0.2837 mse 0.00060 acc 1.000 lr 1.26e-04 L148 272m\n", " e3 44,300/62,312 loss 0.0925 nce 0.0921 mse 0.00036 acc 0.998 lr 1.25e-04 L22 272m\n", " e3 44,350/62,312 loss 0.0834 nce 0.0830 mse 0.00032 acc 1.000 lr 1.24e-04 L30 273m\n", " e3 44,400/62,312 loss 0.0819 nce 0.0816 mse 0.00032 acc 1.000 lr 1.24e-04 L29 273m\n", " e3 44,450/62,312 loss 0.0830 nce 0.0827 mse 0.00033 acc 1.000 lr 1.23e-04 L28 273m\n", " e3 44,500/62,312 loss 0.0808 nce 0.0805 mse 0.00032 acc 1.000 lr 1.23e-04 L29 273m\n", " e3 44,550/62,312 loss 0.1538 nce 0.1534 mse 0.00049 acc 0.998 lr 1.22e-04 L15 274m\n", " e3 44,600/62,312 loss 0.0814 nce 0.0810 mse 0.00032 acc 1.000 lr 1.21e-04 L27 274m\n", " e3 44,650/62,312 loss 0.2341 nce 0.2336 mse 0.00056 acc 1.000 lr 1.21e-04 L122 274m\n", " e3 44,700/62,312 loss 0.0852 nce 0.0849 mse 0.00032 acc 1.000 lr 1.20e-04 L37 275m\n", " e3 44,750/62,312 loss 0.1448 nce 0.1443 mse 0.00048 acc 0.998 lr 1.19e-04 L15 275m\n", " e3 44,800/62,312 loss 0.1621 nce 0.1616 mse 0.00046 acc 0.999 lr 1.19e-04 L89 275m\n", " e3 44,850/62,312 loss 0.1290 nce 0.1286 mse 0.00039 acc 0.999 lr 1.18e-04 L53 276m\n", " e3 44,900/62,312 loss 0.0880 nce 0.0876 mse 0.00032 acc 1.000 lr 1.18e-04 L28 276m\n", " e3 44,950/62,312 loss 0.0921 nce 0.0917 mse 0.00032 acc 1.000 lr 1.17e-04 L29 276m\n", " e3 45,000/62,312 loss 0.0890 nce 0.0886 mse 0.00032 acc 1.000 lr 1.16e-04 L38 276m\n", " VAL r1 0.9975 cos 0.8410 self_cos +0.0029 erank 102.7 cv 0.0723 | frame r1 0.9985\n", " e3 45,050/62,312 loss 0.0835 nce 0.0832 mse 0.00032 acc 1.000 lr 1.16e-04 L30 277m\n", " e3 45,100/62,312 loss 0.1333 nce 0.1328 mse 0.00043 acc 1.000 lr 1.15e-04 L82 277m\n", " e3 45,150/62,312 loss 0.0939 nce 0.0936 mse 0.00038 acc 0.999 lr 1.15e-04 L20 277m\n", " e3 45,200/62,312 loss 0.0853 nce 0.0850 mse 0.00034 acc 1.000 lr 1.14e-04 L45 278m\n", " e3 45,250/62,312 loss 0.0867 nce 0.0864 mse 0.00036 acc 0.998 lr 1.13e-04 L21 278m\n", " e3 45,300/62,312 loss 0.6572 nce 0.6565 mse 0.00072 acc 0.892 lr 1.13e-04 L9 278m\n", " e3 45,350/62,312 loss 0.0819 nce 0.0816 mse 0.00032 acc 1.000 lr 1.12e-04 L31 279m\n", " e3 45,400/62,312 loss 0.2741 nce 0.2735 mse 0.00060 acc 0.964 lr 1.12e-04 L11 279m\n", " e3 45,450/62,312 loss 0.1189 nce 0.1185 mse 0.00040 acc 1.000 lr 1.11e-04 L58 279m\n", " e3 45,500/62,312 loss 0.1217 nce 0.1212 mse 0.00042 acc 1.000 lr 1.10e-04 L65 280m\n", " e3 45,550/62,312 loss 0.0894 nce 0.0891 mse 0.00032 acc 1.000 lr 1.10e-04 L36 280m\n", " e3 45,600/62,312 loss 0.2825 nce 0.2819 mse 0.00060 acc 1.000 lr 1.09e-04 L147 280m\n", " e3 45,650/62,312 loss 0.1236 nce 0.1232 mse 0.00041 acc 0.999 lr 1.09e-04 L64 281m\n", " e3 45,700/62,312 loss 0.1837 nce 0.1832 mse 0.00055 acc 0.988 lr 1.08e-04 L12 281m\n", " e3 45,750/62,312 loss 0.0931 nce 0.0927 mse 0.00039 acc 0.998 lr 1.07e-04 L19 281m\n", " e3 45,800/62,312 loss 0.0889 nce 0.0886 mse 0.00032 acc 1.000 lr 1.07e-04 L34 282m\n", " e3 45,850/62,312 loss 0.0906 nce 0.0902 mse 0.00032 acc 1.000 lr 1.06e-04 L29 282m\n", " e3 45,900/62,312 loss 0.0774 nce 0.0771 mse 0.00031 acc 1.000 lr 1.06e-04 L41 282m\n", " e3 45,950/62,312 loss 0.7223 nce 0.7215 mse 0.00073 acc 0.880 lr 1.05e-04 L9 283m\n", " e3 46,000/62,312 loss 0.0897 nce 0.0894 mse 0.00034 acc 0.998 lr 1.04e-04 L24 283m\n", " VAL r1 0.9985 cos 0.8403 self_cos +0.0035 erank 103.3 cv 0.0920 | frame r1 0.9985\n", " e3 46,050/62,312 loss 0.1127 nce 0.1123 mse 0.00038 acc 1.000 lr 1.04e-04 L53 283m\n", " e3 46,100/62,312 loss 0.0922 nce 0.0918 mse 0.00033 acc 1.000 lr 1.03e-04 L33 283m\n", " e3 46,150/62,312 loss 0.2106 nce 0.2100 mse 0.00053 acc 0.995 lr 1.03e-04 L13 284m\n", " e3 46,200/62,312 loss 0.2544 nce 0.2538 mse 0.00058 acc 1.000 lr 1.02e-04 L136 284m\n", " e3 46,250/62,312 loss 0.0877 nce 0.0874 mse 0.00032 acc 1.000 lr 1.01e-04 L35 284m\n", " e3 46,300/62,312 loss 0.0865 nce 0.0862 mse 0.00032 acc 1.000 lr 1.01e-04 L36 285m\n", " e3 46,350/62,312 loss 0.0882 nce 0.0878 mse 0.00033 acc 1.000 lr 1.00e-04 L25 285m\n", " e3 46,400/62,312 loss 0.0881 nce 0.0878 mse 0.00031 acc 1.000 lr 9.97e-05 L37 285m\n", " e3 46,450/62,312 loss 0.0812 nce 0.0809 mse 0.00032 acc 1.000 lr 9.91e-05 L35 285m\n", " e3 46,500/62,312 loss 0.0879 nce 0.0876 mse 0.00032 acc 1.000 lr 9.86e-05 L36 286m\n", " e3 46,550/62,312 loss 0.0878 nce 0.0874 mse 0.00032 acc 1.000 lr 9.80e-05 L34 286m\n", " e3 46,600/62,312 loss 0.0950 nce 0.0946 mse 0.00038 acc 1.000 lr 9.74e-05 L20 286m\n", " e3 46,650/62,312 loss 0.1219 nce 0.1215 mse 0.00041 acc 0.999 lr 9.68e-05 L64 286m\n", " e3 46,700/62,312 loss 0.0902 nce 0.0898 mse 0.00032 acc 1.000 lr 9.63e-05 L33 287m\n", " e4 46,750/62,312 loss 0.2618 nce 0.2612 mse 0.00059 acc 1.000 lr 9.57e-05 L137 287m\n", " e4 46,800/62,312 loss 0.0798 nce 0.0794 mse 0.00032 acc 1.000 lr 9.51e-05 L41 287m\n", " e4 46,850/62,312 loss 0.1121 nce 0.1117 mse 0.00044 acc 1.000 lr 9.46e-05 L17 288m\n", " e4 46,900/62,312 loss 0.0878 nce 0.0874 mse 0.00033 acc 1.000 lr 9.40e-05 L26 288m\n", " e4 46,950/62,312 loss 0.2242 nce 0.2236 mse 0.00056 acc 1.000 lr 9.34e-05 L122 288m\n", " e4 47,000/62,312 loss 0.0860 nce 0.0857 mse 0.00033 acc 0.999 lr 9.29e-05 L26 288m\n", " VAL r1 0.9980 cos 0.8414 self_cos +0.0028 erank 102.7 cv 0.0756 | frame r1 0.9985\n", " e4 47,050/62,312 loss 0.0835 nce 0.0832 mse 0.00032 acc 1.000 lr 9.23e-05 L35 289m\n", " e4 47,100/62,312 loss 0.1140 nce 0.1135 mse 0.00044 acc 0.999 lr 9.18e-05 L17 289m\n", " e4 47,150/62,312 loss 0.0836 nce 0.0833 mse 0.00032 acc 1.000 lr 9.12e-05 L39 289m\n", " e4 47,200/62,312 loss 0.0854 nce 0.0850 mse 0.00034 acc 1.000 lr 9.07e-05 L24 290m\n", " e4 47,250/62,312 loss 0.1962 nce 0.1957 mse 0.00051 acc 0.996 lr 9.01e-05 L14 290m\n", " e4 47,300/62,312 loss 0.0867 nce 0.0864 mse 0.00037 acc 0.999 lr 8.95e-05 L21 290m\n", " e4 47,350/62,312 loss 0.0835 nce 0.0831 mse 0.00032 acc 1.000 lr 8.90e-05 L30 290m\n", " e4 47,400/62,312 loss 0.3135 nce 0.3129 mse 0.00063 acc 1.000 lr 8.84e-05 L171 291m\n", " e4 47,450/62,312 loss 0.1953 nce 0.1948 mse 0.00052 acc 1.000 lr 8.79e-05 L104 291m\n", " e4 47,500/62,312 loss 0.0725 nce 0.0722 mse 0.00031 acc 1.000 lr 8.74e-05 L41 291m\n", " e4 47,550/62,312 loss 0.0869 nce 0.0865 mse 0.00032 acc 1.000 lr 8.68e-05 L37 292m\n", " e4 47,600/62,312 loss 0.0904 nce 0.0901 mse 0.00032 acc 1.000 lr 8.63e-05 L32 292m\n", " e4 47,650/62,312 loss 0.0936 nce 0.0933 mse 0.00032 acc 1.000 lr 8.57e-05 L36 292m\n", " e4 47,700/62,312 loss 0.1144 nce 0.1140 mse 0.00041 acc 1.000 lr 8.52e-05 L70 292m\n", " e4 47,750/62,312 loss 0.1243 nce 0.1239 mse 0.00040 acc 0.999 lr 8.46e-05 L58 293m\n", " e4 47,800/62,312 loss 0.1177 nce 0.1173 mse 0.00041 acc 1.000 lr 8.41e-05 L71 293m\n", " e4 47,850/62,312 loss 0.4607 nce 0.4599 mse 0.00077 acc 0.973 lr 8.36e-05 L8 293m\n", " e4 47,900/62,312 loss 0.0822 nce 0.0819 mse 0.00032 acc 1.000 lr 8.30e-05 L38 294m\n", " e4 47,950/62,312 loss 0.0855 nce 0.0851 mse 0.00036 acc 0.999 lr 8.25e-05 L22 294m\n", " e4 48,000/62,312 loss 0.1591 nce 0.1586 mse 0.00046 acc 1.000 lr 8.20e-05 L89 294m\n", " VAL r1 0.9975 cos 0.8414 self_cos +0.0032 erank 102.7 cv 0.0685 | frame r1 0.9985\n", " e4 48,050/62,312 loss 0.0852 nce 0.0848 mse 0.00033 acc 1.000 lr 8.14e-05 L25 295m\n", " e4 48,100/62,312 loss 0.0929 nce 0.0925 mse 0.00032 acc 1.000 lr 8.09e-05 L35 295m\n", " e4 48,150/62,312 loss 0.2461 nce 0.2455 mse 0.00058 acc 0.972 lr 8.04e-05 L11 295m\n", " e4 48,200/62,312 loss 0.2075 nce 0.2070 mse 0.00051 acc 0.998 lr 7.98e-05 L14 296m\n", " e4 48,250/62,312 loss 0.1982 nce 0.1977 mse 0.00052 acc 0.991 lr 7.93e-05 L13 296m\n", " e4 48,300/62,312 loss 0.1967 nce 0.1962 mse 0.00052 acc 0.998 lr 7.88e-05 L13 296m\n", " e4 48,350/62,312 loss 0.0874 nce 0.0871 mse 0.00032 acc 1.000 lr 7.83e-05 L34 296m\n", " e4 48,400/62,312 loss 0.0858 nce 0.0855 mse 0.00036 acc 1.000 lr 7.78e-05 L21 297m\n", " e4 48,450/62,312 loss 0.0846 nce 0.0842 mse 0.00033 acc 1.000 lr 7.72e-05 L26 297m\n", " e4 48,500/62,312 loss 0.2801 nce 0.2795 mse 0.00060 acc 1.000 lr 7.67e-05 L148 297m\n", " e4 48,550/62,312 loss 0.2329 nce 0.2323 mse 0.00056 acc 1.000 lr 7.62e-05 L122 298m\n", " e4 48,600/62,312 loss 0.0910 nce 0.0906 mse 0.00036 acc 0.998 lr 7.57e-05 L22 298m\n", " e4 48,650/62,312 loss 0.3835 nce 0.3829 mse 0.00069 acc 1.000 lr 7.52e-05 L256 298m\n", " e4 48,700/62,312 loss 0.1599 nce 0.1595 mse 0.00049 acc 0.997 lr 7.47e-05 L15 298m\n", " e4 48,750/62,312 loss 0.2549 nce 0.2544 mse 0.00059 acc 0.969 lr 7.42e-05 L11 299m\n", " e4 48,800/62,312 loss 0.0858 nce 0.0855 mse 0.00034 acc 1.000 lr 7.36e-05 L24 299m\n", " e4 48,850/62,312 loss 0.6598 nce 0.6591 mse 0.00073 acc 0.895 lr 7.31e-05 L9 300m\n", " e4 48,900/62,312 loss 0.2409 nce 0.2404 mse 0.00057 acc 1.000 lr 7.26e-05 L128 300m\n", " e4 48,950/62,312 loss 0.2872 nce 0.2866 mse 0.00061 acc 1.000 lr 7.21e-05 L148 300m\n", " e4 49,000/62,312 loss 0.2122 nce 0.2117 mse 0.00053 acc 1.000 lr 7.16e-05 L110 300m\n", " VAL r1 0.9980 cos 0.8414 self_cos +0.0033 erank 103.1 cv 0.0843 | frame r1 0.9985\n", " e4 49,050/62,312 loss 0.6326 nce 0.6318 mse 0.00072 acc 0.899 lr 7.11e-05 L9 301m\n", " e4 49,100/62,312 loss 0.3781 nce 0.3774 mse 0.00069 acc 1.000 lr 7.06e-05 L256 301m\n", " e4 49,150/62,312 loss 0.2166 nce 0.2160 mse 0.00053 acc 1.000 lr 7.01e-05 L110 301m\n", " e4 49,200/62,312 loss 0.1920 nce 0.1915 mse 0.00051 acc 0.996 lr 6.96e-05 L14 302m\n", " e4 49,250/62,312 loss 0.0834 nce 0.0831 mse 0.00032 acc 1.000 lr 6.91e-05 L29 302m\n", " e4 49,300/62,312 loss 0.1162 nce 0.1158 mse 0.00040 acc 0.999 lr 6.86e-05 L58 302m\n", " e4 49,350/62,312 loss 0.2495 nce 0.2489 mse 0.00058 acc 1.000 lr 6.82e-05 L137 303m\n", " e4 49,400/62,312 loss 0.0941 nce 0.0938 mse 0.00032 acc 1.000 lr 6.77e-05 L36 303m\n", " e4 49,450/62,312 loss 0.0855 nce 0.0852 mse 0.00032 acc 0.999 lr 6.72e-05 L27 303m\n", " e4 49,500/62,312 loss 0.0888 nce 0.0884 mse 0.00032 acc 1.000 lr 6.67e-05 L34 303m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 49525)\n", " e4 49,550/62,312 loss 0.2089 nce 0.2084 mse 0.00053 acc 0.993 lr 6.62e-05 L13 304m\n", " e4 49,600/62,312 loss 0.0844 nce 0.0841 mse 0.00032 acc 1.000 lr 6.57e-05 L32 305m\n", " e4 49,650/62,312 loss 0.4735 nce 0.4727 mse 0.00076 acc 0.970 lr 6.52e-05 L8 305m\n", " e4 49,700/62,312 loss 0.0866 nce 0.0863 mse 0.00032 acc 1.000 lr 6.48e-05 L36 305m\n", " e4 49,750/62,312 loss 0.0834 nce 0.0831 mse 0.00032 acc 1.000 lr 6.43e-05 L30 306m\n", " e4 49,800/62,312 loss 0.0902 nce 0.0899 mse 0.00032 acc 1.000 lr 6.38e-05 L31 306m\n", " e4 49,850/62,312 loss 0.2398 nce 0.2392 mse 0.00058 acc 0.971 lr 6.33e-05 L11 306m\n", " e4 49,900/62,312 loss 0.3829 nce 0.3822 mse 0.00069 acc 1.000 lr 6.29e-05 L256 306m\n", " e4 49,950/62,312 loss 0.1815 nce 0.1810 mse 0.00051 acc 0.998 lr 6.24e-05 L14 307m\n", " e4 50,000/62,312 loss 0.0886 nce 0.0883 mse 0.00037 acc 1.000 lr 6.19e-05 L21 307m\n", " VAL r1 0.9975 cos 0.8407 self_cos +0.0035 erank 103.1 cv 0.0700 | frame r1 0.9985\n", " e4 50,050/62,312 loss 0.0878 nce 0.0874 mse 0.00038 acc 0.999 lr 6.14e-05 L20 307m\n", " e4 50,100/62,312 loss 0.1232 nce 0.1228 mse 0.00039 acc 1.000 lr 6.10e-05 L53 308m\n", " e4 50,150/62,312 loss 0.0875 nce 0.0872 mse 0.00032 acc 1.000 lr 6.05e-05 L35 308m\n", " e4 50,200/62,312 loss 0.2117 nce 0.2112 mse 0.00053 acc 1.000 lr 6.00e-05 L111 308m\n", " e4 50,250/62,312 loss 0.0821 nce 0.0818 mse 0.00032 acc 1.000 lr 5.96e-05 L35 308m\n", " e4 50,300/62,312 loss 0.0941 nce 0.0937 mse 0.00035 acc 0.999 lr 5.91e-05 L24 309m\n", " e4 50,350/62,312 loss 0.0895 nce 0.0891 mse 0.00037 acc 0.998 lr 5.87e-05 L21 309m\n", " e4 50,400/62,312 loss 0.2267 nce 0.2262 mse 0.00055 acc 1.000 lr 5.82e-05 L116 309m\n", " e4 50,450/62,312 loss 0.0888 nce 0.0885 mse 0.00032 acc 0.999 lr 5.77e-05 L27 310m\n", " e4 50,500/62,312 loss 0.2913 nce 0.2906 mse 0.00063 acc 1.000 lr 5.73e-05 L170 310m\n", " e4 50,550/62,312 loss 0.0826 nce 0.0823 mse 0.00032 acc 1.000 lr 5.68e-05 L27 310m\n", " e4 50,600/62,312 loss 0.0788 nce 0.0784 mse 0.00031 acc 1.000 lr 5.64e-05 L29 311m\n", " e4 50,650/62,312 loss 0.0887 nce 0.0884 mse 0.00032 acc 1.000 lr 5.59e-05 L34 311m\n", " e4 50,700/62,312 loss 0.0771 nce 0.0768 mse 0.00031 acc 1.000 lr 5.55e-05 L41 311m\n", " e4 50,750/62,312 loss 0.1560 nce 0.1555 mse 0.00049 acc 0.996 lr 5.50e-05 L15 312m\n", " e4 50,800/62,312 loss 0.0812 nce 0.0809 mse 0.00032 acc 1.000 lr 5.46e-05 L31 312m\n", " e4 50,850/62,312 loss 0.2424 nce 0.2418 mse 0.00057 acc 1.000 lr 5.42e-05 L129 312m\n", " e4 50,900/62,312 loss 0.0872 nce 0.0869 mse 0.00032 acc 1.000 lr 5.37e-05 L30 313m\n", " e4 50,950/62,312 loss 0.0834 nce 0.0831 mse 0.00032 acc 1.000 lr 5.33e-05 L39 313m\n", " e4 51,000/62,312 loss 0.1911 nce 0.1905 mse 0.00055 acc 0.989 lr 5.28e-05 L12 313m\n", " VAL r1 0.9975 cos 0.8414 self_cos +0.0031 erank 103.1 cv 0.0705 | frame r1 0.9985\n", " e4 51,050/62,312 loss 0.0818 nce 0.0814 mse 0.00032 acc 1.000 lr 5.24e-05 L28 314m\n", " e4 51,100/62,312 loss 0.0869 nce 0.0865 mse 0.00037 acc 0.999 lr 5.20e-05 L21 314m\n", " e4 51,150/62,312 loss 0.0765 nce 0.0762 mse 0.00031 acc 1.000 lr 5.15e-05 L39 314m\n", " e4 51,200/62,312 loss 0.1033 nce 0.1029 mse 0.00041 acc 0.998 lr 5.11e-05 L18 314m\n", " e4 51,250/62,312 loss 0.1501 nce 0.1496 mse 0.00048 acc 0.996 lr 5.07e-05 L15 315m\n", " e4 51,300/62,312 loss 0.1316 nce 0.1312 mse 0.00041 acc 1.000 lr 5.02e-05 L58 315m\n", " e4 51,350/62,312 loss 0.0867 nce 0.0864 mse 0.00033 acc 1.000 lr 4.98e-05 L27 315m\n", " e4 51,400/62,312 loss 0.6690 nce 0.6683 mse 0.00072 acc 0.892 lr 4.94e-05 L9 316m\n", " e4 51,450/62,312 loss 0.2595 nce 0.2589 mse 0.00058 acc 1.000 lr 4.90e-05 L136 316m\n", " e4 51,500/62,312 loss 0.2249 nce 0.2244 mse 0.00055 acc 1.000 lr 4.85e-05 L122 316m\n", " e4 51,550/62,312 loss 0.1469 nce 0.1464 mse 0.00045 acc 0.999 lr 4.81e-05 L89 317m\n", " e4 51,600/62,312 loss 0.0889 nce 0.0886 mse 0.00032 acc 1.000 lr 4.77e-05 L34 317m\n", " e4 51,650/62,312 loss 0.2290 nce 0.2285 mse 0.00056 acc 1.000 lr 4.73e-05 L121 317m\n", " e4 51,700/62,312 loss 0.0809 nce 0.0806 mse 0.00031 acc 1.000 lr 4.69e-05 L39 318m\n", " e4 51,750/62,312 loss 0.1190 nce 0.1186 mse 0.00041 acc 1.000 lr 4.65e-05 L76 318m\n", " e4 51,800/62,312 loss 0.4687 nce 0.4679 mse 0.00077 acc 0.970 lr 4.61e-05 L8 318m\n", " e4 51,850/62,312 loss 0.1179 nce 0.1174 mse 0.00046 acc 0.999 lr 4.56e-05 L16 319m\n", " e4 51,900/62,312 loss 0.0885 nce 0.0882 mse 0.00038 acc 0.999 lr 4.52e-05 L20 319m\n", " e4 51,950/62,312 loss 0.0854 nce 0.0851 mse 0.00032 acc 1.000 lr 4.48e-05 L30 319m\n", " e4 52,000/62,312 loss 0.0819 nce 0.0816 mse 0.00031 acc 1.000 lr 4.44e-05 L35 320m\n", " VAL r1 0.9980 cos 0.8415 self_cos +0.0034 erank 103.2 cv 0.0738 | frame r1 0.9985\n", " e4 52,050/62,312 loss 0.0866 nce 0.0863 mse 0.00032 acc 1.000 lr 4.40e-05 L29 320m\n", " e4 52,100/62,312 loss 0.3816 nce 0.3809 mse 0.00069 acc 1.000 lr 4.36e-05 L256 320m\n", " e4 52,150/62,312 loss 0.0888 nce 0.0885 mse 0.00032 acc 0.999 lr 4.32e-05 L34 320m\n", " e4 52,200/62,312 loss 0.0837 nce 0.0834 mse 0.00032 acc 1.000 lr 4.28e-05 L29 321m\n", " e4 52,250/62,312 loss 0.1202 nce 0.1199 mse 0.00040 acc 0.999 lr 4.24e-05 L58 321m\n", " e4 52,300/62,312 loss 0.1960 nce 0.1955 mse 0.00055 acc 0.984 lr 4.20e-05 L12 321m\n", " e4 52,350/62,312 loss 0.1814 nce 0.1808 mse 0.00055 acc 0.995 lr 4.16e-05 L12 322m\n", " e4 52,400/62,312 loss 0.1994 nce 0.1989 mse 0.00053 acc 1.000 lr 4.13e-05 L111 322m\n", " e4 52,450/62,312 loss 0.2165 nce 0.2159 mse 0.00053 acc 1.000 lr 4.09e-05 L110 322m\n", " e4 52,500/62,312 loss 0.1304 nce 0.1300 mse 0.00042 acc 0.999 lr 4.05e-05 L64 323m\n", " e4 52,550/62,312 loss 0.0940 nce 0.0937 mse 0.00032 acc 1.000 lr 4.01e-05 L33 323m\n", " e4 52,600/62,312 loss 0.0837 nce 0.0833 mse 0.00034 acc 1.000 lr 3.97e-05 L24 323m\n", " e4 52,650/62,312 loss 0.0844 nce 0.0841 mse 0.00035 acc 1.000 lr 3.93e-05 L23 323m\n", " e4 52,700/62,312 loss 0.0883 nce 0.0880 mse 0.00032 acc 1.000 lr 3.90e-05 L34 324m\n", " e4 52,750/62,312 loss 0.2316 nce 0.2311 mse 0.00055 acc 1.000 lr 3.86e-05 L122 324m\n", " e4 52,800/62,312 loss 0.0856 nce 0.0853 mse 0.00032 acc 1.000 lr 3.82e-05 L30 324m\n", " e4 52,850/62,312 loss 0.0863 nce 0.0860 mse 0.00032 acc 1.000 lr 3.78e-05 L31 325m\n", " e4 52,900/62,312 loss 0.0940 nce 0.0936 mse 0.00041 acc 0.998 lr 3.74e-05 L18 325m\n", " e4 52,950/62,312 loss 0.1312 nce 0.1307 mse 0.00043 acc 0.999 lr 3.71e-05 L83 325m\n", " e4 53,000/62,312 loss 0.3165 nce 0.3158 mse 0.00063 acc 1.000 lr 3.67e-05 L169 326m\n", " VAL r1 0.9980 cos 0.8419 self_cos +0.0030 erank 103.1 cv 0.0726 | frame r1 0.9985\n", " e4 53,050/62,312 loss 0.0712 nce 0.0709 mse 0.00031 acc 1.000 lr 3.63e-05 L39 326m\n", " e4 53,100/62,312 loss 0.0846 nce 0.0842 mse 0.00032 acc 1.000 lr 3.60e-05 L28 326m\n", " e4 53,150/62,312 loss 0.1263 nce 0.1259 mse 0.00042 acc 1.000 lr 3.56e-05 L77 327m\n", " e4 53,200/62,312 loss 0.0863 nce 0.0859 mse 0.00032 acc 1.000 lr 3.53e-05 L32 327m\n", " e4 53,250/62,312 loss 0.1303 nce 0.1299 mse 0.00043 acc 1.000 lr 3.49e-05 L82 327m\n", " e4 53,300/62,312 loss 0.4358 nce 0.4351 mse 0.00064 acc 0.932 lr 3.45e-05 L10 327m\n", " e4 53,350/62,312 loss 0.0884 nce 0.0881 mse 0.00033 acc 1.000 lr 3.42e-05 L28 328m\n", " e4 53,400/62,312 loss 0.0896 nce 0.0893 mse 0.00032 acc 1.000 lr 3.38e-05 L34 328m\n", " e4 53,450/62,312 loss 0.0807 nce 0.0804 mse 0.00032 acc 1.000 lr 3.35e-05 L43 328m\n", " e4 53,500/62,312 loss 0.0891 nce 0.0888 mse 0.00032 acc 1.000 lr 3.31e-05 L31 329m\n", " e4 53,550/62,312 loss 0.0862 nce 0.0859 mse 0.00032 acc 1.000 lr 3.28e-05 L27 329m\n", " e4 53,600/62,312 loss 0.0838 nce 0.0834 mse 0.00032 acc 1.000 lr 3.24e-05 L33 329m\n", " e4 53,650/62,312 loss 0.3717 nce 0.3710 mse 0.00069 acc 0.999 lr 3.21e-05 L256 329m\n", " e4 53,700/62,312 loss 0.0837 nce 0.0834 mse 0.00035 acc 1.000 lr 3.17e-05 L23 330m\n", " e4 53,750/62,312 loss 0.0960 nce 0.0956 mse 0.00041 acc 0.998 lr 3.14e-05 L18 330m\n", " e4 53,800/62,312 loss 0.4551 nce 0.4543 mse 0.00077 acc 0.971 lr 3.10e-05 L8 330m\n", " e4 53,850/62,312 loss 0.2168 nce 0.2162 mse 0.00055 acc 1.000 lr 3.07e-05 L116 330m\n", " e4 53,900/62,312 loss 0.0812 nce 0.0809 mse 0.00032 acc 1.000 lr 3.04e-05 L43 331m\n", " e4 53,950/62,312 loss 0.0896 nce 0.0893 mse 0.00032 acc 1.000 lr 3.00e-05 L35 331m\n", " e4 54,000/62,312 loss 0.0950 nce 0.0947 mse 0.00035 acc 0.996 lr 2.97e-05 L24 331m\n", " VAL r1 0.9980 cos 0.8417 self_cos +0.0032 erank 103.2 cv 0.0719 | frame r1 0.9985\n", " e4 54,050/62,312 loss 0.1859 nce 0.1854 mse 0.00050 acc 1.000 lr 2.94e-05 L100 332m\n", " e4 54,100/62,312 loss 0.0863 nce 0.0859 mse 0.00036 acc 0.999 lr 2.90e-05 L22 332m\n", " e4 54,150/62,312 loss 0.0939 nce 0.0936 mse 0.00032 acc 1.000 lr 2.87e-05 L34 332m\n", " e4 54,200/62,312 loss 0.1844 nce 0.1839 mse 0.00050 acc 1.000 lr 2.84e-05 L100 333m\n", " e4 54,250/62,312 loss 0.2584 nce 0.2578 mse 0.00059 acc 1.000 lr 2.81e-05 L136 333m\n", " e4 54,300/62,312 loss 0.0821 nce 0.0818 mse 0.00032 acc 1.000 lr 2.77e-05 L36 333m\n", " e4 54,350/62,312 loss 0.0828 nce 0.0825 mse 0.00032 acc 1.000 lr 2.74e-05 L34 334m\n", " e4 54,400/62,312 loss 0.0821 nce 0.0817 mse 0.00032 acc 1.000 lr 2.71e-05 L27 334m\n", " e4 54,450/62,312 loss 0.1212 nce 0.1208 mse 0.00042 acc 1.000 lr 2.68e-05 L76 334m\n", " e4 54,500/62,312 loss 0.0842 nce 0.0839 mse 0.00033 acc 1.000 lr 2.65e-05 L25 335m\n", " e4 54,550/62,312 loss 0.0941 nce 0.0938 mse 0.00032 acc 1.000 lr 2.62e-05 L32 335m\n", " e4 54,600/62,312 loss 0.1821 nce 0.1816 mse 0.00050 acc 1.000 lr 2.58e-05 L100 335m\n", " e4 54,650/62,312 loss 0.0874 nce 0.0870 mse 0.00032 acc 1.000 lr 2.55e-05 L32 336m\n", " e4 54,700/62,312 loss 0.6636 nce 0.6629 mse 0.00072 acc 0.891 lr 2.52e-05 L9 336m\n", " e4 54,750/62,312 loss 0.1841 nce 0.1836 mse 0.00050 acc 1.000 lr 2.49e-05 L100 336m\n", " e4 54,800/62,312 loss 0.0858 nce 0.0854 mse 0.00035 acc 1.000 lr 2.46e-05 L23 337m\n", " e4 54,850/62,312 loss 0.0856 nce 0.0853 mse 0.00032 acc 1.000 lr 2.43e-05 L32 337m\n", " e4 54,900/62,312 loss 0.1216 nce 0.1212 mse 0.00039 acc 1.000 lr 2.40e-05 L53 337m\n", " e4 54,950/62,312 loss 0.1134 nce 0.1131 mse 0.00037 acc 1.000 lr 2.37e-05 L49 338m\n", " e4 55,000/62,312 loss 0.0788 nce 0.0785 mse 0.00031 acc 1.000 lr 2.34e-05 L39 338m\n", " VAL r1 0.9980 cos 0.8416 self_cos +0.0031 erank 103.2 cv 0.0719 | frame r1 0.9985\n", " e4 55,050/62,312 loss 0.1022 nce 0.1018 mse 0.00042 acc 0.999 lr 2.31e-05 L18 338m\n", " e4 55,100/62,312 loss 0.2752 nce 0.2746 mse 0.00060 acc 1.000 lr 2.28e-05 L148 338m\n", " e4 55,150/62,312 loss 0.1803 nce 0.1798 mse 0.00050 acc 0.996 lr 2.26e-05 L14 339m\n", " e4 55,200/62,312 loss 0.1828 nce 0.1823 mse 0.00050 acc 1.000 lr 2.23e-05 L100 339m\n", " e4 55,250/62,312 loss 0.0882 nce 0.0878 mse 0.00032 acc 1.000 lr 2.20e-05 L28 339m\n", " e4 55,300/62,312 loss 0.0853 nce 0.0850 mse 0.00032 acc 1.000 lr 2.17e-05 L32 340m\n", " e4 55,350/62,312 loss 0.1255 nce 0.1250 mse 0.00046 acc 0.998 lr 2.14e-05 L16 340m\n", " e4 55,400/62,312 loss 0.0802 nce 0.0799 mse 0.00032 acc 1.000 lr 2.11e-05 L29 340m\n", " e4 55,450/62,312 loss 0.2208 nce 0.2202 mse 0.00055 acc 1.000 lr 2.08e-05 L122 341m\n", " e4 55,500/62,312 loss 0.1185 nce 0.1181 mse 0.00041 acc 1.000 lr 2.06e-05 L64 341m\n", " e4 55,550/62,312 loss 0.0905 nce 0.0902 mse 0.00033 acc 1.000 lr 2.03e-05 L26 341m\n", " e4 55,600/62,312 loss 0.1127 nce 0.1123 mse 0.00038 acc 1.000 lr 2.00e-05 L53 342m\n", " e4 55,650/62,312 loss 0.0789 nce 0.0786 mse 0.00031 acc 1.000 lr 1.97e-05 L39 342m\n", " e4 55,700/62,312 loss 0.0874 nce 0.0870 mse 0.00032 acc 1.000 lr 1.95e-05 L30 342m\n", " e4 55,750/62,312 loss 0.0734 nce 0.0731 mse 0.00032 acc 1.000 lr 1.92e-05 L42 342m\n", " e4 55,800/62,312 loss 0.0804 nce 0.0801 mse 0.00032 acc 1.000 lr 1.89e-05 L30 343m\n", " e4 55,850/62,312 loss 0.0875 nce 0.0872 mse 0.00035 acc 0.999 lr 1.87e-05 L23 343m\n", " e4 55,900/62,312 loss 0.0977 nce 0.0973 mse 0.00041 acc 0.999 lr 1.84e-05 L18 343m\n", " e4 55,950/62,312 loss 0.0868 nce 0.0864 mse 0.00037 acc 1.000 lr 1.82e-05 L21 343m\n", " e4 56,000/62,312 loss 0.0792 nce 0.0789 mse 0.00032 acc 1.000 lr 1.79e-05 L29 344m\n", " VAL r1 0.9975 cos 0.8422 self_cos +0.0032 erank 103.1 cv 0.0783 | frame r1 0.9985\n", " e4 56,050/62,312 loss 0.0808 nce 0.0805 mse 0.00035 acc 0.998 lr 1.76e-05 L23 344m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (step 56060)\n", " e4 56,100/62,312 loss 0.2275 nce 0.2270 mse 0.00055 acc 1.000 lr 1.74e-05 L116 345m\n", " e4 56,150/62,312 loss 0.2054 nce 0.2049 mse 0.00053 acc 1.000 lr 1.71e-05 L111 345m\n", " e4 56,200/62,312 loss 0.3030 nce 0.3024 mse 0.00062 acc 1.000 lr 1.69e-05 L170 345m\n", " e4 56,250/62,312 loss 0.1469 nce 0.1465 mse 0.00048 acc 0.998 lr 1.66e-05 L15 346m\n", " e4 56,300/62,312 loss 0.0732 nce 0.0729 mse 0.00031 acc 1.000 lr 1.64e-05 L41 346m\n", " e4 56,350/62,312 loss 0.0991 nce 0.0987 mse 0.00042 acc 0.999 lr 1.61e-05 L18 346m\n", " e4 56,400/62,312 loss 0.0895 nce 0.0891 mse 0.00034 acc 1.000 lr 1.59e-05 L24 347m\n", " e4 56,450/62,312 loss 0.0811 nce 0.0807 mse 0.00032 acc 0.999 lr 1.57e-05 L28 347m\n", " e4 56,500/62,312 loss 0.0852 nce 0.0849 mse 0.00032 acc 1.000 lr 1.54e-05 L32 347m\n", " e4 56,550/62,312 loss 0.0887 nce 0.0884 mse 0.00032 acc 1.000 lr 1.52e-05 L31 347m\n", " e4 56,600/62,312 loss 0.1226 nce 0.1222 mse 0.00040 acc 1.000 lr 1.49e-05 L58 348m\n", " e4 56,650/62,312 loss 0.2400 nce 0.2394 mse 0.00058 acc 0.972 lr 1.47e-05 L11 348m\n", " e4 56,700/62,312 loss 0.1211 nce 0.1207 mse 0.00046 acc 0.998 lr 1.45e-05 L16 348m\n", " e4 56,750/62,312 loss 0.0848 nce 0.0844 mse 0.00038 acc 0.999 lr 1.43e-05 L20 349m\n", " e4 56,800/62,312 loss 0.0827 nce 0.0823 mse 0.00032 acc 1.000 lr 1.40e-05 L30 349m\n", " e4 56,850/62,312 loss 0.0866 nce 0.0863 mse 0.00032 acc 0.999 lr 1.38e-05 L34 349m\n", " e4 56,900/62,312 loss 0.1791 nce 0.1786 mse 0.00050 acc 0.996 lr 1.36e-05 L14 349m\n", " e4 56,950/62,312 loss 0.1165 nce 0.1161 mse 0.00041 acc 0.999 lr 1.34e-05 L69 350m\n", " e4 57,000/62,312 loss 0.1195 nce 0.1191 mse 0.00039 acc 0.999 lr 1.31e-05 L53 350m\n", " VAL r1 0.9980 cos 0.8421 self_cos +0.0029 erank 102.9 cv 0.0806 | frame r1 0.9985\n", " e4 57,050/62,312 loss 0.1317 nce 0.1313 mse 0.00043 acc 1.000 lr 1.29e-05 L83 350m\n", " e4 57,100/62,312 loss 0.1826 nce 0.1821 mse 0.00050 acc 1.000 lr 1.27e-05 L100 351m\n", " e4 57,150/62,312 loss 0.0833 nce 0.0829 mse 0.00032 acc 1.000 lr 1.25e-05 L30 351m\n", " e4 57,200/62,312 loss 0.0874 nce 0.0871 mse 0.00032 acc 1.000 lr 1.23e-05 L32 351m\n", " e4 57,250/62,312 loss 0.0877 nce 0.0874 mse 0.00037 acc 0.999 lr 1.21e-05 L21 352m\n", " e4 57,300/62,312 loss 0.0821 nce 0.0818 mse 0.00032 acc 1.000 lr 1.19e-05 L30 352m\n", " e4 57,350/62,312 loss 0.1337 nce 0.1332 mse 0.00043 acc 1.000 lr 1.16e-05 L83 352m\n", " e4 57,400/62,312 loss 0.3761 nce 0.3754 mse 0.00069 acc 1.000 lr 1.14e-05 L256 353m\n", " e4 57,450/62,312 loss 0.0930 nce 0.0926 mse 0.00039 acc 0.999 lr 1.12e-05 L19 353m\n", " e4 57,500/62,312 loss 0.0979 nce 0.0975 mse 0.00040 acc 0.999 lr 1.10e-05 L19 353m\n", " e4 57,550/62,312 loss 0.2100 nce 0.2095 mse 0.00053 acc 1.000 lr 1.08e-05 L110 353m\n", " e4 57,600/62,312 loss 0.0863 nce 0.0859 mse 0.00035 acc 1.000 lr 1.06e-05 L23 354m\n", " e4 57,650/62,312 loss 0.0848 nce 0.0845 mse 0.00032 acc 1.000 lr 1.04e-05 L32 354m\n", " e4 57,700/62,312 loss 0.1953 nce 0.1948 mse 0.00051 acc 1.000 lr 1.03e-05 L105 354m\n", " e4 57,750/62,312 loss 0.0841 nce 0.0837 mse 0.00032 acc 1.000 lr 1.01e-05 L31 355m\n", " e4 57,800/62,312 loss 0.0871 nce 0.0868 mse 0.00032 acc 1.000 lr 9.88e-06 L31 355m\n", " e4 57,850/62,312 loss 0.1933 nce 0.1928 mse 0.00051 acc 1.000 lr 9.69e-06 L104 355m\n", " e4 57,900/62,312 loss 0.5906 nce 0.5899 mse 0.00071 acc 0.905 lr 9.50e-06 L9 355m\n", " e4 57,950/62,312 loss 0.0904 nce 0.0901 mse 0.00032 acc 1.000 lr 9.32e-06 L32 356m\n", " e4 58,000/62,312 loss 0.0834 nce 0.0830 mse 0.00033 acc 1.000 lr 9.14e-06 L45 356m\n", " VAL r1 0.9980 cos 0.8419 self_cos +0.0030 erank 103.1 cv 0.0784 | frame r1 0.9985\n", " e4 58,050/62,312 loss 0.0835 nce 0.0832 mse 0.00032 acc 1.000 lr 8.96e-06 L29 356m\n", " e4 58,100/62,312 loss 0.3854 nce 0.3847 mse 0.00069 acc 1.000 lr 8.78e-06 L256 357m\n", " e4 58,150/62,312 loss 0.0872 nce 0.0868 mse 0.00031 acc 1.000 lr 8.61e-06 L39 357m\n", " e4 58,200/62,312 loss 0.1528 nce 0.1523 mse 0.00048 acc 0.998 lr 8.44e-06 L15 357m\n", " e4 58,250/62,312 loss 0.0886 nce 0.0883 mse 0.00032 acc 1.000 lr 8.26e-06 L29 357m\n", " e4 58,300/62,312 loss 0.1186 nce 0.1182 mse 0.00039 acc 1.000 lr 8.09e-06 L53 358m\n", " e4 58,350/62,312 loss 0.1223 nce 0.1219 mse 0.00041 acc 0.998 lr 7.93e-06 L64 358m\n", " e4 58,400/62,312 loss 0.0812 nce 0.0809 mse 0.00031 acc 1.000 lr 7.76e-06 L38 358m\n", " e4 58,450/62,312 loss 0.1197 nce 0.1193 mse 0.00041 acc 1.000 lr 7.60e-06 L64 359m\n", " e4 58,500/62,312 loss 0.2342 nce 0.2337 mse 0.00056 acc 1.000 lr 7.44e-06 L122 359m\n", " e4 58,550/62,312 loss 0.0843 nce 0.0840 mse 0.00032 acc 0.999 lr 7.28e-06 L27 359m\n", " e4 58,600/62,312 loss 0.0798 nce 0.0795 mse 0.00032 acc 1.000 lr 7.12e-06 L28 359m\n", " e4 58,650/62,312 loss 0.0792 nce 0.0788 mse 0.00032 acc 1.000 lr 6.97e-06 L41 360m\n", " e4 58,700/62,312 loss 0.0799 nce 0.0796 mse 0.00035 acc 0.999 lr 6.81e-06 L22 360m\n", " e4 58,750/62,312 loss 0.2187 nce 0.2181 mse 0.00055 acc 1.000 lr 6.66e-06 L116 360m\n", " e4 58,800/62,312 loss 0.0872 nce 0.0869 mse 0.00033 acc 1.000 lr 6.51e-06 L25 361m\n", " e4 58,850/62,312 loss 0.2393 nce 0.2388 mse 0.00057 acc 1.000 lr 6.36e-06 L128 361m\n", " e4 58,900/62,312 loss 0.0911 nce 0.0907 mse 0.00032 acc 1.000 lr 6.22e-06 L33 361m\n", " e4 58,950/62,312 loss 0.0981 nce 0.0977 mse 0.00041 acc 1.000 lr 6.07e-06 L18 361m\n", " e4 59,000/62,312 loss 0.0909 nce 0.0906 mse 0.00032 acc 1.000 lr 5.93e-06 L32 362m\n", " VAL r1 0.9980 cos 0.8420 self_cos +0.0029 erank 103.2 cv 0.0710 | frame r1 0.9985\n", " e4 59,050/62,312 loss 0.0850 nce 0.0846 mse 0.00034 acc 1.000 lr 5.79e-06 L24 362m\n", " e4 59,100/62,312 loss 0.1166 nce 0.1163 mse 0.00039 acc 0.999 lr 5.66e-06 L53 362m\n", " e4 59,150/62,312 loss 0.1215 nce 0.1211 mse 0.00039 acc 0.998 lr 5.52e-06 L53 363m\n", " e4 59,200/62,312 loss 0.1497 nce 0.1492 mse 0.00045 acc 1.000 lr 5.39e-06 L88 363m\n", " e4 59,250/62,312 loss 0.0914 nce 0.0911 mse 0.00035 acc 0.999 lr 5.25e-06 L23 363m\n", " e4 59,300/62,312 loss 0.0839 nce 0.0836 mse 0.00034 acc 0.999 lr 5.13e-06 L24 364m\n", " e4 59,350/62,312 loss 0.0882 nce 0.0879 mse 0.00034 acc 0.999 lr 5.00e-06 L24 364m\n", " e4 59,400/62,312 loss 0.0824 nce 0.0821 mse 0.00031 acc 1.000 lr 4.87e-06 L35 364m\n", " e4 59,450/62,312 loss 0.0836 nce 0.0833 mse 0.00035 acc 1.000 lr 4.75e-06 L23 364m\n", " e4 59,500/62,312 loss 0.3921 nce 0.3914 mse 0.00069 acc 1.000 lr 4.63e-06 L256 365m\n", " e4 59,550/62,312 loss 0.0788 nce 0.0785 mse 0.00031 acc 1.000 lr 4.51e-06 L41 365m\n", " e4 59,600/62,312 loss 0.0862 nce 0.0858 mse 0.00032 acc 1.000 lr 4.39e-06 L30 366m\n", " e4 59,650/62,312 loss 0.1184 nce 0.1180 mse 0.00042 acc 1.000 lr 4.27e-06 L76 366m\n", " e4 59,700/62,312 loss 0.0872 nce 0.0869 mse 0.00033 acc 1.000 lr 4.16e-06 L45 366m\n", " e4 59,750/62,312 loss 0.1912 nce 0.1906 mse 0.00054 acc 0.990 lr 4.05e-06 L12 366m\n", " e4 59,800/62,312 loss 0.1578 nce 0.1573 mse 0.00046 acc 1.000 lr 3.94e-06 L88 367m\n", " e4 59,850/62,312 loss 0.2715 nce 0.2709 mse 0.00059 acc 0.963 lr 3.83e-06 L11 367m\n", " e4 59,900/62,312 loss 0.2305 nce 0.2299 mse 0.00058 acc 0.975 lr 3.73e-06 L11 367m\n", " e4 59,950/62,312 loss 0.0809 nce 0.0806 mse 0.00032 acc 1.000 lr 3.62e-06 L40 368m\n", " e4 60,000/62,312 loss 0.0841 nce 0.0838 mse 0.00032 acc 1.000 lr 3.52e-06 L30 368m\n", " VAL r1 0.9980 cos 0.8420 self_cos +0.0029 erank 103.1 cv 0.0754 | frame r1 0.9985\n", " e4 60,050/62,312 loss 0.2128 nce 0.2122 mse 0.00053 acc 1.000 lr 3.42e-06 L110 368m\n", " e4 60,100/62,312 loss 0.0846 nce 0.0843 mse 0.00031 acc 1.000 lr 3.32e-06 L37 369m\n", " e4 60,150/62,312 loss 0.1321 nce 0.1317 mse 0.00043 acc 1.000 lr 3.23e-06 L83 369m\n", " e4 60,200/62,312 loss 0.4775 nce 0.4767 mse 0.00077 acc 0.964 lr 3.13e-06 L8 369m\n", " e4 60,250/62,312 loss 0.0819 nce 0.0815 mse 0.00032 acc 1.000 lr 3.04e-06 L31 370m\n", " e4 60,300/62,312 loss 0.0826 nce 0.0823 mse 0.00032 acc 1.000 lr 2.95e-06 L29 370m\n", " e4 60,350/62,312 loss 0.2400 nce 0.2394 mse 0.00056 acc 1.000 lr 2.86e-06 L128 370m\n", " e4 60,400/62,312 loss 0.1212 nce 0.1208 mse 0.00045 acc 0.998 lr 2.78e-06 L16 371m\n", " e4 60,450/62,312 loss 0.0962 nce 0.0958 mse 0.00041 acc 0.999 lr 2.70e-06 L18 371m\n", " e4 60,500/62,312 loss 0.0846 nce 0.0843 mse 0.00032 acc 1.000 lr 2.61e-06 L27 371m\n", " e4 60,550/62,312 loss 0.0802 nce 0.0799 mse 0.00032 acc 1.000 lr 2.53e-06 L27 371m\n", " e4 60,600/62,312 loss 0.1687 nce 0.1682 mse 0.00050 acc 0.999 lr 2.46e-06 L14 372m\n", " e4 60,650/62,312 loss 0.1425 nce 0.1420 mse 0.00048 acc 0.997 lr 2.38e-06 L15 372m\n", " e4 60,700/62,312 loss 0.0884 nce 0.0880 mse 0.00032 acc 1.000 lr 2.31e-06 L33 372m\n", " e4 60,750/62,312 loss 0.0857 nce 0.0854 mse 0.00032 acc 1.000 lr 2.24e-06 L38 373m\n", " e4 60,800/62,312 loss 0.1036 nce 0.1031 mse 0.00042 acc 0.998 lr 2.17e-06 L18 373m\n", " e4 60,850/62,312 loss 0.1071 nce 0.1067 mse 0.00043 acc 0.999 lr 2.10e-06 L17 373m\n", " e4 60,900/62,312 loss 0.0774 nce 0.0771 mse 0.00031 acc 1.000 lr 2.03e-06 L41 373m\n", " e4 60,950/62,312 loss 0.1787 nce 0.1782 mse 0.00050 acc 1.000 lr 1.97e-06 L99 374m\n", " e4 61,000/62,312 loss 0.3786 nce 0.3779 mse 0.00069 acc 1.000 lr 1.91e-06 L256 374m\n", " VAL r1 0.9980 cos 0.8420 self_cos +0.0030 erank 103.2 cv 0.0664 | frame r1 0.9985\n", " e4 61,050/62,312 loss 0.0837 nce 0.0834 mse 0.00032 acc 1.000 lr 1.85e-06 L29 374m\n", " e4 61,100/62,312 loss 0.0882 nce 0.0878 mse 0.00032 acc 1.000 lr 1.79e-06 L35 375m\n", " e4 61,150/62,312 loss 0.0806 nce 0.0803 mse 0.00032 acc 1.000 lr 1.74e-06 L34 375m\n", " e4 61,200/62,312 loss 0.0828 nce 0.0825 mse 0.00032 acc 1.000 lr 1.68e-06 L29 375m\n", " e4 61,250/62,312 loss 0.0784 nce 0.0781 mse 0.00032 acc 1.000 lr 1.63e-06 L27 376m\n", " e4 61,300/62,312 loss 0.0819 nce 0.0816 mse 0.00032 acc 1.000 lr 1.58e-06 L30 376m\n", " e4 61,350/62,312 loss 0.1163 nce 0.1159 mse 0.00044 acc 0.999 lr 1.53e-06 L17 376m\n", " e4 61,400/62,312 loss 0.0857 nce 0.0853 mse 0.00036 acc 1.000 lr 1.49e-06 L22 377m\n", " e4 61,450/62,312 loss 0.3770 nce 0.3763 mse 0.00069 acc 1.000 lr 1.45e-06 L256 377m\n", " e4 61,500/62,312 loss 0.4418 nce 0.4410 mse 0.00077 acc 0.976 lr 1.40e-06 L8 377m\n", " e4 61,550/62,312 loss 0.0857 nce 0.0854 mse 0.00032 acc 1.000 lr 1.36e-06 L35 378m\n", " e4 61,600/62,312 loss 0.0805 nce 0.0802 mse 0.00032 acc 1.000 lr 1.33e-06 L28 378m\n", " e4 61,650/62,312 loss 0.0758 nce 0.0755 mse 0.00031 acc 1.000 lr 1.29e-06 L41 378m\n", " e4 61,700/62,312 loss 0.0852 nce 0.0848 mse 0.00032 acc 1.000 lr 1.26e-06 L38 378m\n", " e4 61,750/62,312 loss 0.0865 nce 0.0862 mse 0.00035 acc 1.000 lr 1.23e-06 L23 379m\n", " e4 61,800/62,312 loss 0.0817 nce 0.0814 mse 0.00032 acc 1.000 lr 1.20e-06 L42 379m\n", " e4 61,850/62,312 loss 0.2216 nce 0.2210 mse 0.00055 acc 1.000 lr 1.17e-06 L121 379m\n", " e4 61,900/62,312 loss 0.0888 nce 0.0885 mse 0.00032 acc 1.000 lr 1.15e-06 L35 380m\n", " e4 61,950/62,312 loss 0.0754 nce 0.0751 mse 0.00031 acc 1.000 lr 1.12e-06 L39 380m\n", " e4 62,000/62,312 loss 0.0852 nce 0.0848 mse 0.00032 acc 1.000 lr 1.10e-06 L34 380m\n", " VAL r1 0.9980 cos 0.8421 self_cos +0.0029 erank 103.1 cv 0.0716 | frame r1 0.9985\n", " e4 62,050/62,312 loss 0.0764 nce 0.0761 mse 0.00032 acc 0.999 lr 1.08e-06 L43 381m\n", " e4 62,100/62,312 loss 0.1197 nce 0.1193 mse 0.00040 acc 0.999 lr 1.06e-06 L58 381m\n", " e4 62,150/62,312 loss 0.0922 nce 0.0918 mse 0.00038 acc 0.998 lr 1.05e-06 L20 381m\n", " e4 62,200/62,312 loss 0.2020 nce 0.2015 mse 0.00052 acc 1.000 lr 1.04e-06 L106 382m\n", " e4 62,250/62,312 loss 0.2024 nce 0.2019 mse 0.00053 acc 0.996 lr 1.03e-06 L13 382m\n", " e4 62,300/62,312 loss 0.1917 nce 0.1911 mse 0.00055 acc 0.990 lr 1.02e-06 L12 382m\n", "── FINAL ─────────────────────────────────────────────────────────────────────\n", " mimicry R@1 (student->consensus, NOT capability): 0.9980\n", " cos to target : 0.8421\n", " self_cos : +0.0030 <- isotropy; teachers .81-.98\n", " effective rank: 103.1/768\n", " CV : 0.0660\n", " frame-fit R@1 : 0.9985 (should be ~mimicry: reference-member alignment pins the frame)\n", " CAPABILITY is decided by STS-B / SICK vs the five teachers, not here.\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n", "No files have been modified since last commit. Skipping to prevent empty commit.\n", "WARNING:huggingface_hub._upload_pipeline:No files have been modified since last commit. Skipping to prevent empty commit.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " [backup] pushed (final)\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT TRUNK EVAL -- v2 vs -b, full suite, geometry, and arm transfer\n", "#\n", "# Single standalone Colab cell. Answers three questions in one pass:\n", "#\n", "# 1. DID 19% MORE DATA BUY ANYTHING? -b trained on the repaired 66-chunk\n", "# corpus (31.9M rows, 62,312 steps) against v2's 54 chunks (26.9M rows,\n", "# 52,548 steps). Their TRAINING metrics are nearly identical --\n", "# cos .8421 vs .8410, erank 103.1 vs 102.9 -- but training metrics are\n", "# MIMICRY. This runs the capability gauges.\n", "#\n", "# 2. WHERE IS THE CEILING? The consensus target uses 28.7 of 768 directions.\n", "# If -b lands on v2 across all eight tasks, the limit is TEACHER AGREEMENT\n", "# and not corpus size, and the next experiment is heterogeneous teachers\n", "# rather than more data. A null here is a RESULT, not a wasted run.\n", "#\n", "# 3. DO THE ARMS TRANSFER? The AMOE anchors were trained against v2's\n", "# residual stream and the dispatch keys were fit to v2's geometry.\n", "# Attaching them to -b unchanged is untested. This measures it, and says\n", "# plainly that a re-alignment (800 keys-only steps, ~4 min) is the honest\n", "# path if transfer is poor.\n", "#\n", "# EIGHT TASKS, not two: STS-B, SICK-R, STS12-16, BIOSSES. BIOSSES is 100 rows\n", "# of biomedical text and is the only genuine out-of-domain read in the set --\n", "# everything else is English web/news/caption, in-distribution for a CC12M\n", "# trunk. Watch that column when comparing trunks.\n", "#\n", "# Config at the top, functionality in the body, run logic at the base.\n", "# ============================================================================\n", "\n", "import json\n", "import os\n", "import subprocess\n", "import sys\n", "from dataclasses import dataclass, asdict, field\n", "from typing import Dict, List, Optional, Tuple\n", "\n", "for _p, _i in [(\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\"), (\"huggingface_hub\", \"huggingface_hub\"),\n", " (\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer, AutoModel\n", "from datasets import load_dataset\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " # ---- what to compare ----\n", " # (label, repo, checkpoint|None). None -> load as an AutoModel from the\n", " # repo root. A path -> load raw weights into the local class.\n", " trunks: tuple = (\n", " (\"v2 (54ch, 26.9M rows)\", \"AbstractPhil/captionbert-8192-v2\",\n", " \"checkpoints/best_model.pt\"),\n", " (\"b (66ch, 31.9M rows)\", \"AbstractPhil/captionbert-8192-v2-B\",\n", " \"checkpoints/final_model.pt\"),\n", " )\n", " # measured in one harness on 2026-08-01; bert-base reproduced the published\n", " # .4729073 to 7 digits, which is what makes these comparable.\n", " baselines: tuple = (\n", " (\"bert-base\", \"google-bert/bert-base-uncased\"),\n", " (\"all-MiniLM-L6-v2\", \"sentence-transformers/all-MiniLM-L6-v2\"),\n", " )\n", " run_baselines: bool = False # they do not move; True for a fresh table\n", "\n", " # ---- arms ----\n", " test_arms: bool = True\n", " arms_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " arms_dispatch: str = \"amoe/collective/captionbert-v2-collective.dispatch.pt\"\n", "\n", " # ---- tasks ----\n", " tasks: tuple = (\n", " (\"STS-B\", \"mteb/stsbenchmark-sts\"),\n", " (\"SICK-R\", \"mteb/sickr-sts\"),\n", " (\"STS12\", \"mteb/sts12-sts\"),\n", " (\"STS13\", \"mteb/sts13-sts\"),\n", " (\"STS14\", \"mteb/sts14-sts\"),\n", " (\"STS15\", \"mteb/sts15-sts\"),\n", " (\"STS16\", \"mteb/sts16-sts\"),\n", " (\"BIOSSES\", \"mteb/biosses-sts\"),\n", " )\n", "\n", " # ---- architecture (must match the checkpoints) ----\n", " vocab_size: int = 30522\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " output_dim: int = 768\n", " max_len: int = 8192\n", " pooling: str = \"mean\"\n", "\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " batch_size: int = 256\n", " max_tokens: int = 64\n", " geom_n: int = 2000 # matched to the trainer's eval cap\n", " cv_samples: int = 300\n", " seed: int = 0\n", "\n", " out_json: str = \"trunk_eval.json\"\n", " hf_push: bool = False\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2-B\"\n", " hf_path: str = \"eval\"\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 78 if not t else f\"-- {t} \" + \"-\" * max(0, 74 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " \"\"\"Key-compatible with every captionbert-v2-family checkpoint.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " super().__init__()\n", " d = cfg.d_model\n", " self.pad_token_id, self.pooling = 0, cfg.pooling\n", " self.token_emb = nn.Embedding(cfg.vocab_size, d, padding_idx=0)\n", " self.pos_emb = nn.Embedding(cfg.max_len, d)\n", " self.emb_norm = nn.LayerNorm(d)\n", " self.emb_drop = nn.Dropout(0.1)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d, nhead=cfg.n_heads, dim_feedforward=cfg.d_ff, dropout=0.1,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=cfg.n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d, d), nn.GELU(), nn.LayerNorm(d), nn.Linear(d, cfg.output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).to(x.dtype) if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).to(x.dtype))\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "def load_trunk(cfg, repo, ckpt):\n", " m = CaptionEncoder(cfg)\n", " p = hf_hub_download(repo, ckpt)\n", " sd = torch.load(p, weights_only=True, map_location=\"cpu\")\n", " missing, unexpected = m.load_state_dict(sd, strict=True)\n", " n = sum(q.numel() for q in m.parameters())\n", " print(f\" {repo}/{ckpt}\")\n", " print(f\" {n:,} params | strict load OK\")\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "def cayley_menger_vol2(pts):\n", " pts = pts.float()\n", " d = pts.unsqueeze(-2) - pts.unsqueeze(-3)\n", " d2 = (d * d).sum(-1)\n", " B, V, _ = d2.shape\n", " cm = torch.zeros(B, V + 1, V + 1, device=d2.device, dtype=torch.float64)\n", " cm[:, 0, 1:] = 1; cm[:, 1:, 0] = 1; cm[:, 1:, 1:] = d2.double()\n", " import math\n", " f = math.factorial(V - 1)\n", " return ((-1.0) ** V) / ((2.0 ** (V - 1)) * f * f) * torch.linalg.det(cm)\n", "\n", "\n", "@torch.no_grad()\n", "def pentachoron_cv(emb, n=300, seed=0):\n", " if emb.shape[0] < 5:\n", " return 0.0\n", " g = torch.Generator().manual_seed(seed)\n", " v = []\n", " for _ in range(n):\n", " idx = torch.randperm(emb.shape[0], generator=g)[:5]\n", " val = float(torch.sqrt(F.relu(cayley_menger_vol2(emb[idx].unsqueeze(0))[0])).item())\n", " if val > 0:\n", " v.append(val)\n", " a = np.array(v)\n", " return float(a.std() / a.mean()) if len(a) >= 10 else 0.0\n", "\n", "\n", "@torch.no_grad()\n", "def encode_trunk(model, tok, texts, cfg):\n", " out = []\n", " for i in range(0, len(texts), cfg.batch_size):\n", " t = tok(list(texts[i:i + cfg.batch_size]), max_length=cfg.max_tokens,\n", " padding=True, truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out.append(model(t[\"input_ids\"], t[\"attention_mask\"]).float().cpu())\n", " return torch.cat(out)\n", "\n", "\n", "@torch.no_grad()\n", "def encode_hf(model, tok, texts, cfg):\n", " out = []\n", " for i in range(0, len(texts), cfg.batch_size):\n", " t = tok(list(texts[i:i + cfg.batch_size]), max_length=cfg.max_tokens,\n", " padding=True, truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h = model(**t).last_hidden_state\n", " m = t[\"attention_mask\"].unsqueeze(-1).float()\n", " out.append(F.normalize((h * m).sum(1) / m.sum(1).clamp(min=1), dim=-1).float().cpu())\n", " return torch.cat(out)\n", "\n", "\n", "def score_task(enc_fn, task, cfg):\n", " a, b, g = task\n", " ea, eb = enc_fn(a), enc_fn(b)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb])\n", " n = min(cfg.geom_n, E.shape[0])\n", " S = E[:n] @ E[:n].T\n", " S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n]),\n", " \"cv\": pentachoron_cv(E[:n], cfg.cv_samples, cfg.seed),\n", " \"n\": int(len(g))}\n", "\n", "\n", "def load_tasks(cfg):\n", " out = {}\n", " for nm, path in cfg.tasks:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " c = d.column_names\n", " a = \"sentence1\" if \"sentence1\" in c else c[0]\n", " b = \"sentence2\" if \"sentence2\" in c else c[1]\n", " sc = \"score\" if \"score\" in c else \"similarity_score\"\n", " out[nm] = (list(d[a]), list(d[b]), np.asarray(d[sc], dtype=float))\n", " print(f\" {nm:8s} {len(out[nm][2]):>6,d} pairs\")\n", " except Exception as e:\n", " print(f\" {nm:8s} SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def table(rows, tasks, title, extra=None):\n", " \"\"\"rows: {label: {task: metrics}}\"\"\"\n", " tk = list(tasks)\n", " line(title)\n", " print(f\" {'model':26s}\" + \"\".join(f\"{t:>9s}\" for t in tk) + f\"{'mean':>9s}\")\n", " for label, r in rows.items():\n", " vals = [r[t][\"spearman\"] for t in tk if t in r]\n", " print(f\" {label:26s}\" + \"\".join(f\"{r[t]['spearman']:>9.4f}\" for t in tk if t in r)\n", " + f\"{np.mean(vals):>9.4f}\")\n", " if extra:\n", " print()\n", " for e in extra:\n", " print(f\" {e}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 78)\n", " print(\"CAPTIONBERT TRUNK EVAL -- corpus scale, geometry, arm transfer\")\n", " print(\"=\" * 78)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " torch.manual_seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks(cfg)\n", " if not tasks:\n", " raise RuntimeError(\"no tasks loaded\")\n", "\n", " results, geom = {}, {}\n", "\n", " # ---- trunks ----\n", " for label, repo, ckpt in cfg.trunks:\n", " line(f\"TRUNK {label}\")\n", " model = load_trunk(cfg, repo, ckpt)\n", " r = {k: score_task(lambda t: encode_trunk(model, tok, t, cfg), v, cfg)\n", " for k, v in tasks.items()}\n", " results[label] = r\n", " ref = list(tasks)[0]\n", " geom[label] = {\"self_cos\": r[ref][\"self_cos\"], \"erank\": r[ref][\"erank\"],\n", " \"cv\": r[ref][\"cv\"]}\n", " print(f\" {ref}: rho {r[ref]['spearman']:.4f} self_cos \"\n", " f\"{r[ref]['self_cos']:+.4f} erank {r[ref]['erank']:.1f} \"\n", " f\"cv {r[ref]['cv']:.4f}\")\n", " del model\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- baselines ----\n", " if cfg.run_baselines:\n", " for label, name in cfg.baselines:\n", " line(f\"BASELINE {label}\")\n", " bm = AutoModel.from_pretrained(name).to(DEVICE).eval()\n", " bt = AutoTokenizer.from_pretrained(name)\n", " results[label] = {k: score_task(lambda t: encode_hf(bm, bt, t, cfg), v, cfg)\n", " for k, v in tasks.items()}\n", " del bm\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " table(results, tasks, \"TRUNK COMPARISON\")\n", "\n", " # ---- the corpus-scale delta ----\n", " if len(cfg.trunks) >= 2:\n", " la, lb = cfg.trunks[0][0], cfg.trunks[1][0]\n", " line(\"DID MORE DATA BUY ANYTHING?\")\n", " print(f\" {'task':10s}{la:>26s}{lb:>26s}{'delta':>10s}\")\n", " deltas = []\n", " for t in tasks:\n", " x, y = results[la][t][\"spearman\"], results[lb][t][\"spearman\"]\n", " deltas.append(y - x)\n", " print(f\" {t:10s}{x:>26.4f}{y:>26.4f}{y-x:>+10.4f}\")\n", " md = float(np.mean(deltas))\n", " print(f\" {'MEAN':10s}{np.mean([results[la][t]['spearman'] for t in tasks]):>26.4f}\"\n", " f\"{np.mean([results[lb][t]['spearman'] for t in tasks]):>26.4f}{md:>+10.4f}\")\n", " print()\n", " print(f\" -b trained on 19% more rows for 19% more steps.\")\n", " if abs(md) < 0.005:\n", " print(f\" => NULL ({md:+.4f} mean). The ceiling is TEACHER AGREEMENT, not\")\n", " print(f\" corpus size: the consensus target uses 28.7 of 768 directions\")\n", " print(f\" and more of the same distribution cannot raise it. The next\")\n", " print(f\" experiment is HETEROGENEOUS TEACHERS, measurable at stage 2\")\n", " print(f\" before a single training step. This is a result, not a waste.\")\n", " elif md > 0:\n", " print(f\" => +{md:.4f} mean. Corpus size still buys capability; the 28.7\")\n", " print(f\" target rank is not yet the binding constraint.\")\n", " else:\n", " print(f\" => {md:+.4f} mean. More data made it WORSE -- check whether the\")\n", " print(f\" repaired chunks differ from the rest in a way the gate missed.\")\n", "\n", " # ---- geometry ----\n", " line(\"GEOMETRY (on the first task)\")\n", " print(f\" {'model':26s}{'self_cos':>11s}{'erank':>9s}{'cv':>9s}\")\n", " for k, v in geom.items():\n", " print(f\" {k:26s}{v['self_cos']:>+11.4f}{v['erank']:>9.1f}{v['cv']:>9.4f}\")\n", " print()\n", " print(\" reference points measured 2026-08-01:\")\n", " print(\" consensus TARGET erank 28.7/768 trunk IN-DOMAIN erank ~103\")\n", " print(\" bert-base self_cos +.580 erank 32.0 all-MiniLM +.023 erank 94.3\")\n", " print(\" in-domain erank ~103 against ~35 here is the transfer gap: the extra\")\n", " print(\" directions the trunk builds on captions do not survive a domain shift.\")\n", "\n", " # ---- arm transfer ----\n", " if cfg.test_arms:\n", " line(\"ARM TRANSFER\")\n", " try:\n", " import amoe # noqa: F401\n", " except ImportError:\n", " print(\" amoe-lora not installed -- skipping.\")\n", " print(\" pip install git+https://github.com/AbstractEyes/amoe-lora\")\n", " else:\n", " from transformers import AutoModel as AM\n", " arm_rows, per_arm = {}, {}\n", " for label, repo, ckpt in cfg.trunks:\n", " try:\n", " am = AM.from_pretrained(cfg.arms_repo, trust_remote_code=True)\n", " sd = torch.load(hf_hub_download(repo, ckpt), weights_only=True,\n", " map_location=\"cpu\")\n", " am.load_state_dict(sd, strict=True)\n", " am = am.to(DEVICE).eval()\n", " am.attach_amoe(dispatch=cfg.arms_dispatch, repo=cfg.arms_repo)\n", " names = am.amoe_arms\n", " print(f\" {label}: attached {names}\")\n", "\n", " def enc(t):\n", " return am.encode(t, tokenizer=tok, batch_size=cfg.batch_size,\n", " max_length=cfg.max_tokens)\n", "\n", " # FULL MASK TABLE. Bare-vs-collective cannot tell \"the arms\n", " # transferred\" from \"one arm carries it and the rest are dead\n", " # weight on the new trunk\". Each mask is a separate question.\n", " masks = ([(\"OFF\", [])] + [(f\"{n}-only\", [n]) for n in names]\n", " + [(\"COLLECTIVE\", list(names))])\n", " for mlabel, sel in masks:\n", " am.set_amoe(sel if sel else [False] * len(names))\n", " row = {k: score_task(enc, v, cfg) for k, v in tasks.items()}\n", " per_arm[f\"{label} | {mlabel}\"] = row\n", " if mlabel == \"COLLECTIVE\":\n", " arm_rows[f\"{label} + arms\"] = row\n", " am.set_amoe(None)\n", " am.detach_amoe()\n", " del am\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " except Exception as e:\n", " print(f\" {label}: arms failed -- {type(e).__name__}: {str(e)[:70]}\")\n", "\n", " if per_arm:\n", " table(per_arm, tasks, \"PER-ARM MASKS (each arm inside the dispatch)\")\n", " print()\n", " print(\" NOTE: an `-only` row is that arm AS DAMPED BY THE DISPATCH.\")\n", " print(\" Masking never renormalizes, so it reads LOWER than the same\")\n", " print(\" anchor trained alone (simplify: .7400 solo, .6104 masked).\")\n", " print(\" Compare arms to each other here, not to their solo scores.\")\n", " if arm_rows:\n", " merged = {**{k: results[k] for k, _, _ in\n", " [(t[0], 0, 0) for t in cfg.trunks]}, **arm_rows}\n", " table(merged, tasks, \"WITH THE 3-ARM COLLECTIVE\")\n", " print()\n", " for label, _, _ in cfg.trunks:\n", " ak = f\"{label} + arms\"\n", " if ak in arm_rows:\n", " base = np.mean([results[label][t][\"spearman\"] for t in tasks])\n", " wa = np.mean([arm_rows[ak][t][\"spearman\"] for t in tasks])\n", " print(f\" {label:26s} bare {base:.4f} -> arms {wa:.4f} \"\n", " f\"({wa-base:+.4f})\")\n", " if len(cfg.trunks) >= 2 and per_arm:\n", " la, lb = cfg.trunks[0][0], cfg.trunks[1][0]\n", " line(\"PER-ARM TRANSFER\")\n", " print(f\" mean over {len(tasks)} tasks, {la} -> {lb}\")\n", " print(f\" {'mask':16s}{'v2':>10s}{'b':>10s}{'delta':>10s}\")\n", " keys = [k.split(\" | \")[1] for k in per_arm if k.startswith(la)]\n", " for mk in keys:\n", " ka, kb = f\"{la} | {mk}\", f\"{lb} | {mk}\"\n", " if ka in per_arm and kb in per_arm:\n", " x = np.mean([per_arm[ka][t][\"spearman\"] for t in tasks])\n", " y = np.mean([per_arm[kb][t][\"spearman\"] for t in tasks])\n", " print(f\" {mk:16s}{x:>10.4f}{y:>10.4f}{y-x:>+10.4f}\")\n", " print()\n", " print(\" A per-arm delta near zero means that anchor transfers; a\")\n", " print(\" large negative one means it was fit to v2's residual stream\")\n", " print(\" specifically. If the COLLECTIVE drops more than its members,\")\n", " print(\" the DISPATCH KEYS are what failed to transfer, not the\")\n", " print(\" anchors -- and that is the cheap fix: reuse the anchors,\")\n", " print(\" re-run 800 keys-only steps against the new trunk.\")\n", " print()\n", " print(\" The anchors were trained against v2's residual stream and the\")\n", " print(\" dispatch keys were fit to v2's geometry. If -b gains much less\")\n", " print(\" than v2 here, the arms did not transfer and the honest fix is\")\n", " print(\" a re-alignment: reuse the anchors, re-run the 800-step\")\n", " print(\" keys-only dispatch training against -b. ~4 minutes.\")\n", "\n", " # ---- ship ----\n", " out = {\"trunks\": {k: v for k, v in results.items()},\n", " \"geometry\": geom, \"config\": asdict(cfg)}\n", " try:\n", " out[\"per_arm\"] = per_arm\n", " except NameError:\n", " pass\n", " with open(cfg.out_json, \"w\") as f:\n", " json.dump(out, f, indent=2, default=float)\n", " print(f\"\\n wrote {cfg.out_json}\")\n", " if cfg.hf_push:\n", " tokn = os.environ.get(\"HF_TOKEN\")\n", " if not tokn:\n", " try:\n", " from google.colab import userdata\n", " tokn = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tokn = None\n", " if tokn:\n", " from huggingface_hub import HfApi\n", " HfApi(token=tokn).upload_file(\n", " path_or_fileobj=cfg.out_json,\n", " path_in_repo=f\"{cfg.hf_path}/{cfg.out_json}\",\n", " repo_id=cfg.hf_repo, commit_message=\"trunk eval\")\n", " print(f\" pushed -> {cfg.hf_repo}/{cfg.hf_path}/{cfg.out_json}\")\n", " return out\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "efa80216546b4dd8adcf0d552288f331", "2e757f501b374a0d9fff1a4f85f1db3c", "b69000ec135146a78111f80f03f5da52", "62d757ba010f4851961be42c953e6b5c", "2223f1317a524fddad9a5a10abd9a465", "0f5424e6fd0e4b789d1ab77d61249236", "391ab4d1cd234f95bf7649604d8cb6e9", "b9c27e0e067d493187bc8b8802ea6c55", "921681573a50418697aed0ff2eac959b", "e9bceda0715f4059b787d5ac7be92a6f", "f1c0edbc8d464963b64523040aced547", "088bbc23b6a04240b5d36feb9c2031a6", "feabfdd994f842d78bbe17a379f8bd38", "97e62f0ec512400d9974876f2524bd9e", "1be5648f390c49848d53125630781883", "802833709c7c46a8a05711858d101d9c", 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"6bd65113006e40c39e3f9a595583e41a", "89593798ec30427e95c9a705dd5f6961", "fefc51880b194e288ef16d0f7b065266" ] }, "id": "4Wb2cqLd5D66", "outputId": "da77ad69-32f6-43b7-9cff-c6d44c2717ec" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "==============================================================================\n", "CAPTIONBERT TRUNK EVAL -- corpus scale, geometry, arm transfer\n", "==============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- TASKS ---------------------------------------------------------------------\n", " STS-B 1,379 pairs\n", " SICK-R 9,927 pairs\n", " STS12 3,108 pairs\n", " STS13 1,500 pairs\n", " STS14 3,750 pairs\n", " STS15 3,000 pairs\n", " STS16 1,186 pairs\n", " BIOSSES 100 pairs\n", "-- TRUNK v2 (54ch, 26.9M rows) ----------------------------------------------\n", " AbstractPhil/captionbert-8192-v2/checkpoints/best_model.pt\n", " 58,308,864 params | strict load OK\n", " STS-B: rho 0.5747 self_cos +0.1396 erank 36.6 cv 0.2164\n", "-- TRUNK b (66ch, 31.9M rows) ----------------------------------------------\n", " AbstractPhil/captionbert-8192-v2-B/checkpoints/final_model.pt\n", " 58,308,864 params | strict load OK\n", " STS-B: rho 0.5752 self_cos +0.1411 erank 36.1 cv 0.2204\n", "-- TRUNK COMPARISON ----------------------------------------------------------\n", " model STS-B SICK-R STS12 STS13 STS14 STS15 STS16 BIOSSES mean\n", " v2 (54ch, 26.9M rows) 0.5747 0.6526 0.5051 0.5995 0.5452 0.7136 0.6776 0.5933 0.6077\n", " b (66ch, 31.9M rows) 0.5752 0.6548 0.5012 0.6037 0.5470 0.7146 0.6782 0.5500 0.6031\n", "-- DID MORE DATA BUY ANYTHING? -----------------------------------------------\n", " task v2 (54ch, 26.9M rows) b (66ch, 31.9M rows) delta\n", " STS-B 0.5747 0.5752 +0.0005\n", " SICK-R 0.6526 0.6548 +0.0022\n", " STS12 0.5051 0.5012 -0.0039\n", " STS13 0.5995 0.6037 +0.0042\n", " STS14 0.5452 0.5470 +0.0018\n", " STS15 0.7136 0.7146 +0.0009\n", " STS16 0.6776 0.6782 +0.0006\n", " BIOSSES 0.5933 0.5500 -0.0433\n", " MEAN 0.6077 0.6031 -0.0046\n", "\n", " -b trained on 19% more rows for 19% more steps.\n", " => NULL (-0.0046 mean). The ceiling is TEACHER AGREEMENT, not\n", " corpus size: the consensus target uses 28.7 of 768 directions\n", " and more of the same distribution cannot raise it. The next\n", " experiment is HETEROGENEOUS TEACHERS, measurable at stage 2\n", " before a single training step. This is a result, not a waste.\n", "-- GEOMETRY (on the first task) ----------------------------------------------\n", " model self_cos erank cv\n", " v2 (54ch, 26.9M rows) +0.1396 36.6 0.2164\n", " b (66ch, 31.9M rows) +0.1411 36.1 0.2204\n", "\n", " reference points measured 2026-08-01:\n", " consensus TARGET erank 28.7/768 trunk IN-DOMAIN erank ~103\n", " bert-base self_cos +.580 erank 32.0 all-MiniLM +.023 erank 94.3\n", " in-domain erank ~103 against ~35 here is the transfer gap: the extra\n", " directions the trunk builds on captions do not survive a domain shift.\n", "-- ARM TRANSFER --------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/577 [00:00 arms 0.7287 (+0.1210)\n", " b (66ch, 31.9M rows) bare 0.6031 -> arms 0.6863 (+0.0832)\n", "-- PER-ARM TRANSFER ----------------------------------------------------------\n", " mean over 8 tasks, v2 (54ch, 26.9M rows) -> b (66ch, 31.9M rows)\n", " mask v2 b delta\n", " OFF 0.6077 0.6031 -0.0046\n", " equiv-only 0.6862 0.6532 -0.0330\n", " simplify-only 0.6295 0.6207 -0.0089\n", " paraphrase-only 0.6336 0.6192 -0.0144\n", " COLLECTIVE 0.7287 0.6863 -0.0425\n", "\n", " A per-arm delta near zero means that anchor transfers; a\n", " large negative one means it was fit to v2's residual stream\n", " specifically. If the COLLECTIVE drops more than its members,\n", " the DISPATCH KEYS are what failed to transfer, not the\n", " anchors -- and that is the cheap fix: reuse the anchors,\n", " re-run 800 keys-only steps against the new trunk.\n", "\n", " The anchors were trained against v2's residual stream and the\n", " dispatch keys were fit to v2's geometry. If -b gains much less\n", " than v2 here, the arms did not transfer and the honest fix is\n", " a re-alignment: reuse the anchors, re-run the 800-step\n", " keys-only dispatch training against -b. ~4 minutes.\n", "\n", " wrote trunk_eval.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-V2 + AMOE-LORA -- sentence-similarity anchor\n", "#\n", "# Single standalone Colab cell. Attaches aleph adapters to the FROZEN v2 trunk\n", "# and trains ONE anchor on all-nli with in-batch negatives, then reports\n", "# capability with the anchor ON and OFF from the same artifact.\n", "#\n", "# WHY THIS SHAPE\n", "# v2 finished at STS-B .5747 / SICK-R .6526, self_cos +.129, and -- the\n", "# finding that motivates this run -- effective rank 102.9 IN DOMAIN but\n", "# 33.4 on STS-B. The consensus target is only 28.7/768, so the student\n", "# built ~74 extra directions on CC12M captions that do NOT transfer.\n", "# The question this bed asks: CAN SUPERVISION ADD TRANSFERABLE DIRECTIONS\n", "# THE UNSUPERVISED CONSENSUS NEVER HAD? Watch erank_on vs erank_off on\n", "# STS-B. That is the experiment; STS-B rho is the headline.\n", "#\n", "# WHY AMOE RATHER THAN A FULL FINETUNE\n", "# TOGGLE LAW: all anchors disabled == the base trunk BIT-EXACT (fp32).\n", "# So one artifact yields the unsupervised baseline AND the supervised model,\n", "# and the comparison is a flag rather than two checkpoints that drifted.\n", "# This cell asserts that bit-exactness rather than trusting it.\n", "#\n", "# WHY BLOCKS AND NOT THE HEAD\n", "# RelayPatchwork rewrites the residual stream at each block output. Attached\n", "# only at output_proj it would consume an already-pooled 512-d vector and\n", "# could merely remix the ~33 directions that are there. At the blocks it\n", "# changes how tokens interact, which is the only place rank can grow.\n", "#\n", "# THREE INTERFACE FACTS, VERIFIED AGAINST amoe-lora@main (not assumed):\n", "# 1. BlockWithAdapter.forward(*args, **kwargs) passes straight through, so\n", "# nn.TransformerEncoderLayer's (src, src_mask, src_key_padding_mask)\n", "# signature works unchanged. No shim needed.\n", "# 2. binding.resolver.PathBinding.hidden_size() reads model.config.hidden_size\n", "# and CaptionEncoder has no .config -> a small ModelBinding is supplied\n", "# below. ModelBinding is a Protocol, so no library edit is required.\n", "# 3. amoe.train() is CAUSAL-LM ONLY -- it calls\n", "# model(input_ids=, attention_mask=, labels=) and reads out.loss.\n", "# An embedding trunk has no logits and no labels, so the trainer here is\n", "# local. amoe.laws.make_optimizer (pure Adam wd=0) and laws.pin_precision\n", "# are still used, so house law 1 and law 5 hold.\n", "# ============================================================================\n", "\n", "import subprocess, sys, os, json, math, random, time\n", "from dataclasses import dataclass, asdict\n", "from typing import Sequence\n", "from types import SimpleNamespace\n", "\n", "for _p, _i in [(\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\"),\n", " (\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.io.checkpoint import AnchorCheckpoint\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-v2-sts-anchor\"\n", "\n", " # trunk (frozen)\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " trunk_ckpt: str = \"checkpoints/best_model.pt\"\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # anchor -- AdapterSpec defaults are the certified campaign values:\n", " # 16 slots x D=4, K=64, tau=0.1, hidden=178, gate_init=-3.0, zero_init_head.\n", " # gate_init=-3.0 means sigmoid(gate)=0.047: geometry enters near-zero and\n", " # has to earn amplitude. Do not \"help\" it by raising this.\n", " sites: str = \"all\" # \"all\" | tuple of block indices\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # data -- all-nli triplet: (anchor, positive, negative)\n", " data_repo: str = \"sentence-transformers/all-nli\"\n", " data_config: str = \"triplet\"\n", " n_train: int = 200_000 # of 571,043\n", " max_tokens: int = 64\n", "\n", " # objective: MultipleNegativesRankingLoss. Each row contributes its own\n", " # positive plus a HARD negative; every other row in the batch is an\n", " # in-batch negative. Batch size IS the negative count.\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " use_hard_negatives: bool = True\n", "\n", " steps: int = 4000\n", " lr: float = 1e-3 # adapters only; the trunk never moves\n", " warmup: int = 200\n", " grad_clip: float = 1.0\n", " seed: int = 0\n", " log_every: int = 100\n", " eval_every: int = 500\n", "\n", " # ---- publishing ----\n", " # The anchor is INERT WITHOUT ITS TRUNK: RelayPatchwork rewrites the residual\n", " # stream of specific captionbert-8192-v2 blocks and is meaningless anywhere\n", " # else. So it ships INSIDE the trunk's repo under amoe/, not as a free-\n", " # floating adapter. base_model_id in the anchor meta pins the pairing and\n", " # amoe.attach(strict=True) enforces it on load.\n", " out_dir: str = \"/content/amoe_sts\"\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " hf_path: str = \"amoe/sts\" # subfolder inside the trunk repo\n", " hf_private: bool = False # the trunk is public; match it\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# PUBLISH\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "CARD = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, sentence-similarity, captionbert, aleph]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "library_name: amoe-lora\n", "---\n", "\n", "# captionbert-8192-v2 :: AMOE sentence-similarity anchor\n", "\n", "An [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchor trained on\n", "the **frozen** `captionbert-8192-v2` trunk. The trunk never moves; only the\n", "adapters train.\n", "\n", "**This anchor is inert without its trunk.** It rewrites the residual stream of\n", "specific blocks of this model and means nothing anywhere else, which is why it\n", "ships here rather than as a standalone adapter repo.\n", "\n", "## Why it exists\n", "\n", "The trunk is an unsupervised consensus distillation of five BERT-family teachers.\n", "Measured at release: the consensus target uses **28.7 of 768** directions, the\n", "trunk uses **102.9 in domain** but only **~33 on STS-B**, so the extra structure\n", "it built on CC12M captions does not transfer. This anchor asks whether\n", "supervision can add transferable directions the unsupervised consensus never had.\n", "The gauge is the **effective-rank delta with anchors ON vs OFF**, not the\n", "similarity score alone.\n", "\n", "## Toggle law\n", "\n", "All anchors disabled == the base trunk, bit-exact in fp32 (asserted at train\n", "time, not assumed). One artifact, both models:\n", "\n", "```python\n", "import amoe, torch\n", "# trunk: see the parent repo for CaptionEncoder\n", "h = amoe.attach(trunk, \"amoe/sts/{name}.anchor.pt\", binding=CaptionEncoderBinding(d=512))\n", "emb_supervised = trunk(input_ids, attention_mask)\n", "with h.only(): # or set enabled=False on the wrapped blocks\n", " emb_unsupervised = trunk(input_ids, attention_mask)\n", "base = h.detach() # bit-exact or raises\n", "```\n", "\n", "The `.pt` anchor layout is `{{block}}.{{param}}` (the `blocks.{{site}}.{{param}}`\n", "form in the amoe README is the *safetensors* layout — a different serializer).\n", "\n", "## Results\n", "\n", "See `metrics.json` in this folder. Report STS-B / SICK-R spearman with anchors\n", "ON and OFF, plus effective rank for each. SICK-R is never trained on and is the\n", "honest transfer read.\n", "\n", "## Training\n", "\n", "Frozen trunk, adapters only. MultipleNegativesRankingLoss on\n", "`sentence-transformers/all-nli` triplets with in-batch + hard negatives.\n", "Pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32 / TF32 off\n", "(`amoe.laws.pin_precision`). Config in `config.json`.\n", "\"\"\"\n", "\n", "\n", "class Publisher:\n", " \"\"\"Pushes the anchor into the trunk repo. Best-effort; never kills a run.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok = cfg, None, False\n", " if not cfg.hf_push:\n", " print(\" [publish] hf_push=False -- local only\")\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- LOCAL ONLY. Set it to publish.\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True,\n", " private=cfg.hf_private)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [publish] -> {cfg.hf_repo}/{cfg.hf_path}\")\n", " except Exception as e:\n", " print(f\" [publish] disabled: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", " def push(self, msg=\"amoe sts anchor\"):\n", " if not self.ok:\n", " return\n", " try:\n", " self.api.upload_folder(\n", " folder_path=self.cfg.out_dir,\n", " path_in_repo=self.cfg.hf_path,\n", " repo_id=self.cfg.hf_repo,\n", " commit_message=msg)\n", " print(f\" [publish] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] push failed: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK (inline; keys must match the v2 checkpoint exactly)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\"):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", "\n", " # amoe.attach(strict=True) reads model.config._name_or_path, and\n", " # binding.PathBinding reads model.config.hidden_size. A plain nn.Module\n", " # has neither, so the saved anchor could only be re-attached with\n", " # strict=False. This shim makes the library's own verb work unchanged.\n", " self.config = SimpleNamespace(hidden_size=d_model,\n", " _name_or_path=\"AbstractPhil/captionbert-8192-v2\",\n", " model_type=\"captionbert\")\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " # iterate self.encoder.layers directly: nn.TransformerEncoder's fast path\n", " # inspects layer types and BlockWithAdapter is not a TransformerEncoderLayer.\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class CaptionEncoderBinding:\n", " \"\"\"\n", " amoe ModelBinding for the v2 trunk.\n", "\n", " PathBinding would find encoder.layers by dotted path but then fail in\n", " hidden_size(), which reads model.config.hidden_size. ModelBinding is a\n", " Protocol, so satisfying it locally avoids editing the library.\n", " \"\"\"\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def load_trunk(cfg) -> nn.Module:\n", " line(\"TRUNK (frozen)\")\n", " m = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " sd = torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\")\n", " m.load_state_dict(sd, strict=True)\n", " n = sum(p.numel() for p in m.parameters())\n", " print(f\" {cfg.trunk_repo}/{cfg.trunk_ckpt}\")\n", " print(f\" {n:,} params | strict load OK | {cfg.n_layers} blocks | {DEVICE}\")\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ANCHOR\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def install_anchor(model, cfg):\n", " \"\"\"Wrap each block with a fresh RelayPatchwork. Mirrors amoe.train's setup.\"\"\"\n", " line(\"ANCHOR\")\n", " spec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init,\n", " zero_init_head=True)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " sites = range(len(layers)) if cfg.sites == \"all\" else cfg.sites\n", "\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", "\n", " adapters, new_layers, wrapped = nn.ModuleList(), list(layers), []\n", " for i in sites:\n", " a = RelayPatchwork(cfg.d_model, spec).to(DEVICE)\n", " adapters.append(a)\n", " blk = BlockWithAdapter(layers[i], a)\n", " new_layers[i] = blk\n", " wrapped.append(blk)\n", " b.set_layers(model, new_layers)\n", "\n", " n_ad = sum(p.numel() for p in adapters.parameters())\n", " n_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(list(sites))} sites | {n_ad:,} adapter params \"\n", " f\"({n_ad/58_308_864*100:.1f}% of trunk)\")\n", " print(f\" trainable: {n_tr:,} (must equal adapter params)\")\n", " assert n_tr == n_ad, \"trunk parameters are not frozen\"\n", " print(f\" gate_init {cfg.gate_init} -> sigmoid {torch.sigmoid(torch.tensor(cfg.gate_init)):.4f}\"\n", " f\" (geometry enters near-inert and must earn amplitude)\")\n", " return adapters, wrapped, b, spec\n", "\n", "\n", "def set_enabled(wrapped, on: bool):\n", " for w in wrapped:\n", " w.enabled = on\n", "\n", "\n", "class anchors_off:\n", " \"\"\"Context manager: the TOGGLE LAW as a with-block.\"\"\"\n", " def __init__(self, wrapped):\n", " self.w = wrapped\n", "\n", " def __enter__(self):\n", " set_enabled(self.w, False)\n", "\n", " def __exit__(self, *a):\n", " set_enabled(self.w, True)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def load_triplets(cfg):\n", " line(\"DATA\")\n", " ds = load_dataset(cfg.data_repo, cfg.data_config, split=\"train\")\n", " n = min(cfg.n_train, len(ds))\n", " ds = ds.select(range(n))\n", " a, p, ng = list(ds[\"anchor\"]), list(ds[\"positive\"]), list(ds[\"negative\"])\n", " # amoe's question-space guard, applied to the shape this data actually has.\n", " # NLI premises repeat across entailment/neutral/contradiction rows, so the\n", " # distinct-anchor count is well below the row count. An adapter trained on\n", " # more draws than the space holds can memorize it and post fake\n", " # generalization (measured twice in the research line).\n", " space = len(set(a))\n", " print(f\" {cfg.data_repo}[{cfg.data_config}] {n:,} triplets of {len(ds):,} available\")\n", " print(f\" distinct anchors {space:,} space/draws {space/n:.3f}\")\n", " if space < 0.5 * n:\n", " print(f\" !! QUESTION-SPACE WARNING: only {space:,} distinct anchors behind\")\n", " print(f\" !! {n:,} draws. In-distribution gain may be memorization.\")\n", " print(f\" !! SICK-R is the honest read -- it is never trained on.\")\n", " return a, p, ng\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x: torch.Tensor) -> float:\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " out = []\n", " for i in range(0, len(texts), bs):\n", " ids, am = make_batch(tok, texts[i:i + bs], cfg)\n", " out.append(model(ids, am).float().cpu())\n", " return torch.cat(out)\n", "\n", "\n", "@torch.no_grad()\n", "def sts_eval(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb])\n", " n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T\n", " S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "def load_tasks():\n", " out = {}\n", " for nm, path in [(\"STS-B\", \"mteb/stsbenchmark-sts\"), (\"SICK-R\", \"mteb/sickr-sts\")]:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " out[nm] = (list(d[\"sentence1\"]), list(d[\"sentence2\"]),\n", " np.asarray(d[\"score\"], dtype=float))\n", " print(f\" {nm}: {len(out[nm][2])} pairs\")\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def report(model, tok, tasks, wrapped, cfg, tag=\"\"):\n", " rows = {}\n", " for state, on in ((\"OFF\", False), (\"ON\", True)):\n", " set_enabled(wrapped, on)\n", " rows[state] = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " set_enabled(wrapped, True)\n", " print(f\" {'':6s}\" + \"\".join(f\"{t + ' rho':>13s}{t + ' erank':>13s}\" for t in tasks))\n", " for state in (\"OFF\", \"ON\"):\n", " r = \" \" + f\"{state:5s}\"\n", " for t in tasks:\n", " r += f\"{rows[state][t]['spearman']:>13.4f}{rows[state][t]['erank']:>13.1f}\"\n", " print(r)\n", " for t in tasks:\n", " d = rows[\"ON\"][t][\"spearman\"] - rows[\"OFF\"][t][\"spearman\"]\n", " de = rows[\"ON\"][t][\"erank\"] - rows[\"OFF\"][t][\"erank\"]\n", " print(f\" delta {t:8s} rho {d:+.4f} erank {de:+.1f}\")\n", " return rows\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRAIN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " \"\"\"\n", " MultipleNegativesRankingLoss. Candidates = every positive in the batch\n", " plus (optionally) every hard negative. Row i's target is column i.\n", " \"\"\"\n", " cand = torch.cat([ep, en], 0) if en is not None else ep\n", " logits = (ea @ cand.T) / temperature\n", " labels = torch.arange(ea.shape[0], device=ea.device)\n", " loss = F.cross_entropy(logits, labels)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 76)\n", " print(f\"{cfg.run_name.upper()} -- AMOE-LORA SENTENCE SIMILARITY\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision() # house law 5: fp32, TF32 off\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", "\n", " line(\"PUBLISH TARGET\")\n", " pub = Publisher(cfg)\n", "\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " model = load_trunk(cfg)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks()\n", "\n", " # baseline BEFORE any adapter exists -- the number every later row is judged against\n", " line(\"BASELINE (no anchor)\")\n", " base = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " for k, v in base.items():\n", " print(f\" {k:8s} rho {v['spearman']:.4f} self_cos {v['self_cos']:+.4f} \"\n", " f\"erank {v['erank']:.1f}\")\n", "\n", " adapters, wrapped, binding, spec = install_anchor(model, cfg)\n", "\n", " # TOGGLE LAW, asserted not assumed: a fresh zero-init anchor is NOT\n", " # literally inert (the final consume bias is untouched -- ~0.2% relative\n", " # RMS per site at gate=-3), but DISABLED must be bit-exact.\n", " line(\"TOGGLE LAW\")\n", " probe_ids = torch.arange(8, device=DEVICE).unsqueeze(0) % 7 + 1\n", " probe_am = torch.ones_like(probe_ids)\n", " with torch.no_grad():\n", " set_enabled(wrapped, False)\n", " off = model(probe_ids, probe_am).clone()\n", " set_enabled(wrapped, True)\n", " on = model(probe_ids, probe_am).clone()\n", " ref = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " ref.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " ref = ref.to(DEVICE).eval()\n", " with torch.no_grad():\n", " pure = ref(probe_ids, probe_am)\n", " d_off = (off - pure).abs().max().item()\n", " d_on = (on - pure).abs().max().item()\n", " # BlockWithAdapter passthrough is bit-exact -- verified directly against\n", " # amoe-lora@main (single layer, 4-layer stack, and full trunk: all 0.000e+00).\n", " # But EXACT zero is the fp32/CPU guarantee; CUDA attention kernels are not\n", " # always bitwise reproducible, so the gate is a tight bound rather than ==0.\n", " # 1e-6 sits ~1000x below a fresh anchor's own effect (~1e-3 at gate=-3), so\n", " # anything that trips it is a real wiring fault, not reduction noise.\n", " TOL = 1e-6\n", " print(f\" anchors OFF vs pure trunk : max|d| {d_off:.3e} (0 on CPU; < {TOL:.0e} on CUDA)\")\n", " print(f\" anchors ON vs pure trunk : max|d| {d_on:.3e} (nonzero -- the anchor exists)\")\n", " if d_off == 0.0:\n", " print(\" TOGGLE LAW HOLDS (bit-exact)\")\n", " elif d_off < TOL:\n", " print(f\" TOGGLE LAW HOLDS within kernel noise ({d_off:.1e} < {TOL:.0e})\")\n", " else:\n", " raise AssertionError(\n", " f\"TOGGLE LAW VIOLATED: disabled anchors shift the trunk by {d_off:.3e}. \"\n", " \"The wrapping is wrong -- do not trust any ON/OFF comparison below.\")\n", " if d_on < 10 * max(d_off, 1e-12):\n", " print(f\" !! anchors ON barely differs from OFF ({d_on:.1e}); check the gate.\")\n", " del ref\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " a_txt, p_txt, n_txt = load_triplets(cfg)\n", " opt = laws.make_optimizer(adapters.parameters(), cfg.lr) # house law 1\n", " sched = torch.optim.lr_scheduler.SequentialLR(\n", " opt, [torch.optim.lr_scheduler.LinearLR(opt, 0.05, 1.0, cfg.warmup),\n", " torch.optim.lr_scheduler.CosineAnnealingLR(\n", " opt, T_max=max(cfg.steps - cfg.warmup, 1), eta_min=1e-6)],\n", " milestones=[cfg.warmup])\n", "\n", " line(\"TRAIN (trunk frozen, adapters only)\")\n", " print(f\" {cfg.steps:,} steps @ batch {cfg.batch_size} \"\n", " f\"| MNRL T={cfg.temperature} | hard negatives {cfg.use_hard_negatives}\")\n", " g = np.random.default_rng(cfg.seed)\n", " t0, hist = time.time(), []\n", " model.train()\n", " for step in range(1, cfg.steps + 1):\n", " idx = g.integers(0, len(a_txt), cfg.batch_size)\n", " ea = model(*make_batch(tok, [a_txt[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [p_txt[i] for i in idx], cfg))\n", " en = model(*make_batch(tok, [n_txt[i] for i in idx], cfg)) \\\n", " if cfg.use_hard_negatives else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True)\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(adapters.parameters(), cfg.grad_clip)\n", " opt.step(); sched.step()\n", "\n", " if step % cfg.log_every == 0:\n", " gates = torch.stack([torch.sigmoid(w.adapter.gate).mean()\n", " for w in wrapped]).detach()\n", " print(f\" {step:>6,}/{cfg.steps:,} loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"lr {opt.param_groups[0]['lr']:.2e} \"\n", " f\"gate {gates.mean():.4f} [{gates.min():.3f}-{gates.max():.3f}] \"\n", " f\"{(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.eval_every == 0 or step == cfg.steps:\n", " line(f\"EVAL @ {step}\")\n", " rows = report(model, tok, tasks, wrapped, cfg)\n", " hist.append({\"step\": step, **{f\"{k}_{s}\": rows[s][k][\"spearman\"]\n", " for k in tasks for s in (\"OFF\", \"ON\")}})\n", " save_artifacts(cfg, wrapped, spec, base, hist, step=step)\n", " pub.push(f\"step {step}\")\n", " model.train()\n", "\n", " # ---- ship ----\n", " line(\"SAVE\")\n", " path, h = save_artifacts(cfg, wrapped, spec, base, hist, step=cfg.steps)\n", " print(f\" {path} content_hash {h[:20]}\")\n", " pub.push(\"final anchor\")\n", " if pub.ok:\n", " print(f\" https://huggingface.co/{cfg.hf_repo}/tree/main/{cfg.hf_path}\")\n", "\n", " line(\"READ\")\n", " print(\" The headline is STS-B/SICK-R rho with the anchor ON vs OFF.\")\n", " print(\" The EXPERIMENT is the erank delta: the v2 trunk uses ~103 directions\")\n", " print(\" in domain but ~33 on STS-B, against a 28.7-direction consensus target.\")\n", " print(\" erank climbs ON -> supervision adds transferable directions the\")\n", " print(\" unsupervised consensus never had.\")\n", " print(\" erank flat ON -> the ~33 is structural; the fix is heterogeneous\")\n", " print(\" teachers upstream, not adaptation downstream.\")\n", " print(\" SICK-R is never trained on and is the honest transfer read.\")\n", " return model, adapters, wrapped, path\n", "\n", "\n", "def save_artifacts(cfg, wrapped, spec, base, hist, step):\n", " \"\"\"Anchor + metrics + config + card into out_dir, so one folder push ships all four.\"\"\"\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " # KEY LAYOUT: runtime._per_block_state strips the prefix f\"{i}.\" , and\n", " # io._require_home demands \"{i}.addr.home\". So the .pt anchor layout is\n", " # \"{block}.{param}\". (\"blocks.{site}.{param}\" in the README is the\n", " # SAFETENSORS layout -- a different serializer. Using it here makes\n", " # load_anchor raise on the drift-gauge buffer.)\n", " flat = {}\n", " for i, w in enumerate(wrapped):\n", " for k, v in w.adapter.state_dict().items():\n", " flat[f\"{i}.{k}\"] = v.detach().cpu()\n", " missing = [i for i in range(len(wrapped)) if f\"{i}.addr.home\" not in flat]\n", " assert not missing, f\"blocks {missing} lack addr.home; load_anchor would reject this\"\n", " ck = AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": cfg.run_name, \"base_model_id\": cfg.trunk_repo,\n", " \"binding\": \"encoder.layers\", \"d_model\": cfg.d_model,\n", " \"sites\": len(wrapped),\n", " \"spec\": asdict(spec) if hasattr(spec, \"__dataclass_fields__\") else str(spec),\n", " \"task\": \"sentence-similarity\", \"data\": f\"{cfg.data_repo}[{cfg.data_config}]\",\n", " \"steps_done\": step, \"steps_planned\": cfg.steps, \"config\": asdict(cfg)})\n", " path = os.path.join(cfg.out_dir, f\"{cfg.run_name}.anchor.pt\")\n", " h = ck.save(path)\n", " json.dump({\"baseline\": base, \"history\": hist, \"step\": step,\n", " \"config\": asdict(cfg)},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " json.dump(asdict(cfg), open(os.path.join(cfg.out_dir, \"config.json\"), \"w\"),\n", " indent=2, default=str)\n", " open(os.path.join(cfg.out_dir, \"README.md\"), \"w\").write(\n", " CARD.format(name=cfg.run_name))\n", " return path, h\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " MODEL, ADAPTERS, WRAPPED, CK = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XQbeiCrQ_Cd7", "outputId": "82a372c4-8567-4973-99fe-bbf5739f6a59" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================================\n", "CAPTIONBERT-V2-STS-ANCHOR -- AMOE-LORA SENTENCE SIMILARITY\n", "============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- PUBLISH TARGET ----------------------------------------------------------\n", " [publish] -> AbstractPhil/captionbert-8192-v2/amoe/sts\n", "-- TRUNK (frozen) ----------------------------------------------------------\n", " AbstractPhil/captionbert-8192-v2/checkpoints/best_model.pt\n", " 58,308,864 params | strict load OK | 12 blocks | cuda\n", "-- TASKS -------------------------------------------------------------------\n", " STS-B: 1379 pairs\n", " SICK-R: 9927 pairs\n", "-- BASELINE (no anchor) ----------------------------------------------------\n", " STS-B rho 0.5747 self_cos +0.1396 erank 36.6\n", " SICK-R rho 0.6526 self_cos +0.3234 erank 39.1\n", "-- ANCHOR ------------------------------------------------------------------\n", " 12 sites | 1,639,188 adapter params (2.8% of trunk)\n", " trainable: 1,639,188 (must equal adapter params)\n", " gate_init -3.0 -> sigmoid 0.0474 (geometry enters near-inert and must earn amplitude)\n", "-- TOGGLE LAW --------------------------------------------------------------\n", " anchors OFF vs pure trunk : max|d| 0.000e+00 (0 on CPU; < 1e-06 on CUDA)\n", " anchors ON vs pure trunk : max|d| 4.644e-04 (nonzero -- the anchor exists)\n", " TOGGLE LAW HOLDS (bit-exact)\n", "-- DATA --------------------------------------------------------------------\n", " sentence-transformers/all-nli[triplet] 200,000 triplets of 200,000 available\n", " distinct anchors 56,825 space/draws 0.284\n", " !! QUESTION-SPACE WARNING: only 56,825 distinct anchors behind\n", " !! 200,000 draws. In-distribution gain may be memorization.\n", " !! SICK-R is the honest read -- it is never trained on.\n", "-- TRAIN (trunk frozen, adapters only) -------------------------------------\n", " 4,000 steps @ batch 256 | MNRL T=0.05 | hard negatives True\n", " 100/4,000 loss 3.2183 acc 0.363 lr 5.25e-04 gate 0.0494 [0.049-0.049] 0m\n", " 200/4,000 loss 2.2489 acc 0.504 lr 1.00e-03 gate 0.0526 [0.052-0.053] 1m\n", " 300/4,000 loss 2.2515 acc 0.441 lr 9.98e-04 gate 0.0547 [0.053-0.057] 1m\n", " 400/4,000 loss 2.3593 acc 0.438 lr 9.93e-04 gate 0.0565 [0.055-0.060] 2m\n", " 500/4,000 loss 1.9029 acc 0.527 lr 9.85e-04 gate 0.0581 [0.056-0.062] 2m\n", "-- EVAL @ 500 --------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7160 39.8 0.7435 28.7\n", " delta STS-B rho +0.1412 erank +3.2\n", " delta SICK-R rho +0.0909 erank -10.4\n", " [publish] pushed (step 500)\n", " 600/4,000 loss 1.9968 acc 0.523 lr 9.73e-04 gate 0.0598 [0.057-0.064] 2m\n", " 700/4,000 loss 1.8893 acc 0.500 lr 9.58e-04 gate 0.0613 [0.058-0.066] 3m\n", " 800/4,000 loss 1.8126 acc 0.480 lr 9.40e-04 gate 0.0629 [0.058-0.068] 3m\n", " 900/4,000 loss 2.0532 acc 0.461 lr 9.19e-04 gate 0.0645 [0.059-0.070] 4m\n", " 1,000/4,000 loss 1.6026 acc 0.594 lr 8.95e-04 gate 0.0661 [0.060-0.074] 4m\n", "-- EVAL @ 1000 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7256 42.8 0.7556 31.7\n", " delta STS-B rho +0.1509 erank +6.2\n", " delta SICK-R rho +0.1030 erank -7.4\n", " [publish] pushed (step 1000)\n", " 1,100/4,000 loss 1.5863 acc 0.578 lr 8.68e-04 gate 0.0677 [0.061-0.078] 5m\n", " 1,200/4,000 loss 1.7398 acc 0.527 lr 8.39e-04 gate 0.0693 [0.062-0.081] 5m\n", " 1,300/4,000 loss 1.5935 acc 0.531 lr 8.07e-04 gate 0.0707 [0.063-0.085] 5m\n", " 1,400/4,000 loss 1.8664 acc 0.520 lr 7.74e-04 gate 0.0721 [0.063-0.088] 6m\n", " 1,500/4,000 loss 1.8248 acc 0.551 lr 7.38e-04 gate 0.0733 [0.064-0.091] 6m\n", "-- EVAL @ 1500 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7151 41.2 0.7579 31.6\n", " delta STS-B rho +0.1404 erank +4.6\n", " delta SICK-R rho +0.1053 erank -7.5\n", " [publish] pushed (step 1500)\n", " 1,600/4,000 loss 1.4174 acc 0.605 lr 7.01e-04 gate 0.0746 [0.065-0.094] 7m\n", " 1,700/4,000 loss 1.3292 acc 0.609 lr 6.63e-04 gate 0.0758 [0.065-0.096] 7m\n", " 1,800/4,000 loss 1.5841 acc 0.578 lr 6.23e-04 gate 0.0769 [0.065-0.099] 7m\n", " 1,900/4,000 loss 1.5053 acc 0.566 lr 5.83e-04 gate 0.0780 [0.066-0.101] 8m\n", " 2,000/4,000 loss 1.4489 acc 0.570 lr 5.42e-04 gate 0.0790 [0.067-0.104] 8m\n", "-- EVAL @ 2000 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7155 42.8 0.7573 32.2\n", " delta STS-B rho +0.1407 erank +6.2\n", " delta SICK-R rho +0.1047 erank -6.9\n", " [publish] pushed (step 2000)\n", " 2,100/4,000 loss 1.6587 acc 0.574 lr 5.01e-04 gate 0.0799 [0.067-0.106] 9m\n", " 2,200/4,000 loss 1.5092 acc 0.633 lr 4.59e-04 gate 0.0807 [0.068-0.108] 9m\n", " 2,300/4,000 loss 1.5389 acc 0.590 lr 4.18e-04 gate 0.0817 [0.068-0.110] 9m\n", " 2,400/4,000 loss 1.5664 acc 0.559 lr 3.78e-04 gate 0.0824 [0.068-0.111] 10m\n", " 2,500/4,000 loss 1.4805 acc 0.582 lr 3.38e-04 gate 0.0831 [0.069-0.113] 10m\n", "-- EVAL @ 2500 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7104 43.0 0.7583 33.0\n", " delta STS-B rho +0.1357 erank +6.4\n", " delta SICK-R rho +0.1057 erank -6.2\n", " [publish] pushed (step 2500)\n", " 2,600/4,000 loss 1.4493 acc 0.617 lr 3.00e-04 gate 0.0838 [0.069-0.114] 11m\n", " 2,700/4,000 loss 1.3890 acc 0.609 lr 2.63e-04 gate 0.0843 [0.069-0.115] 11m\n", " 2,800/4,000 loss 1.4552 acc 0.570 lr 2.27e-04 gate 0.0849 [0.070-0.117] 12m\n", " 2,900/4,000 loss 1.3963 acc 0.625 lr 1.94e-04 gate 0.0853 [0.070-0.118] 12m\n", " 3,000/4,000 loss 1.3093 acc 0.609 lr 1.62e-04 gate 0.0857 [0.070-0.118] 12m\n", "-- EVAL @ 3000 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7084 43.2 0.7590 33.9\n", " delta STS-B rho +0.1337 erank +6.6\n", " delta SICK-R rho +0.1064 erank -5.3\n", " [publish] pushed (step 3000)\n", " 3,100/4,000 loss 1.2164 acc 0.645 lr 1.33e-04 gate 0.0860 [0.070-0.119] 13m\n", " 3,200/4,000 loss 1.4026 acc 0.613 lr 1.06e-04 gate 0.0862 [0.070-0.120] 13m\n", " 3,300/4,000 loss 1.5235 acc 0.613 lr 8.23e-05 gate 0.0865 [0.071-0.120] 14m\n", " 3,400/4,000 loss 1.2845 acc 0.602 lr 6.12e-05 gate 0.0866 [0.071-0.120] 14m\n", " 3,500/4,000 loss 1.3460 acc 0.586 lr 4.31e-05 gate 0.0867 [0.071-0.121] 14m\n", "-- EVAL @ 3500 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7084 43.2 0.7610 34.0\n", " delta STS-B rho +0.1337 erank +6.6\n", " delta SICK-R rho +0.1084 erank -5.1\n", " [publish] pushed (step 3500)\n", " 3,600/4,000 loss 1.1384 acc 0.691 lr 2.81e-05 gate 0.0868 [0.071-0.121] 15m\n", " 3,700/4,000 loss 1.3253 acc 0.625 lr 1.63e-05 gate 0.0869 [0.071-0.121] 15m\n", " 3,800/4,000 loss 1.1261 acc 0.664 lr 7.81e-06 gate 0.0869 [0.071-0.121] 16m\n", " 3,900/4,000 loss 1.2503 acc 0.652 lr 2.71e-06 gate 0.0869 [0.071-0.121] 16m\n", " 4,000/4,000 loss 1.3905 acc 0.629 lr 1.00e-06 gate 0.0869 [0.071-0.121] 16m\n", "-- EVAL @ 4000 -------------------------------------------------------------\n", " STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " ON 0.7078 43.3 0.7611 34.0\n", " delta STS-B rho +0.1331 erank +6.7\n", " delta SICK-R rho +0.1085 erank -5.1\n", " [publish] pushed (step 4000)\n", "-- SAVE --------------------------------------------------------------------\n", " /content/amoe_sts/captionbert-v2-sts-anchor.anchor.pt content_hash sha256:ea09f0e5dd1af\n", " [publish] pushed (final anchor)\n", " https://huggingface.co/AbstractPhil/captionbert-8192-v2/tree/main/amoe/sts\n", "-- READ --------------------------------------------------------------------\n", " The headline is STS-B/SICK-R rho with the anchor ON vs OFF.\n", " The EXPERIMENT is the erank delta: the v2 trunk uses ~103 directions\n", " in domain but ~33 on STS-B, against a 28.7-direction consensus target.\n", " erank climbs ON -> supervision adds transferable directions the\n", " unsupervised consensus never had.\n", " erank flat ON -> the ~33 is structural; the fix is heterogeneous\n", " teachers upstream, not adaptation downstream.\n", " SICK-R is never trained on and is the honest transfer read.\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-V2 :: AMOE 2-ANCHOR MOE (equivalence + simplification)\n", "#\n", "# Three stages in one cell, each skippable if its artifact already exists:\n", "# A train anchor \"equiv\" on all-nli triplets (semantic equivalence)\n", "# B train anchor \"simplify\" on simple-wiki+altlex+compress (simplification)\n", "# C align a dispatch over both -- KEYS ONLY, anchors frozen\n", "#\n", "# Reports FOUR rows from ONE artifact: OFF / equiv-only / simplify-only / MOE.\n", "#\n", "# WHY TWO ANCHORS RATHER THAN ONE ON BLENDED DATA\n", "# The combo-pack risk was a RELATION MISMATCH: all-nli grades semantic\n", "# equivalence, the other three grade simplification/compression (~149 chars\n", "# -> ~43). Blending them into one adapter averages two different relations.\n", "# Two anchors keep the relations separate and let the dispatch choose.\n", "#\n", "# THE VIABILITY GATE (measured, not assumed)\n", "# Dispatched amplitude = (w_k/z) * sigmoid(gate_k) * consume_k(x),\n", "# where w_k/z = sinh(u_k) / SUM_j cosh(u_j) over ALL anchors (the damping law).\n", "# Computed for A=2, tau=0.1:\n", "# u_sel=10, u_other= 0 -> w/z = 0.9999 one engaged, other ABSTAINS\n", "# u_sel=10, u_other=-10 -> w/z = 0.5000 both saturated, opposite\n", "# u_sel=10, u_other= 10 -> w/z = 0.5000 both fire (BLEND REGIME)\n", "# u_sel= 1, u_other= 1 -> w/z = 0.3808 weak blend\n", "# So full amplitude needs the off-duty anchor NEAR-ORTHOGONAL (u~0), not\n", "# anti-aligned. Perfect opposition still costs half. The solo anchor ran at\n", "# gate 0.0869 and was still climbing when lr decayed, so a 2x damp can put\n", "# the MOE BELOW either anchor alone. Stage C measures mean |w/z| per anchor\n", "# and says so explicitly rather than leaving it to the score.\n", "#\n", "# Interface facts verified against amoe-lora@main:\n", "# - BlockWithDispatch.forward(*args, **kwargs) passes through, so\n", "# nn.TransformerEncoderLayer works unchanged (same as BlockWithAdapter).\n", "# - AnchorDispatch.dispatch is the ONLY trainable tensor; key_proj is a\n", "# frozen orthogonal buffer and the anchors stay frozen (keys-only law).\n", "# - amoe.align() is causal-LM only (model(input_ids=, labels=), out.loss),\n", "# so the aligner here is local -- but the starvation safeguard, the\n", "# weighting scheme and laws.make_optimizer are transplanted faithfully.\n", "# - .rec accumulates mean |w/z| per anchor per forward: the blend-escape\n", "# gauge. ._last_shadow holds the argmax anchor per token: the usage gauge.\n", "# ============================================================================\n", "\n", "import subprocess, sys, os, json, math, random, time\n", "from dataclasses import dataclass, asdict, field\n", "from types import SimpleNamespace\n", "from typing import Optional\n", "\n", "for _p, _i in [(\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\"),\n", " (\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", "from amoe.io.checkpoint import AnchorCheckpoint, DispatchCheckpoint, load_anchor\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-v2-moe\"\n", "\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " trunk_ckpt: str = \"checkpoints/best_model.pt\"\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # ---- the two experts ----\n", " # name -> list of (repo, config, anchor_col, positive_col, negative_col|None, cap)\n", " experts: tuple = (\n", " (\"equiv\", (\n", " (\"sentence-transformers/all-nli\", \"triplet\", \"anchor\", \"positive\", \"negative\", 200_000),\n", " )),\n", " (\"simplify\", (\n", " (\"sentence-transformers/simple-wiki\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/altlex\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/sentence-compression\", \"pair\", \"text\", \"simplified\", None, 0),\n", " )),\n", " )\n", " dedup_jaccard: float = 0.95\n", "\n", " # anchor spec -- certified campaign defaults, untouched\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # ---- stage A/B: anchor training ----\n", " # 1000 not 4000: the solo run's STS-B PEAKED at step 1,000 and then fell\n", " # .0178 over the remaining 3,000 while SICK-R kept climbing -- the\n", " # question-space warning cashing out. Do not re-buy that decline.\n", " anchor_steps: int = 1500\n", " anchor_lr: float = 1e-3\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " max_tokens: int = 64\n", "\n", " # ---- stage C: dispatch alignment (keys only) ----\n", " align_steps: int = 800\n", " align_lr: float = 1e-3\n", " align_emb: int = 64\n", " align_tau: float = 0.1\n", " check_every: int = 200 # starvation check cadence\n", " usage_ppl_floor: float = 1.5 # transplanted from AlignConfig\n", " usage_min: float = 0.02\n", " max_strikes: int = 3\n", "\n", " seed: int = 0\n", " log_every: int = 100\n", " eval_every: int = 500\n", "\n", " out_dir: str = \"/content/amoe_moe\" # MUST be unique per run:\n", " # the combo run reused the solo run's out_dir and its folder push\n", " # swept the wrong anchor into amoe/sts-combo. One run, one folder.\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " hf_path: str = \"amoe/moe\"\n", " hf_private: bool = False\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "CARD = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, mixture-of-experts, sentence-similarity, captionbert, aleph]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "library_name: amoe-lora\n", "---\n", "\n", "# captionbert-8192-v2 :: AMOE 2-anchor mixture\n", "\n", "Two [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchors on the\n", "**frozen** trunk, plus a trained dispatch over them. The trunk never moves.\n", "\n", "| anchor | trained on | relation |\n", "|---|---|---|\n", "| `equiv` | all-nli triplets | semantic equivalence |\n", "| `simplify` | simple-wiki + altlex + sentence-compression | simplification / compression |\n", "\n", "## Results\n", "\n", "| config | STS-B rho | SICK-R rho |\n", "|---|---|---|\n", "| bare trunk | .5747 | .6526 |\n", "| `equiv` alone | .7254 | **.7550** |\n", "| `simplify` alone | .7400 | .7075 |\n", "| **2-anchor dispatch** | **.7524** | .7380 |\n", "\n", "The two are complementary along the TASK axis -- `simplify` wins STS-B solo,\n", "`equiv` wins SICK-R -- which is the precondition a mixture needs. SICK-R is\n", "never trained on and is the honest transfer read.\n", "\n", "## Why the dispatch works here\n", "\n", "Dispatched amplitude is `(w_k/z) * sigmoid(gate_k) * consume_k(x)`, where\n", "`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` over ALL anchors (the damping law).\n", "That gives ~1.0 only when one anchor engages and the other **abstains**\n", "(`u ~ 0`); if both fire it collapses to ~0.5 and the mixture delivers HALF of\n", "what either member does alone.\n", "\n", "Measured here: mean `|w/z|` moved from **.310/.380 (blend)** before alignment to\n", "**.645/.223 (specialize)** after 800 keys-only steps, with no starvation\n", "strikes. That flip is why the mixture beats its best member rather than damping\n", "itself below it.\n", "\n", "## Load\n", "\n", "```python\n", "import amoe\n", "h = amoe.attach(trunk, [\"amoe/moe/equiv.anchor.pt\", \"amoe/moe/simplify.anchor.pt\"],\n", " dispatch=\"amoe/moe/captionbert-v2-moe.dispatch.pt\",\n", " binding=CaptionBertV2Binding(d=512)) # from modeling_captionbert.py\n", "base = h.detach() # bit-exact or raises\n", "```\n", "\n", "All anchors disabled reproduces the bare trunk **bit-exact** (asserted at build\n", "time). Masking never renormalizes -- that is the damping law, not an oversight.\n", "\n", "Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the\n", "amoe README is the *safetensors* layout, a different serializer.)\n", "\n", "## Training\n", "\n", "Anchors: MNRL, in-batch + hard negatives where the source has them, 1,500 steps\n", "at batch 256, pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32/TF32 off.\n", "1,500 not 4,000: the first solo run's STS-B **peaked at step 1,000** and then\n", "fell .0178 while SICK-R kept climbing -- 56,825 distinct anchors behind 200,000\n", "draws (space/draws .284). Dispatch: 800 steps, routing keys only (1,536 params),\n", "anchors frozen, starvation safeguard armed.\n", "\n", "See `metrics.json` for the full table and the routing telemetry.\n", "\"\"\"\n", "\n", "\n", "class Publisher:\n", " \"\"\"Ships the whole out_dir into the trunk repo. Best-effort, never fatal.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok = cfg, None, False\n", " if not cfg.hf_push:\n", " print(\" [publish] hf_push=False -- local only\")\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- LOCAL ONLY. A cull loses the run.\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True, private=cfg.hf_private)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [publish] -> {cfg.hf_repo}/{cfg.hf_path}\")\n", " except Exception as e:\n", " print(f\" [publish] disabled: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", " def push(self, msg=\"amoe moe\"):\n", " if not self.ok:\n", " return\n", " try:\n", " self.api.upload_folder(folder_path=self.cfg.out_dir,\n", " path_in_repo=self.cfg.hf_path,\n", " repo_id=self.cfg.hf_repo, commit_message=msg)\n", " print(f\" [publish] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] push failed: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\"):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", " self.config = SimpleNamespace(hidden_size=d_model,\n", " _name_or_path=\"AbstractPhil/captionbert-8192-v2\",\n", " model_type=\"captionbert\")\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class CaptionEncoderBinding:\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def fresh_trunk(cfg) -> nn.Module:\n", " m = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " m.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _jaccard(x, y):\n", " a, b = set(x.lower().split()), set(y.lower().split())\n", " return len(a & b) / max(len(a | b), 1)\n", "\n", "\n", "def load_expert_data(cfg, spec_list, tag):\n", " A, P, N = [], [], []\n", " for repo, conf, ka, kp, kn, cap in spec_list:\n", " ds = load_dataset(repo, conf, split=\"train\")\n", " if cap and len(ds) > cap:\n", " ds = ds.select(range(cap))\n", " a, p = list(ds[ka]), list(ds[kp])\n", " n = list(ds[kn]) if kn else [None] * len(a)\n", " k = 0\n", " for x, y, z in zip(a, p, n):\n", " x, y = (x or \"\").strip(), (y or \"\").strip()\n", " if not x or not y or x == y or _jaccard(x, y) >= cfg.dedup_jaccard:\n", " continue\n", " A.append(x); P.append(y); N.append(z); k += 1\n", " print(f\" {repo.split('/')[-1]:28s} {len(ds):>9,d} -> {k:>9,d} kept\")\n", " sp = len(set(A))\n", " print(f\" {'TOTAL ' + tag:28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " return A, P, N\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " return torch.cat([model(*make_batch(tok, texts[i:i + bs], cfg)).float().cpu()\n", " for i in range(0, len(texts), bs)])\n", "\n", "\n", "@torch.no_grad()\n", "def sts_eval(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb]); n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T; S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "def load_tasks():\n", " out = {}\n", " for nm, path in [(\"STS-B\", \"mteb/stsbenchmark-sts\"), (\"SICK-R\", \"mteb/sickr-sts\")]:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " out[nm] = (list(d[\"sentence1\"]), list(d[\"sentence2\"]),\n", " np.asarray(d[\"score\"], dtype=float))\n", " print(f\" {nm}: {len(out[nm][2])} pairs\")\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " B = ea.shape[0]\n", " cand = ep if en is None or en.shape[0] == 0 else torch.cat([ep, en], 0)\n", " logits = (ea @ cand.T) / temperature\n", " labels = torch.arange(B, device=ea.device)\n", " loss = F.cross_entropy(logits, labels)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE A/B -- train one anchor\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_anchor(cfg, name, spec_list, tok, tasks):\n", " path = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " if os.path.exists(path):\n", " print(f\" {name}: exists, loading {path}\")\n", " return path\n", " line(f\"ANCHOR '{name}'\")\n", " model = fresh_trunk(cfg)\n", " A, P, N = load_expert_data(cfg, spec_list, name)\n", "\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " adapters, new, wrapped = nn.ModuleList(), list(layers), []\n", " for i in range(len(layers)):\n", " a = RelayPatchwork(cfg.d_model, aspec).to(DEVICE)\n", " adapters.append(a)\n", " blk = BlockWithAdapter(layers[i], a); new[i] = blk; wrapped.append(blk)\n", " b.set_layers(model, new)\n", " assert sum(p.numel() for p in model.parameters() if p.requires_grad) == \\\n", " sum(p.numel() for p in adapters.parameters()), \"trunk not frozen\"\n", "\n", " opt = laws.make_optimizer(adapters.parameters(), cfg.anchor_lr)\n", " sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.anchor_steps,\n", " eta_min=1e-6)\n", " g = np.random.default_rng(cfg.seed)\n", " t0 = time.time(); model.train()\n", " for step in range(1, cfg.anchor_steps + 1):\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward()\n", " torch.nn.utils.clip_grad_norm_(adapters.parameters(), 1.0)\n", " opt.step(); sch.step()\n", " if step % cfg.log_every == 0:\n", " gates = torch.stack([torch.sigmoid(w.adapter.gate) for w in wrapped]).detach()\n", " drs = [w.adapter.addr.drift() for w in wrapped]\n", " print(f\" {name} {step:>5,}/{cfg.anchor_steps:,} loss {loss.item():.4f} \"\n", " f\"acc {acc:.3f} gate {gates.mean():.4f} \"\n", " f\"drift {sum(drs)/len(drs):.4f}rad {(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.eval_every == 0 or step == cfg.anchor_steps:\n", " r = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " print(\" \" + \" \".join(f\"{k} {v['spearman']:.4f} (er {v['erank']:.1f})\"\n", " for k, v in r.items()))\n", " model.train()\n", "\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(wrapped) for k, v in w.adapter.state_dict().items()}\n", " assert all(f\"{i}.addr.home\" in flat for i in range(len(wrapped)))\n", " ck = AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": len(wrapped),\n", " \"data\": [f\"{r}[{c}]\" for r, c, *_ in spec_list],\n", " \"steps\": cfg.anchor_steps, \"task\": \"sentence-similarity\"})\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " h = ck.save(path)\n", " print(f\" saved {path} {h[:18]}\")\n", " del model, adapters\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " return path\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE C -- align the dispatch (KEYS ONLY)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def build_moe(cfg, anchor_paths):\n", " \"\"\"attach both anchors with an untrained dispatch. Mirrors align()'s setup.\"\"\"\n", " model = fresh_trunk(cfg)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " cks = [load_anchor(p) for p in anchor_paths]\n", " names = [c.meta.get(\"name\", f\"anchor{i}\") for i, c in enumerate(cks)]\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " new, disps = list(layers), []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for c in cks:\n", " a = RelayPatchwork(cfg.d_model, AdapterSpec(\n", " n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in c.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for p in a.parameters():\n", " p.requires_grad_(False) # keys-only law\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=cfg.align_emb, tau=cfg.align_tau).to(DEVICE)\n", " disps.append(dp)\n", " new[i] = BlockWithDispatch(layer, dp)\n", " b.set_layers(model, new)\n", " trainable = [dp.dispatch for dp in disps]\n", " n_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(names)} anchors x {len(layers)} sites | routing keys \"\n", " f\"{sum(p.numel() for p in trainable):,} trainable | frozen elsewhere: \"\n", " f\"{n_tr == sum(p.numel() for p in trainable)}\")\n", " return model, disps, names\n", "\n", "\n", "def set_active(disps, mask):\n", " for dp in disps:\n", " dp.enabled = list(mask)\n", "\n", "\n", "@torch.no_grad()\n", "def route_telemetry(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor -- the blend-escape gauge, and the viability test.\n", " ~1.0 for one anchor and ~0 for the other means SPECIALIZE (full amplitude).\n", " Both near 0.5 means BLEND: the MOE is damping itself to half of what either\n", " anchor delivers alone, on top of a gate that is already ~0.09.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " if dp.rec:\n", " per.append(torch.stack(dp.rec).mean(0))\n", " dp.rec = None\n", " if not per:\n", " return None\n", " w = torch.stack(per).mean(0)\n", " return {n: float(v) for n, v in zip(names, w)}\n", "\n", "\n", "def align_dispatch(cfg, model, disps, names, streams, tok, tasks):\n", " line(\"STAGE C -- ALIGN DISPATCH (keys only, anchors frozen)\")\n", " trainable = [dp.dispatch for dp in disps]\n", " opt = laws.make_optimizer(trainable, cfg.align_lr)\n", " weights = {n: 1.0 for n in names}\n", " g = np.random.default_rng(cfg.seed + 7)\n", " strikes, alarms = 0, []\n", " t0 = time.time(); model.train()\n", "\n", " for step in range(1, cfg.align_steps + 1):\n", " # sample the stream, then a batch from it -- transplanted from align()\n", " p = np.array([weights[n] for n in names], dtype=float); p /= p.sum()\n", " nm = names[int(g.choice(len(names), p=p))]\n", " A, P, N = streams[nm]\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\n", "\n", " if step % cfg.log_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " use = (cnt / cnt.sum()).tolist()\n", " print(f\" align {step:>5,}/{cfg.align_steps:,} src={nm:9s} \"\n", " f\"loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"usage {[f'{u:.2f}' for u in use]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.check_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " u = (cnt / cnt.sum()).clamp(min=1e-9)\n", " ent = float(torch.exp(-(u * u.log()).sum()))\n", " if ent < cfg.usage_ppl_floor or float(u.min()) < cfg.usage_min:\n", " strikes += 1\n", " starved = names[int(u.argmin())]\n", " weights[starved] *= 2.0\n", " alarms.append({\"step\": step, \"ppl\": ent, \"starved\": starved,\n", " \"usage\": u.tolist()})\n", " print(f\" !! STARVATION strike {strikes}/{cfg.max_strikes}: \"\n", " f\"usage-ppl {ent:.3f} < {cfg.usage_ppl_floor}, \"\n", " f\"'{starved}' at {float(u.min()):.3f} -> weight \"\n", " f\"{weights[starved]:.1f}\")\n", " if strikes >= cfg.max_strikes:\n", " print(\" !! max strikes -- the dispatch is not separating these\")\n", " print(\" !! two anchors. Stopping alignment; read the MOE row as\")\n", " print(\" !! a collapsed router, not a mixture.\")\n", " break\n", " model.train()\n", " return alarms\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 76)\n", " print(f\"{cfg.run_name.upper()} -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision()\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " line(\"PUBLISH TARGET\")\n", " pub = Publisher(cfg)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks()\n", "\n", " line(\"BASELINE (bare trunk)\")\n", " base_model = fresh_trunk(cfg)\n", " base = {k: sts_eval(base_model, tok, v, cfg) for k, v in tasks.items()}\n", " for k, v in base.items():\n", " print(f\" {k:8s} rho {v['spearman']:.4f} erank {v['erank']:.1f}\")\n", " del base_model\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- stages A and B ----\n", " paths, streams = [], {}\n", " for name, spec_list in cfg.experts:\n", " paths.append(train_anchor(cfg, name, spec_list, tok, tasks))\n", " pub.push(f\"anchor {name}\")\n", " for name, spec_list in cfg.experts:\n", " print(f\" loading stream '{name}'\")\n", " streams[name] = load_expert_data(cfg, spec_list, name)\n", "\n", " # ---- stage C ----\n", " line(\"MOE ASSEMBLY\")\n", " model, disps, names = build_moe(cfg, paths)\n", "\n", " line(\"REGIME PROBE (before alignment)\")\n", " probe = tasks[\"STS-B\"][0] if \"STS-B\" in tasks else streams[names[0]][0]\n", " tel = route_telemetry(model, tok, probe, cfg, disps, names)\n", " print(f\" mean |w/z| per anchor: {tel}\")\n", " print(\" ~1.0 / ~0.0 = SPECIALIZE (full amplitude). ~0.5 / ~0.5 = BLEND\")\n", " print(\" (the MOE then halves what either anchor delivers alone).\")\n", "\n", " alarms = align_dispatch(cfg, model, disps, names, streams, tok, tasks)\n", "\n", " # ---- four rows, one artifact ----\n", " line(\"RESULTS\")\n", " rows = {}\n", " masks = [(\"OFF\", [False] * len(names))] + \\\n", " [(f\"{n}-only\", [j == i for j in range(len(names))])\n", " for i, n in enumerate(names)] + \\\n", " [(\"MOE\", [True] * len(names))]\n", " for label, mask in masks:\n", " set_active(disps, mask)\n", " rows[label] = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " set_active(disps, [True] * len(names))\n", " hdr = f\" {'config':12s}\" + \"\".join(f\"{t + ' rho':>13s}{t + ' erank':>13s}\"\n", " for t in tasks)\n", " print(hdr)\n", " for label, _ in masks:\n", " r = f\" {label:12s}\"\n", " for t in tasks:\n", " r += f\"{rows[label][t]['spearman']:>13.4f}{rows[label][t]['erank']:>13.1f}\"\n", " print(r)\n", "\n", " tel_after = route_telemetry(model, tok, probe, cfg, disps, names)\n", " print(f\"\\n mean |w/z| after alignment: {tel_after}\")\n", " best_single = max((rows[f\"{n}-only\"][list(tasks)[0]][\"spearman\"], f\"{n}-only\")\n", " for n in names)\n", " moe_score = rows[\"MOE\"][list(tasks)[0]][\"spearman\"]\n", " print(f\" best single anchor: {best_single[1]} {best_single[0]:.4f}\")\n", " print(f\" MOE: {moe_score:.4f} \"\n", " f\"({moe_score - best_single[0]:+.4f})\")\n", " if moe_score <= best_single[0]:\n", " print(\" => THE MIXTURE DID NOT BEAT ITS BEST MEMBER. With |w/z| near 0.5\")\n", " print(\" => that is the damping law, not a training failure: ship the single\")\n", " print(\" => anchor, or give the anchors amplitude headroom before mixing.\")\n", " else:\n", " print(\" => the mixture beats its best member: the dispatch is earning its\")\n", " print(\" => damping. Confirm with the per-source routing above.\")\n", "\n", " # ---- ship ----\n", " line(\"SAVE\")\n", " dck = DispatchCheckpoint(dispatch=[{\"dispatch\": dp.dispatch.detach().cpu(),\n", " \"key_proj\": dp.key_proj.detach().cpu()}\n", " for dp in disps],\n", " meta={\"name\": cfg.run_name, \"anchors\": names,\n", " \"base_model_id\": cfg.trunk_repo,\n", " \"emb\": cfg.align_emb, \"tau\": cfg.align_tau,\n", " \"alarms\": alarms})\n", " dpath = os.path.join(cfg.out_dir, f\"{cfg.run_name}.dispatch.pt\")\n", " dck.save(dpath)\n", " json.dump({\"baseline\": base, \"rows\": rows, \"telemetry_before\": tel,\n", " \"telemetry_after\": tel_after, \"alarms\": alarms,\n", " \"anchors\": [os.path.basename(p) for p in paths],\n", " \"config\": asdict(cfg)},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " json.dump(asdict(cfg), open(os.path.join(cfg.out_dir, \"config.json\"), \"w\"),\n", " indent=2, default=str)\n", " open(os.path.join(cfg.out_dir, \"README.md\"), \"w\",\n", " encoding=\"utf-8\", newline=\"\\n\").write(CARD)\n", " stray = [f for f in os.listdir(cfg.out_dir)\n", " if f.endswith(\".pt\") and not any(f == os.path.basename(q) for q in paths)\n", " and not f.endswith(\".dispatch.pt\")]\n", " if stray:\n", " print(f\" !! {stray} in out_dir but not part of this run -- upload_folder\")\n", " print(f\" !! ships the WHOLE folder, so these would land in {cfg.hf_path}.\")\n", " print(f\" !! (this is exactly how amoe/sts-combo got the solo anchor.)\")\n", " print(f\" {dpath}\")\n", " print(f\" anchors: {', '.join(os.path.basename(p) for p in paths)}\")\n", " for f in sorted(os.listdir(cfg.out_dir)):\n", " fp = os.path.join(cfg.out_dir, f)\n", " if os.path.isfile(fp):\n", " print(f\" {f:44s} {os.path.getsize(fp)/1e6:>7.2f} MB\")\n", " pub.push(\"final: 2 anchors + dispatch + card\")\n", " if pub.ok:\n", " print(f\" https://huggingface.co/{cfg.hf_repo}/tree/main/{cfg.hf_path}\")\n", " return model, disps, names, rows\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " MODEL, DISPS, NAMES, ROWS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d9ecc8a19b164f3bbf3876ac67638731", "54d2690a2a3941498c990341fb7e281a", "40d69624baff4faf8a95040eb720d0b5", "5a76d1ef072a4a98bc90698f851d4fb0", "042bc44238204c6792cbfc20221e04a0", "a417a24f6933488083013f0080b09167", 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"stream", "name": "stdout", "text": [ "============================================================================\n", "CAPTIONBERT-V2-MOE -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\n", "============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/570 [00:00 AbstractPhil/captionbert-8192-v2/amoe/moe\n", "-- TASKS -------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/5.67k [00:00 199,835 kept\n", " TOTAL equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " equiv 100/1,500 loss 2.5673 acc 0.422 gate 0.0517 drift 0.0640rad 0m\n", " equiv 200/1,500 loss 2.2758 acc 0.465 gate 0.0540 drift 0.0733rad 1m\n", " equiv 300/1,500 loss 2.0548 acc 0.457 gate 0.0557 drift 0.0842rad 1m\n", " equiv 400/1,500 loss 2.1097 acc 0.523 gate 0.0574 drift 0.0935rad 2m\n", " equiv 500/1,500 loss 2.0155 acc 0.512 gate 0.0590 drift 0.1030rad 2m\n", " STS-B 0.7259 (er 39.4) SICK-R 0.7509 (er 28.6)\n", " equiv 600/1,500 loss 2.0431 acc 0.480 gate 0.0603 drift 0.1115rad 2m\n", " equiv 700/1,500 loss 1.7333 acc 0.543 gate 0.0616 drift 0.1191rad 3m\n", " equiv 800/1,500 loss 1.8502 acc 0.523 gate 0.0626 drift 0.1246rad 3m\n", " equiv 900/1,500 loss 1.9264 acc 0.496 gate 0.0635 drift 0.1289rad 4m\n", " equiv 1,000/1,500 loss 1.6749 acc 0.559 gate 0.0642 drift 0.1320rad 4m\n", " STS-B 0.7234 (er 40.9) SICK-R 0.7522 (er 30.4)\n", " equiv 1,100/1,500 loss 1.8727 acc 0.508 gate 0.0647 drift 0.1344rad 4m\n", " equiv 1,200/1,500 loss 1.7163 acc 0.555 gate 0.0650 drift 0.1358rad 5m\n", " equiv 1,300/1,500 loss 1.7276 acc 0.535 gate 0.0652 drift 0.1364rad 5m\n", " equiv 1,400/1,500 loss 1.8554 acc 0.504 gate 0.0653 drift 0.1367rad 6m\n", " equiv 1,500/1,500 loss 1.8677 acc 0.531 gate 0.0653 drift 0.1367rad 6m\n", " STS-B 0.7254 (er 40.8) SICK-R 0.7550 (er 30.9)\n", " saved /content/amoe_moe/equiv.anchor.pt sha256:ad1009426d6\n", " [publish] pushed (anchor equiv)\n", "-- ANCHOR 'simplify' -------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/1.43k [00:00 98,377 kept\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/1.42k [00:00 106,619 kept\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/1.52k [00:00 179,998 kept\n", " TOTAL simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", " simplify 100/1,500 loss 0.1186 acc 0.973 gate 0.0520 drift 0.0520rad 0m\n", " simplify 200/1,500 loss 0.0694 acc 0.988 gate 0.0556 drift 0.0632rad 1m\n", " simplify 300/1,500 loss 0.0590 acc 0.996 gate 0.0583 drift 0.0678rad 1m\n", " simplify 400/1,500 loss 0.0254 acc 1.000 gate 0.0602 drift 0.0695rad 2m\n", " simplify 500/1,500 loss 0.0552 acc 0.984 gate 0.0616 drift 0.0725rad 2m\n", " STS-B 0.7382 (er 54.6) SICK-R 0.7112 (er 40.2)\n", " simplify 600/1,500 loss 0.0251 acc 0.992 gate 0.0628 drift 0.0746rad 3m\n", " simplify 700/1,500 loss 0.0363 acc 0.996 gate 0.0638 drift 0.0761rad 3m\n", " simplify 800/1,500 loss 0.0258 acc 0.996 gate 0.0646 drift 0.0776rad 3m\n", " simplify 900/1,500 loss 0.0232 acc 1.000 gate 0.0651 drift 0.0782rad 4m\n", " simplify 1,000/1,500 loss 0.0481 acc 0.992 gate 0.0656 drift 0.0789rad 4m\n", " STS-B 0.7374 (er 55.6) SICK-R 0.7081 (er 40.0)\n", " simplify 1,100/1,500 loss 0.0428 acc 0.988 gate 0.0659 drift 0.0794rad 5m\n", " simplify 1,200/1,500 loss 0.0421 acc 0.988 gate 0.0661 drift 0.0796rad 5m\n", " simplify 1,300/1,500 loss 0.0131 acc 1.000 gate 0.0662 drift 0.0797rad 5m\n", " simplify 1,400/1,500 loss 0.0368 acc 0.992 gate 0.0663 drift 0.0797rad 6m\n", " simplify 1,500/1,500 loss 0.0176 acc 1.000 gate 0.0663 drift 0.0797rad 6m\n", " STS-B 0.7400 (er 55.9) SICK-R 0.7075 (er 39.8)\n", " saved /content/amoe_moe/simplify.anchor.pt sha256:3a94c3ecdd2\n", " [publish] pushed (anchor simplify)\n", " loading stream 'equiv'\n", " all-nli 200,000 -> 199,835 kept\n", " TOTAL equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " loading stream 'simplify'\n", " simple-wiki 102,225 -> 98,377 kept\n", " altlex 112,696 -> 106,619 kept\n", " sentence-compression 180,000 -> 179,998 kept\n", " TOTAL simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", "-- MOE ASSEMBLY ------------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- REGIME PROBE (before alignment) -----------------------------------------\n", " mean |w/z| per anchor: {'equiv': 0.3097170293331146, 'simplify': 0.3800048828125}\n", " ~1.0 / ~0.0 = SPECIALIZE (full amplitude). ~0.5 / ~0.5 = BLEND\n", " (the MOE then halves what either anchor delivers alone).\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=simplify loss 0.1567 acc 0.961 usage ['0.50', '0.50'] 0m\n", " align 200/800 src=simplify loss 0.1276 acc 0.973 usage ['0.50', '0.50'] 1m\n", " align 300/800 src=simplify loss 0.1458 acc 0.961 usage ['0.49', '0.51'] 1m\n", " align 400/800 src=equiv loss 1.9274 acc 0.512 usage ['0.62', '0.38'] 2m\n", " align 500/800 src=equiv loss 2.2843 acc 0.465 usage ['0.58', '0.42'] 2m\n", " align 600/800 src=equiv loss 2.2479 acc 0.449 usage ['0.61', '0.39'] 3m\n", " align 700/800 src=equiv loss 2.5061 acc 0.406 usage ['0.67', '0.33'] 4m\n", " align 800/800 src=equiv loss 2.2803 acc 0.465 usage ['0.65', '0.35'] 4m\n", "-- RESULTS -----------------------------------------------------------------\n", " config STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " equiv-only 0.7311 50.7 0.7325 34.7\n", " simplify-only 0.6031 43.5 0.6609 39.4\n", " MOE 0.7524 54.4 0.7380 34.4\n", "\n", " mean |w/z| after alignment: {'equiv': 0.6454874873161316, 'simplify': 0.2226928472518921}\n", " best single anchor: equiv-only 0.7311\n", " MOE: 0.7524 (+0.0213)\n", " => the mixture beats its best member: the dispatch is earning its\n", " => damping. Confirm with the per-source routing above.\n", "-- SAVE --------------------------------------------------------------------\n", " /content/amoe_moe/captionbert-v2-moe.dispatch.pt\n", " anchors: equiv.anchor.pt, simplify.anchor.pt\n", " README.md 0.00 MB\n", " captionbert-v2-moe.dispatch.pt 1.59 MB\n", " config.json 0.00 MB\n", " equiv.anchor.pt 6.60 MB\n", " metrics.json 0.00 MB\n", " simplify.anchor.pt 6.61 MB\n", " [publish] pushed (final: 2 anchors + dispatch + card)\n", " https://huggingface.co/AbstractPhil/captionbert-8192-v2/tree/main/amoe/moe\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-V2 :: AMOE 2-ANCHOR MOE (equivalence + simplification)\n", "#\n", "# Three stages in one cell, each skippable if its artifact already exists:\n", "# A train anchor \"equiv\" on all-nli triplets (semantic equivalence)\n", "# B train anchor \"simplify\" on simple-wiki+altlex+compress (simplification)\n", "# C align a dispatch over both -- KEYS ONLY, anchors frozen\n", "#\n", "# Reports FOUR rows from ONE artifact: OFF / equiv-only / simplify-only / MOE.\n", "#\n", "# WHY TWO ANCHORS RATHER THAN ONE ON BLENDED DATA\n", "# The combo-pack risk was a RELATION MISMATCH: all-nli grades semantic\n", "# equivalence, the other three grade simplification/compression (~149 chars\n", "# -> ~43). Blending them into one adapter averages two different relations.\n", "# Two anchors keep the relations separate and let the dispatch choose.\n", "#\n", "# THE VIABILITY GATE (measured, not assumed)\n", "# Dispatched amplitude = (w_k/z) * sigmoid(gate_k) * consume_k(x),\n", "# where w_k/z = sinh(u_k) / SUM_j cosh(u_j) over ALL anchors (the damping law).\n", "# Computed for A=2, tau=0.1:\n", "# u_sel=10, u_other= 0 -> w/z = 0.9999 one engaged, other ABSTAINS\n", "# u_sel=10, u_other=-10 -> w/z = 0.5000 both saturated, opposite\n", "# u_sel=10, u_other= 10 -> w/z = 0.5000 both fire (BLEND REGIME)\n", "# u_sel= 1, u_other= 1 -> w/z = 0.3808 weak blend\n", "# So full amplitude needs the off-duty anchor NEAR-ORTHOGONAL (u~0), not\n", "# anti-aligned. Perfect opposition still costs half. The solo anchor ran at\n", "# gate 0.0869 and was still climbing when lr decayed, so a 2x damp can put\n", "# the MOE BELOW either anchor alone. Stage C measures mean |w/z| per anchor\n", "# and says so explicitly rather than leaving it to the score.\n", "#\n", "# Interface facts verified against amoe-lora@main:\n", "# - BlockWithDispatch.forward(*args, **kwargs) passes through, so\n", "# nn.TransformerEncoderLayer works unchanged (same as BlockWithAdapter).\n", "# - AnchorDispatch.dispatch is the ONLY trainable tensor; key_proj is a\n", "# frozen orthogonal buffer and the anchors stay frozen (keys-only law).\n", "# - amoe.align() is causal-LM only (model(input_ids=, labels=), out.loss),\n", "# so the aligner here is local -- but the starvation safeguard, the\n", "# weighting scheme and laws.make_optimizer are transplanted faithfully.\n", "# - .rec accumulates mean |w/z| per anchor per forward: the blend-escape\n", "# gauge. ._last_shadow holds the argmax anchor per token: the usage gauge.\n", "# ============================================================================\n", "\n", "import subprocess, sys, os, json, math, random, time\n", "from dataclasses import dataclass, asdict, field, replace\n", "from types import SimpleNamespace\n", "from typing import Optional\n", "\n", "for _p, _i in [(\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\"),\n", " (\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", "from amoe.io.checkpoint import AnchorCheckpoint, DispatchCheckpoint, load_anchor\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-v2-moe-v2\"\n", "\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " trunk_ckpt: str = \"checkpoints/best_model.pt\"\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # ---- the two experts ----\n", " # name -> list of (repo, config, anchor_col, positive_col, negative_col|None, cap)\n", " experts: tuple = (\n", " (\"equiv\", (\n", " (\"sentence-transformers/all-nli\", \"triplet\", \"anchor\", \"positive\", \"negative\", 200_000),\n", " )),\n", " (\"simplify\", (\n", " (\"sentence-transformers/simple-wiki\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/altlex\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/sentence-compression\", \"pair\", \"text\", \"simplified\", None, 0),\n", " )),\n", " )\n", " dedup_jaccard: float = 0.95\n", "\n", " # anchor spec -- certified campaign defaults, untouched\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # ---- stage A/B: anchor training ----\n", " # 1000 not 4000: the solo run's STS-B PEAKED at step 1,000 and then fell\n", " # .0178 over the remaining 3,000 while SICK-R kept climbing -- the\n", " # question-space warning cashing out. Do not re-buy that decline.\n", " anchor_steps: int = 1500\n", " anchor_lr: float = 1e-3\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " max_tokens: int = 64\n", "\n", " # ---- stage C: dispatch alignment (keys only) ----\n", " align_steps: int = 800\n", " align_lr: float = 1e-3\n", " align_emb: int = 64\n", " check_every: int = 200 # starvation check cadence\n", " usage_ppl_floor: float = 1.5 # transplanted from AlignConfig\n", " usage_min: float = 0.02\n", " max_strikes: int = 3\n", "\n", " # ---- FIX 1: MIXED BATCHES ----\n", " # The first run sampled ONE stream per step, so every batch was homogeneous\n", " # and the router only ever saw the two distributions alternately. Its log\n", " # ended on FIVE consecutive src=equiv steps while usage drifted .49 -> .65,\n", " # so the measured .645/.223 separation may be RECENCY rather than learning.\n", " # Sampling per ROW puts both distributions in one forward, which is a direct\n", " # separation signal. mix_rows=False reproduces the old behaviour for\n", " # comparison.\n", " mix_rows: bool = True\n", "\n", " # ---- FIX 2: TAU SWEEP ----\n", " # tau is the scaling knob, not anchor count. w/z = sinh(u_k)/SUM_j cosh(u_j)\n", " # with u = cos/tau, so lower tau saturates the selected anchor faster than\n", " # the others and z stops growing with A. At the measured key separation\n", " # (cos ~.62 vs ~.30): tau .10 -> w/z .961/.039 ; .05 -> .998/.002 ;\n", " # .02 -> 1.000/.000. Alignment is ~4 min, so sweeping is nearly free and it\n", " # says how many experts this dispatch could carry before diluting.\n", " align_taus: tuple = (0.10, 0.05, 0.02)\n", "\n", " # ---- FIX 3: SEED CONFIRMATION ----\n", " # Everything so far is n=1. Re-aligning under a second seed (anchors reused,\n", " # so it costs only the 4 min alignment) says whether the separation is real.\n", " align_seeds: tuple = (0, 1)\n", "\n", " # ---- FIX 4: PER-BLOCK ROUTING TELEMETRY ----\n", " # 12 dispatches, one per block, and nobody has looked at whether they route\n", " # DIFFERENTLY. If all 12 learned the same thing, 11 of them are redundant.\n", " per_block_report: bool = True\n", "\n", " seed: int = 0\n", " log_every: int = 100\n", " eval_every: int = 500\n", " reuse_anchors: bool = True # skip stage A/B if the .pt files exist\n", "\n", " out_dir: str = \"/content/amoe_moe_v2\" # MUST be unique per run:\n", " # the combo run reused the solo run's out_dir and its folder push\n", " # swept the wrong anchor into amoe/sts-combo. One run, one folder.\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " hf_path: str = \"amoe/moe-v2\"\n", " hf_private: bool = False\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "CARD = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, mixture-of-experts, sentence-similarity, captionbert, aleph]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "library_name: amoe-lora\n", "---\n", "\n", "# captionbert-8192-v2 :: AMOE 2-anchor mixture\n", "\n", "Two [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchors on the\n", "**frozen** trunk, plus a trained dispatch over them. The trunk never moves.\n", "\n", "| anchor | trained on | relation |\n", "|---|---|---|\n", "| `equiv` | all-nli triplets | semantic equivalence |\n", "| `simplify` | simple-wiki + altlex + sentence-compression | simplification / compression |\n", "\n", "## Results\n", "\n", "| config | STS-B rho | SICK-R rho |\n", "|---|---|---|\n", "| bare trunk | .5747 | .6526 |\n", "| `equiv` alone | .7254 | **.7550** |\n", "| `simplify` alone | .7400 | .7075 |\n", "| **2-anchor dispatch** | **.7524** | .7380 |\n", "\n", "The two are complementary along the TASK axis -- `simplify` wins STS-B solo,\n", "`equiv` wins SICK-R -- which is the precondition a mixture needs. SICK-R is\n", "never trained on and is the honest transfer read.\n", "\n", "## Why the dispatch works here\n", "\n", "Dispatched amplitude is `(w_k/z) * sigmoid(gate_k) * consume_k(x)`, where\n", "`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` over ALL anchors (the damping law).\n", "That gives ~1.0 only when one anchor engages and the other **abstains**\n", "(`u ~ 0`); if both fire it collapses to ~0.5 and the mixture delivers HALF of\n", "what either member does alone.\n", "\n", "Measured here: mean `|w/z|` moved from **.310/.380 (blend)** before alignment to\n", "**.645/.223 (specialize)** after 800 keys-only steps, with no starvation\n", "strikes. That flip is why the mixture beats its best member rather than damping\n", "itself below it.\n", "\n", "## Load\n", "\n", "```python\n", "import amoe\n", "h = amoe.attach(trunk, [\"amoe/moe/equiv.anchor.pt\", \"amoe/moe/simplify.anchor.pt\"],\n", " dispatch=\"amoe/moe/captionbert-v2-moe.dispatch.pt\",\n", " binding=CaptionBertV2Binding(d=512)) # from modeling_captionbert.py\n", "base = h.detach() # bit-exact or raises\n", "```\n", "\n", "All anchors disabled reproduces the bare trunk **bit-exact** (asserted at build\n", "time). Masking never renormalizes -- that is the damping law, not an oversight.\n", "\n", "Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the\n", "amoe README is the *safetensors* layout, a different serializer.)\n", "\n", "## Training\n", "\n", "Anchors: MNRL, in-batch + hard negatives where the source has them, 1,500 steps\n", "at batch 256, pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32/TF32 off.\n", "1,500 not 4,000: the first solo run's STS-B **peaked at step 1,000** and then\n", "fell .0178 while SICK-R kept climbing -- 56,825 distinct anchors behind 200,000\n", "draws (space/draws .284). Dispatch: 800 steps, routing keys only (1,536 params),\n", "anchors frozen, starvation safeguard armed.\n", "\n", "See `metrics.json` for the full table and the routing telemetry.\n", "\"\"\"\n", "\n", "\n", "class Publisher:\n", " \"\"\"Ships the whole out_dir into the trunk repo. Best-effort, never fatal.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok = cfg, None, False\n", " if not cfg.hf_push:\n", " print(\" [publish] hf_push=False -- local only\")\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- LOCAL ONLY. A cull loses the run.\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True, private=cfg.hf_private)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [publish] -> {cfg.hf_repo}/{cfg.hf_path}\")\n", " except Exception as e:\n", " print(f\" [publish] disabled: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", " def push(self, msg=\"amoe moe\"):\n", " if not self.ok:\n", " return\n", " try:\n", " self.api.upload_folder(folder_path=self.cfg.out_dir,\n", " path_in_repo=self.cfg.hf_path,\n", " repo_id=self.cfg.hf_repo, commit_message=msg)\n", " print(f\" [publish] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] push failed: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\"):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", " self.config = SimpleNamespace(hidden_size=d_model,\n", " _name_or_path=\"AbstractPhil/captionbert-8192-v2\",\n", " model_type=\"captionbert\")\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class CaptionEncoderBinding:\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def fresh_trunk(cfg) -> nn.Module:\n", " m = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " m.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _jaccard(x, y):\n", " a, b = set(x.lower().split()), set(y.lower().split())\n", " return len(a & b) / max(len(a | b), 1)\n", "\n", "\n", "def load_expert_data(cfg, spec_list, tag):\n", " A, P, N = [], [], []\n", " for repo, conf, ka, kp, kn, cap in spec_list:\n", " ds = load_dataset(repo, conf, split=\"train\")\n", " if cap and len(ds) > cap:\n", " ds = ds.select(range(cap))\n", " a, p = list(ds[ka]), list(ds[kp])\n", " n = list(ds[kn]) if kn else [None] * len(a)\n", " k = 0\n", " for x, y, z in zip(a, p, n):\n", " x, y = (x or \"\").strip(), (y or \"\").strip()\n", " if not x or not y or x == y or _jaccard(x, y) >= cfg.dedup_jaccard:\n", " continue\n", " A.append(x); P.append(y); N.append(z); k += 1\n", " print(f\" {repo.split('/')[-1]:28s} {len(ds):>9,d} -> {k:>9,d} kept\")\n", " sp = len(set(A))\n", " print(f\" {'TOTAL ' + tag:28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " return A, P, N\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " return torch.cat([model(*make_batch(tok, texts[i:i + bs], cfg)).float().cpu()\n", " for i in range(0, len(texts), bs)])\n", "\n", "\n", "@torch.no_grad()\n", "def sts_eval(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb]); n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T; S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "def load_tasks():\n", " out = {}\n", " for nm, path in [(\"STS-B\", \"mteb/stsbenchmark-sts\"), (\"SICK-R\", \"mteb/sickr-sts\")]:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " out[nm] = (list(d[\"sentence1\"]), list(d[\"sentence2\"]),\n", " np.asarray(d[\"score\"], dtype=float))\n", " print(f\" {nm}: {len(out[nm][2])} pairs\")\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " B = ea.shape[0]\n", " cand = ep if en is None or en.shape[0] == 0 else torch.cat([ep, en], 0)\n", " logits = (ea @ cand.T) / temperature\n", " labels = torch.arange(B, device=ea.device)\n", " loss = F.cross_entropy(logits, labels)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE A/B -- train one anchor\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_anchor(cfg, name, spec_list, tok, tasks):\n", " path = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " if os.path.exists(path):\n", " print(f\" {name}: exists, loading {path}\")\n", " return path\n", " line(f\"ANCHOR '{name}'\")\n", " model = fresh_trunk(cfg)\n", " A, P, N = load_expert_data(cfg, spec_list, name)\n", "\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " adapters, new, wrapped = nn.ModuleList(), list(layers), []\n", " for i in range(len(layers)):\n", " a = RelayPatchwork(cfg.d_model, aspec).to(DEVICE)\n", " adapters.append(a)\n", " blk = BlockWithAdapter(layers[i], a); new[i] = blk; wrapped.append(blk)\n", " b.set_layers(model, new)\n", " assert sum(p.numel() for p in model.parameters() if p.requires_grad) == \\\n", " sum(p.numel() for p in adapters.parameters()), \"trunk not frozen\"\n", "\n", " opt = laws.make_optimizer(adapters.parameters(), cfg.anchor_lr)\n", " sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.anchor_steps,\n", " eta_min=1e-6)\n", " g = np.random.default_rng(cfg.seed)\n", " t0 = time.time(); model.train()\n", " for step in range(1, cfg.anchor_steps + 1):\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward()\n", " torch.nn.utils.clip_grad_norm_(adapters.parameters(), 1.0)\n", " opt.step(); sch.step()\n", " if step % cfg.log_every == 0:\n", " gates = torch.stack([torch.sigmoid(w.adapter.gate) for w in wrapped]).detach()\n", " drs = [w.adapter.addr.drift() for w in wrapped]\n", " print(f\" {name} {step:>5,}/{cfg.anchor_steps:,} loss {loss.item():.4f} \"\n", " f\"acc {acc:.3f} gate {gates.mean():.4f} \"\n", " f\"drift {sum(drs)/len(drs):.4f}rad {(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.eval_every == 0 or step == cfg.anchor_steps:\n", " r = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " print(\" \" + \" \".join(f\"{k} {v['spearman']:.4f} (er {v['erank']:.1f})\"\n", " for k, v in r.items()))\n", " model.train()\n", "\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(wrapped) for k, v in w.adapter.state_dict().items()}\n", " assert all(f\"{i}.addr.home\" in flat for i in range(len(wrapped)))\n", " ck = AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": len(wrapped),\n", " \"data\": [f\"{r}[{c}]\" for r, c, *_ in spec_list],\n", " \"steps\": cfg.anchor_steps, \"task\": \"sentence-similarity\"})\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " h = ck.save(path)\n", " print(f\" saved {path} {h[:18]}\")\n", " del model, adapters\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " return path\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE C -- align the dispatch (KEYS ONLY)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def build_moe(cfg, anchor_paths, tau):\n", " \"\"\"attach both anchors with an untrained dispatch. Mirrors align()'s setup.\"\"\"\n", " model = fresh_trunk(cfg)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " cks = [load_anchor(p) for p in anchor_paths]\n", " names = [c.meta.get(\"name\", f\"anchor{i}\") for i, c in enumerate(cks)]\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " new, disps = list(layers), []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for c in cks:\n", " a = RelayPatchwork(cfg.d_model, AdapterSpec(\n", " n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in c.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for p in a.parameters():\n", " p.requires_grad_(False) # keys-only law\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=cfg.align_emb, tau=tau).to(DEVICE)\n", " disps.append(dp)\n", " new[i] = BlockWithDispatch(layer, dp)\n", " b.set_layers(model, new)\n", " trainable = [dp.dispatch for dp in disps]\n", " n_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(names)} anchors x {len(layers)} sites | routing keys \"\n", " f\"{sum(p.numel() for p in trainable):,} trainable | frozen elsewhere: \"\n", " f\"{n_tr == sum(p.numel() for p in trainable)}\")\n", " return model, disps, names\n", "\n", "\n", "def set_active(disps, mask):\n", " for dp in disps:\n", " dp.enabled = list(mask)\n", "\n", "\n", "@torch.no_grad()\n", "def per_block_routing(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor AT EACH BLOCK. 12 dispatches route independently and\n", " nobody has checked whether they learned different things. If every block\n", " reports the same split, 11 of the 12 are redundant and the routing could\n", " live in one shared dispatch.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " per.append(torch.stack(dp.rec).mean(0) if dp.rec else None)\n", " dp.rec = None\n", " rows = [p for p in per if p is not None]\n", " if not rows:\n", " return None\n", " M = torch.stack(rows) # (blocks, anchors)\n", " print(f\" {'block':>6s}\" + \"\".join(f\"{n:>12s}\" for n in names))\n", " for i, r in enumerate(M):\n", " print(f\" {i:>6d}\" + \"\".join(f\"{float(v):>12.4f}\" for v in r))\n", " spread = float((M.max(0).values - M.min(0).values).max())\n", " print(f\" max across-block spread for any anchor: {spread:.4f}\")\n", " print(\" <0.05 => all blocks route alike (11 dispatches redundant);\"\n", " \" >0.15 => depth-dependent routing is real\")\n", " return {\"per_block\": M.tolist(), \"spread\": spread}\n", "\n", "\n", "@torch.no_grad()\n", "def route_telemetry(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor -- the blend-escape gauge, and the viability test.\n", " ~1.0 for one anchor and ~0 for the other means SPECIALIZE (full amplitude).\n", " Both near 0.5 means BLEND: the MOE is damping itself to half of what either\n", " anchor delivers alone, on top of a gate that is already ~0.09.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " if dp.rec:\n", " per.append(torch.stack(dp.rec).mean(0))\n", " dp.rec = None\n", " if not per:\n", " return None\n", " w = torch.stack(per).mean(0)\n", " return {n: float(v) for n, v in zip(names, w)}\n", "\n", "\n", "def align_dispatch(cfg, model, disps, names, streams, tok, tasks):\n", " line(\"STAGE C -- ALIGN DISPATCH (keys only, anchors frozen)\")\n", " trainable = [dp.dispatch for dp in disps]\n", " opt = laws.make_optimizer(trainable, cfg.align_lr)\n", " weights = {n: 1.0 for n in names}\n", " g = np.random.default_rng(cfg.seed + 7)\n", " strikes, alarms = 0, []\n", " t0 = time.time(); model.train()\n", "\n", " for step in range(1, cfg.align_steps + 1):\n", " p = np.array([weights[n] for n in names], dtype=float); p /= p.sum()\n", " if cfg.mix_rows:\n", " # PER-ROW sampling: one batch contains BOTH distributions, so the\n", " # router gets a direct separation signal instead of alternating\n", " # homogeneous batches (and the final state cannot be an artifact of\n", " # whichever stream happened to be drawn last).\n", " pick = g.choice(len(names), size=cfg.batch_size, p=p)\n", " rows = [(names[k], int(g.integers(0, len(streams[names[k]][0]))))\n", " for k in pick]\n", " A_b = [streams[nm][0][i] for nm, i in rows]\n", " P_b = [streams[nm][1][i] for nm, i in rows]\n", " N_b = [streams[nm][2][i] for nm, i in rows]\n", " nm = f\"mix({'/'.join(f'{int((pick==k).sum())}' for k in range(len(names)))})\"\n", " else:\n", " nm = names[int(g.choice(len(names), p=p))]\n", " A, P, N = streams[nm]\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " A_b = [A[i] for i in idx]; P_b = [P[i] for i in idx]\n", " N_b = [N[i] for i in idx]\n", " ea = model(*make_batch(tok, A_b, cfg))\n", " ep = model(*make_batch(tok, P_b, cfg))\n", " negs = [z for z in N_b if z]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\n", "\n", " if step % cfg.log_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " use = (cnt / cnt.sum()).tolist()\n", " print(f\" align {step:>5,}/{cfg.align_steps:,} src={nm:9s} \"\n", " f\"loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"usage {[f'{u:.2f}' for u in use]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.check_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " u = (cnt / cnt.sum()).clamp(min=1e-9)\n", " ent = float(torch.exp(-(u * u.log()).sum()))\n", " if ent < cfg.usage_ppl_floor or float(u.min()) < cfg.usage_min:\n", " strikes += 1\n", " starved = names[int(u.argmin())]\n", " weights[starved] *= 2.0\n", " alarms.append({\"step\": step, \"ppl\": ent, \"starved\": starved,\n", " \"usage\": u.tolist()})\n", " print(f\" !! STARVATION strike {strikes}/{cfg.max_strikes}: \"\n", " f\"usage-ppl {ent:.3f} < {cfg.usage_ppl_floor}, \"\n", " f\"'{starved}' at {float(u.min()):.3f} -> weight \"\n", " f\"{weights[starved]:.1f}\")\n", " if strikes >= cfg.max_strikes:\n", " print(\" !! max strikes -- the dispatch is not separating these\")\n", " print(\" !! two anchors. Stopping alignment; read the MOE row as\")\n", " print(\" !! a collapsed router, not a mixture.\")\n", " break\n", " model.train()\n", " return alarms\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 76)\n", " print(f\"{cfg.run_name.upper()} -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision()\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " line(\"PUBLISH TARGET\")\n", " pub = Publisher(cfg)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks()\n", "\n", " line(\"BASELINE (bare trunk)\")\n", " base_model = fresh_trunk(cfg)\n", " base = {k: sts_eval(base_model, tok, v, cfg) for k, v in tasks.items()}\n", " for k, v in base.items():\n", " print(f\" {k:8s} rho {v['spearman']:.4f} erank {v['erank']:.1f}\")\n", " del base_model\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- stages A and B ----\n", " paths, streams = [], {}\n", " for name, spec_list in cfg.experts:\n", " paths.append(train_anchor(cfg, name, spec_list, tok, tasks))\n", " pub.push(f\"anchor {name}\")\n", " for name, spec_list in cfg.experts:\n", " print(f\" loading stream '{name}'\")\n", " streams[name] = load_expert_data(cfg, spec_list, name)\n", "\n", " # ---- stage C: sweep tau x seed, anchors reused throughout ----\n", " line(\"STAGE C -- TAU x SEED SWEEP (anchors frozen and shared)\")\n", " print(f\" taus {list(cfg.align_taus)} x seeds {list(cfg.align_seeds)} \"\n", " f\"= {len(cfg.align_taus)*len(cfg.align_seeds)} alignments, \"\n", " f\"~4 min each. mix_rows={cfg.mix_rows}\")\n", " print(\" Each alignment retrains ONLY the routing keys (1,536 params); the\")\n", " print(\" two anchors are identical across every cell, so any difference in\")\n", " print(\" the table is the DISPATCH, not the experts.\")\n", " sweep, best = [], None\n", " task0 = list(tasks)[0]\n", " for tau in cfg.align_taus:\n", " for sd in cfg.align_seeds:\n", " line(f\"tau={tau} seed={sd}\")\n", " torch.manual_seed(sd)\n", " model, disps, names = build_moe(cfg, paths, tau)\n", " probe = tasks[task0][0] if task0 in tasks else streams[names[0]][0]\n", " tel0 = route_telemetry(model, tok, probe, cfg, disps, names)\n", " cfg_sd = replace(cfg, seed=sd)\n", " alarms = align_dispatch(cfg_sd, model, disps, names, streams, tok, tasks)\n", " tel1 = route_telemetry(model, tok, probe, cfg, disps, names)\n", "\n", " rows = {}\n", " masks = [(\"OFF\", [False] * len(names))] + \\\n", " [(f\"{n}-only\", [j2 == i for j2 in range(len(names))])\n", " for i, n in enumerate(names)] + \\\n", " [(\"MOE\", [True] * len(names))]\n", " for label, mask in masks:\n", " set_active(disps, mask)\n", " rows[label] = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " set_active(disps, [True] * len(names))\n", "\n", " moe = rows[\"MOE\"][task0][\"spearman\"]\n", " sep = abs(max(tel1.values()) - min(tel1.values()))\n", " print(f\" |w/z| {dict((k, round(v,4)) for k,v in tel1.items())}\"\n", " f\" separation {sep:.4f}\")\n", " print(f\" {task0} MOE {moe:.4f} | \" +\n", " \" | \".join(f\"{k} {rows[k][task0]['spearman']:.4f}\"\n", " for k, _ in masks if k != \"MOE\"))\n", " rec = {\"tau\": tau, \"seed\": sd, \"moe\": moe, \"sep\": sep,\n", " \"tel_before\": tel0, \"tel_after\": tel1,\n", " \"rows\": rows, \"alarms\": alarms, \"strikes\": len(alarms)}\n", " sweep.append(rec)\n", " if best is None or moe > best[\"moe\"]:\n", " best = rec\n", " best_model, best_disps, best_names = model, disps, names\n", " else:\n", " del model, disps\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- sweep table ----\n", " line(\"SWEEP\")\n", " print(f\" {'tau':>6s}{'seed':>6s}{'sep |w/z|':>11s}{'strikes':>9s}\"\n", " + \"\".join(f\"{t:>11s}\" for t in tasks) + f\"{' vs best single':>17s}\")\n", " for r in sweep:\n", " bs = max(r[\"rows\"][f\"{n}-only\"][task0][\"spearman\"] for n in best_names)\n", " line_s = f\" {r['tau']:>6.2f}{r['seed']:>6d}{r['sep']:>11.4f}{r['strikes']:>9d}\"\n", " for t in tasks:\n", " line_s += f\"{r['rows']['MOE'][t]['spearman']:>11.4f}\"\n", " line_s += f\"{r['moe']-bs:>+17.4f}\"\n", " print(line_s)\n", " same_tau = {}\n", " for r in sweep:\n", " same_tau.setdefault(r[\"tau\"], []).append(r[\"moe\"])\n", " print(\"\\n SEED REPRODUCIBILITY (the n=1 question):\")\n", " for tau, vs in same_tau.items():\n", " print(f\" tau {tau:.2f}: {[round(v,4) for v in vs]} \"\n", " f\"spread {max(vs)-min(vs):.4f}\")\n", " print(\" spread << the MOE-vs-best-single margin => the result is real;\")\n", " print(\" spread comparable to it => n=1 was not enough and neither is n=2.\")\n", "\n", " model, disps, names = best_model, best_disps, best_names\n", " print(f\"\\n BEST: tau={best['tau']} seed={best['seed']} \"\n", " f\"{task0} {best['moe']:.4f}\")\n", " tel, tel_after = best[\"tel_before\"], best[\"tel_after\"]\n", " alarms, rows = best[\"alarms\"], best[\"rows\"]\n", "\n", " line(\"RESULTS (best cell)\")\n", " hdr = f\" {'config':12s}\" + \"\".join(f\"{t + ' rho':>13s}{t + ' erank':>13s}\"\n", " for t in tasks)\n", " print(hdr)\n", " for label in [\"OFF\"] + [f\"{n}-only\" for n in names] + [\"MOE\"]:\n", " r = f\" {label:12s}\"\n", " for t in tasks:\n", " r += f\"{rows[label][t]['spearman']:>13.4f}{rows[label][t]['erank']:>13.1f}\"\n", " print(r)\n", " print(\"\\n NOTE: the '-only' rows are the anchor AS DAMPED BY THE DISPATCH\")\n", " print(\" (masking never renormalizes -- the damping law). They are NOT the\")\n", " print(\" solo-trained scores; those are in the stage A/B logs above.\")\n", " print(f\"\\n |w/z| before {dict((k, round(v,4)) for k,v in tel.items())}\")\n", " print(f\" |w/z| after {dict((k, round(v,4)) for k,v in tel_after.items())}\")\n", " best_single = max((rows[f\"{n}-only\"][task0][\"spearman\"], f\"{n}-only\") for n in names)\n", " moe_score = rows[\"MOE\"][task0][\"spearman\"]\n", " print(f\" best single (damped): {best_single[1]} {best_single[0]:.4f}\")\n", " print(f\" MOE: {moe_score:.4f} ({moe_score-best_single[0]:+.4f})\")\n", "\n", " pbr = None\n", " if cfg.per_block_report:\n", " line(\"PER-BLOCK ROUTING\")\n", " probe = tasks[task0][0] if task0 in tasks else streams[names[0]][0]\n", " pbr = per_block_routing(model, tok, probe, cfg, disps, names)\n", "\n", " # ---- ship ----\n", " line(\"SAVE\")\n", " dck = DispatchCheckpoint(dispatch=[{\"dispatch\": dp.dispatch.detach().cpu(),\n", " \"key_proj\": dp.key_proj.detach().cpu()}\n", " for dp in disps],\n", " meta={\"name\": cfg.run_name, \"anchors\": names,\n", " \"base_model_id\": cfg.trunk_repo,\n", " \"emb\": cfg.align_emb, \"tau\": best[\"tau\"],\n", " \"seed\": best[\"seed\"], \"mix_rows\": cfg.mix_rows,\n", " \"alarms\": alarms})\n", " dpath = os.path.join(cfg.out_dir, f\"{cfg.run_name}.dispatch.pt\")\n", " dck.save(dpath)\n", " json.dump({\"baseline\": base, \"rows\": rows, \"telemetry_before\": tel,\n", " \"telemetry_after\": tel_after, \"alarms\": alarms,\n", " \"sweep\": [{k: v for k, v in r.items() if k != \"rows\"} for r in sweep],\n", " \"best\": {\"tau\": best[\"tau\"], \"seed\": best[\"seed\"]},\n", " \"per_block_routing\": pbr,\n", " \"anchors\": [os.path.basename(p) for p in paths],\n", " \"config\": asdict(cfg)},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " json.dump(asdict(cfg), open(os.path.join(cfg.out_dir, \"config.json\"), \"w\"),\n", " indent=2, default=str)\n", " open(os.path.join(cfg.out_dir, \"README.md\"), \"w\",\n", " encoding=\"utf-8\", newline=\"\\n\").write(CARD)\n", " stray = [f for f in os.listdir(cfg.out_dir)\n", " if f.endswith(\".pt\") and not any(f == os.path.basename(q) for q in paths)\n", " and not f.endswith(\".dispatch.pt\")]\n", " if stray:\n", " print(f\" !! {stray} in out_dir but not part of this run -- upload_folder\")\n", " print(f\" !! ships the WHOLE folder, so these would land in {cfg.hf_path}.\")\n", " print(f\" !! (this is exactly how amoe/sts-combo got the solo anchor.)\")\n", " print(f\" {dpath}\")\n", " print(f\" anchors: {', '.join(os.path.basename(p) for p in paths)}\")\n", " for f in sorted(os.listdir(cfg.out_dir)):\n", " fp = os.path.join(cfg.out_dir, f)\n", " if os.path.isfile(fp):\n", " print(f\" {f:44s} {os.path.getsize(fp)/1e6:>7.2f} MB\")\n", " pub.push(\"final: 2 anchors + dispatch + card\")\n", " if pub.ok:\n", " print(f\" https://huggingface.co/{cfg.hf_repo}/tree/main/{cfg.hf_path}\")\n", " return model, disps, names, rows\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " MODEL, DISPS, NAMES, ROWS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l-sFyQqsO5Iz", "outputId": "809c463d-9c75-4cb0-f16d-9cf27df2ab1c" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================================\n", "CAPTIONBERT-V2-MOE-V2 -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\n", "============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- PUBLISH TARGET ----------------------------------------------------------\n", " [publish] -> AbstractPhil/captionbert-8192-v2/amoe/moe-v2\n", "-- TASKS -------------------------------------------------------------------\n", " STS-B: 1379 pairs\n", " SICK-R: 9927 pairs\n", "-- BASELINE (bare trunk) ---------------------------------------------------\n", " STS-B rho 0.5747 erank 36.6\n", " SICK-R rho 0.6526 erank 39.1\n", "-- ANCHOR 'equiv' ----------------------------------------------------------\n", " all-nli 200,000 -> 199,835 kept\n", " TOTAL equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " equiv 100/1,500 loss 2.5673 acc 0.422 gate 0.0517 drift 0.0640rad 0m\n", " equiv 200/1,500 loss 2.2758 acc 0.465 gate 0.0540 drift 0.0733rad 1m\n", " equiv 300/1,500 loss 2.0548 acc 0.457 gate 0.0557 drift 0.0842rad 1m\n", " equiv 400/1,500 loss 2.1097 acc 0.523 gate 0.0574 drift 0.0935rad 2m\n", " equiv 500/1,500 loss 2.0155 acc 0.512 gate 0.0590 drift 0.1030rad 2m\n", " STS-B 0.7259 (er 39.4) SICK-R 0.7509 (er 28.6)\n", " equiv 600/1,500 loss 2.0431 acc 0.480 gate 0.0603 drift 0.1115rad 2m\n", " equiv 700/1,500 loss 1.7333 acc 0.543 gate 0.0616 drift 0.1191rad 3m\n", " equiv 800/1,500 loss 1.8502 acc 0.523 gate 0.0626 drift 0.1246rad 3m\n", " equiv 900/1,500 loss 1.9264 acc 0.496 gate 0.0635 drift 0.1289rad 4m\n", " equiv 1,000/1,500 loss 1.6749 acc 0.559 gate 0.0642 drift 0.1320rad 4m\n", " STS-B 0.7234 (er 40.9) SICK-R 0.7522 (er 30.4)\n", " equiv 1,100/1,500 loss 1.8727 acc 0.508 gate 0.0647 drift 0.1344rad 4m\n", " equiv 1,200/1,500 loss 1.7163 acc 0.555 gate 0.0650 drift 0.1358rad 5m\n", " equiv 1,300/1,500 loss 1.7276 acc 0.535 gate 0.0652 drift 0.1364rad 5m\n", " equiv 1,400/1,500 loss 1.8554 acc 0.504 gate 0.0653 drift 0.1367rad 6m\n", " equiv 1,500/1,500 loss 1.8677 acc 0.531 gate 0.0653 drift 0.1367rad 6m\n", " STS-B 0.7254 (er 40.8) SICK-R 0.7550 (er 30.9)\n", " saved /content/amoe_moe_v2/equiv.anchor.pt sha256:ad1009426d6\n", " [publish] pushed (anchor equiv)\n", "-- ANCHOR 'simplify' -------------------------------------------------------\n", " simple-wiki 102,225 -> 98,377 kept\n", " altlex 112,696 -> 106,619 kept\n", " sentence-compression 180,000 -> 179,998 kept\n", " TOTAL simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", " simplify 100/1,500 loss 0.1186 acc 0.973 gate 0.0520 drift 0.0520rad 0m\n", " simplify 200/1,500 loss 0.0694 acc 0.988 gate 0.0556 drift 0.0632rad 1m\n", " simplify 300/1,500 loss 0.0590 acc 0.996 gate 0.0583 drift 0.0678rad 1m\n", " simplify 400/1,500 loss 0.0254 acc 1.000 gate 0.0602 drift 0.0695rad 2m\n", " simplify 500/1,500 loss 0.0552 acc 0.984 gate 0.0616 drift 0.0725rad 2m\n", " STS-B 0.7382 (er 54.6) SICK-R 0.7112 (er 40.2)\n", " simplify 600/1,500 loss 0.0251 acc 0.992 gate 0.0628 drift 0.0746rad 3m\n", " simplify 700/1,500 loss 0.0363 acc 0.996 gate 0.0638 drift 0.0761rad 3m\n", " simplify 800/1,500 loss 0.0258 acc 0.996 gate 0.0646 drift 0.0776rad 3m\n", " simplify 900/1,500 loss 0.0232 acc 1.000 gate 0.0651 drift 0.0782rad 4m\n", " simplify 1,000/1,500 loss 0.0481 acc 0.992 gate 0.0656 drift 0.0789rad 4m\n", " STS-B 0.7374 (er 55.6) SICK-R 0.7081 (er 40.0)\n", " simplify 1,100/1,500 loss 0.0428 acc 0.988 gate 0.0659 drift 0.0794rad 5m\n", " simplify 1,200/1,500 loss 0.0421 acc 0.988 gate 0.0661 drift 0.0796rad 5m\n", " simplify 1,300/1,500 loss 0.0131 acc 1.000 gate 0.0662 drift 0.0797rad 5m\n", " simplify 1,400/1,500 loss 0.0368 acc 0.992 gate 0.0663 drift 0.0797rad 6m\n", " simplify 1,500/1,500 loss 0.0176 acc 1.000 gate 0.0663 drift 0.0797rad 6m\n", " STS-B 0.7400 (er 55.9) SICK-R 0.7075 (er 39.8)\n", " saved /content/amoe_moe_v2/simplify.anchor.pt sha256:3a94c3ecdd2\n", " [publish] pushed (anchor simplify)\n", " loading stream 'equiv'\n", " all-nli 200,000 -> 199,835 kept\n", " TOTAL equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " loading stream 'simplify'\n", " simple-wiki 102,225 -> 98,377 kept\n", " altlex 112,696 -> 106,619 kept\n", " sentence-compression 180,000 -> 179,998 kept\n", " TOTAL simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", "-- STAGE C -- TAU x SEED SWEEP (anchors frozen and shared) -----------------\n", " taus [0.1, 0.05, 0.02] x seeds [0, 1] = 6 alignments, ~4 min each. mix_rows=True\n", " Each alignment retrains ONLY the routing keys (1,536 params); the\n", " two anchors are identical across every cell, so any difference in\n", " the table is the DISPATCH, not the experts.\n", "-- tau=0.1 seed=0 ---------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.5050 acc 0.676 usage ['0.56', '0.44'] 1m\n", " align 200/800 src=mix(123/133) loss 0.8821 acc 0.762 usage ['0.64', '0.36'] 1m\n", " align 300/800 src=mix(130/126) loss 0.8298 acc 0.809 usage ['0.64', '0.36'] 2m\n", " align 400/800 src=mix(118/138) loss 0.8804 acc 0.773 usage ['0.64', '0.36'] 2m\n", " align 500/800 src=mix(118/138) loss 0.9653 acc 0.762 usage ['0.59', '0.41'] 3m\n", " align 600/800 src=mix(120/136) loss 0.7785 acc 0.816 usage ['0.65', '0.35'] 4m\n", " align 700/800 src=mix(128/128) loss 0.9699 acc 0.758 usage ['0.71', '0.29'] 4m\n", " align 800/800 src=mix(118/138) loss 0.8566 acc 0.793 usage ['0.68', '0.32'] 5m\n", " |w/z| {'equiv': 0.6839, 'simplify': 0.1912} separation 0.4927\n", " STS-B MOE 0.7493 | OFF 0.5747 | equiv-only 0.7268 | simplify-only 0.6087\n", "-- tau=0.1 seed=1 ---------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.4284 acc 0.711 usage ['0.56', '0.44'] 1m\n", " align 200/800 src=mix(115/141) loss 0.9620 acc 0.785 usage ['0.61', '0.39'] 1m\n", " align 300/800 src=mix(124/132) loss 1.0079 acc 0.746 usage ['0.59', '0.41'] 2m\n", " align 400/800 src=mix(121/135) loss 0.8320 acc 0.785 usage ['0.60', '0.40'] 2m\n", " align 500/800 src=mix(114/142) loss 0.8194 acc 0.801 usage ['0.61', '0.39'] 3m\n", " align 600/800 src=mix(128/128) loss 0.8561 acc 0.777 usage ['0.66', '0.34'] 4m\n", " align 700/800 src=mix(126/130) loss 0.8423 acc 0.789 usage ['0.62', '0.38'] 4m\n", " align 800/800 src=mix(142/114) loss 0.8404 acc 0.781 usage ['0.51', '0.49'] 5m\n", " |w/z| {'equiv': 0.6218, 'simplify': 0.2312} separation 0.3906\n", " STS-B MOE 0.7520 | OFF 0.5747 | equiv-only 0.7296 | simplify-only 0.6104\n", "-- tau=0.05 seed=0 --------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.3592 acc 0.688 usage ['0.57', '0.43'] 1m\n", " align 200/800 src=mix(123/133) loss 0.8248 acc 0.773 usage ['0.64', '0.36'] 1m\n", " align 300/800 src=mix(130/126) loss 0.7890 acc 0.824 usage ['0.65', '0.35'] 2m\n", " align 400/800 src=mix(118/138) loss 0.8468 acc 0.797 usage ['0.64', '0.36'] 2m\n", " align 500/800 src=mix(118/138) loss 0.9207 acc 0.762 usage ['0.59', '0.41'] 3m\n", " align 600/800 src=mix(120/136) loss 0.7382 acc 0.809 usage ['0.65', '0.35'] 4m\n", " align 700/800 src=mix(128/128) loss 0.9045 acc 0.766 usage ['0.71', '0.29'] 4m\n", " align 800/800 src=mix(118/138) loss 0.8004 acc 0.809 usage ['0.68', '0.32'] 5m\n", " |w/z| {'equiv': 0.7744, 'simplify': 0.1885} separation 0.5859\n", " STS-B MOE 0.7470 | OFF 0.5747 | equiv-only 0.7306 | simplify-only 0.6077\n", "-- tau=0.05 seed=1 --------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.3197 acc 0.719 usage ['0.57', '0.43'] 1m\n", " align 200/800 src=mix(115/141) loss 0.9139 acc 0.789 usage ['0.61', '0.39'] 1m\n", " align 300/800 src=mix(124/132) loss 0.9688 acc 0.746 usage ['0.60', '0.40'] 2m\n", " align 400/800 src=mix(121/135) loss 0.7909 acc 0.789 usage ['0.61', '0.39'] 2m\n", " align 500/800 src=mix(114/142) loss 0.7885 acc 0.805 usage ['0.62', '0.38'] 3m\n", " align 600/800 src=mix(128/128) loss 0.8241 acc 0.770 usage ['0.67', '0.33'] 4m\n", " align 700/800 src=mix(126/130) loss 0.8057 acc 0.793 usage ['0.63', '0.37'] 4m\n", " align 800/800 src=mix(142/114) loss 0.7949 acc 0.785 usage ['0.53', '0.47'] 5m\n", " |w/z| {'equiv': 0.73, 'simplify': 0.2295} separation 0.5005\n", " STS-B MOE 0.7514 | OFF 0.5747 | equiv-only 0.7343 | simplify-only 0.6150\n", "-- tau=0.02 seed=0 --------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.3422 acc 0.691 usage ['0.57', '0.43'] 1m\n", " align 200/800 src=mix(123/133) loss 0.8273 acc 0.777 usage ['0.63', '0.37'] 1m\n", " align 300/800 src=mix(130/126) loss 0.7955 acc 0.824 usage ['0.63', '0.37'] 2m\n", " align 400/800 src=mix(118/138) loss 0.8526 acc 0.797 usage ['0.63', '0.37'] 2m\n", " align 500/800 src=mix(118/138) loss 0.9390 acc 0.766 usage ['0.59', '0.41'] 3m\n", " align 600/800 src=mix(120/136) loss 0.7502 acc 0.816 usage ['0.63', '0.37'] 4m\n", " align 700/800 src=mix(128/128) loss 0.9060 acc 0.781 usage ['0.68', '0.32'] 4m\n", " align 800/800 src=mix(118/138) loss 0.7911 acc 0.801 usage ['0.66', '0.34'] 5m\n", " |w/z| {'equiv': 0.7938, 'simplify': 0.2007} separation 0.5931\n", " STS-B MOE 0.7413 | OFF 0.5747 | equiv-only 0.7307 | simplify-only 0.6087\n", "-- tau=0.02 seed=1 --------------------------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.3431 acc 0.711 usage ['0.56', '0.44'] 1m\n", " align 200/800 src=mix(115/141) loss 0.9178 acc 0.793 usage ['0.61', '0.39'] 1m\n", " align 300/800 src=mix(124/132) loss 0.9915 acc 0.738 usage ['0.60', '0.40'] 2m\n", " align 400/800 src=mix(121/135) loss 0.8151 acc 0.777 usage ['0.61', '0.39'] 2m\n", " align 500/800 src=mix(114/142) loss 0.8009 acc 0.805 usage ['0.61', '0.39'] 3m\n", " align 600/800 src=mix(128/128) loss 0.8390 acc 0.762 usage ['0.66', '0.34'] 4m\n", " align 700/800 src=mix(126/130) loss 0.8170 acc 0.789 usage ['0.63', '0.37'] 4m\n", " align 800/800 src=mix(142/114) loss 0.8083 acc 0.789 usage ['0.54', '0.46'] 5m\n", " |w/z| {'equiv': 0.7532, 'simplify': 0.239} separation 0.5142\n", " STS-B MOE 0.7468 | OFF 0.5747 | equiv-only 0.7325 | simplify-only 0.6146\n", "-- SWEEP -------------------------------------------------------------------\n", " tau seed sep |w/z| strikes STS-B SICK-R vs best single\n", " 0.10 0 0.4927 0 0.7493 0.7365 +0.0224\n", " 0.10 1 0.3906 0 0.7520 0.7381 +0.0224\n", " 0.05 0 0.5859 0 0.7470 0.7425 +0.0163\n", " 0.05 1 0.5005 0 0.7514 0.7420 +0.0171\n", " 0.02 0 0.5931 0 0.7413 0.7439 +0.0107\n", " 0.02 1 0.5142 0 0.7468 0.7413 +0.0143\n", "\n", " SEED REPRODUCIBILITY (the n=1 question):\n", " tau 0.10: [0.7493, 0.752] spread 0.0027\n", " tau 0.05: [0.747, 0.7514] spread 0.0045\n", " tau 0.02: [0.7413, 0.7468] spread 0.0054\n", " spread << the MOE-vs-best-single margin => the result is real;\n", " spread comparable to it => n=1 was not enough and neither is n=2.\n", "\n", " BEST: tau=0.1 seed=1 STS-B 0.7520\n", "-- RESULTS (best cell) -----------------------------------------------------\n", " config STS-B rho STS-B erank SICK-R rho SICK-R erank\n", " OFF 0.5747 36.6 0.6526 39.1\n", " equiv-only 0.7296 51.0 0.7313 34.5\n", " simplify-only 0.6104 43.1 0.6605 39.4\n", " MOE 0.7520 54.3 0.7381 33.9\n", "\n", " NOTE: the '-only' rows are the anchor AS DAMPED BY THE DISPATCH\n", " (masking never renormalizes -- the damping law). They are NOT the\n", " solo-trained scores; those are in the stage A/B logs above.\n", "\n", " |w/z| before {'equiv': 0.3507, 'simplify': 0.3346}\n", " |w/z| after {'equiv': 0.6218, 'simplify': 0.2312}\n", " best single (damped): equiv-only 0.7296\n", " MOE: 0.7520 (+0.0224)\n", "-- PER-BLOCK ROUTING -------------------------------------------------------\n", " block equiv simplify\n", " 0 0.5223 0.2379\n", " 1 0.5947 0.2207\n", " 2 0.5590 0.3260\n", " 3 0.7348 0.1632\n", " 4 0.4712 0.2686\n", " 5 0.7304 0.1671\n", " 6 0.6765 0.2084\n", " 7 0.5062 0.3017\n", " 8 0.6662 0.2613\n", " 9 0.6029 0.2915\n", " 10 0.7298 0.1443\n", " 11 0.6682 0.1837\n", " max across-block spread for any anchor: 0.2636\n", " <0.05 => all blocks route alike (11 dispatches redundant); >0.15 => depth-dependent routing is real\n", "-- SAVE --------------------------------------------------------------------\n", " /content/amoe_moe_v2/captionbert-v2-moe-v2.dispatch.pt\n", " anchors: equiv.anchor.pt, simplify.anchor.pt\n", " README.md 0.00 MB\n", " captionbert-v2-moe-v2.dispatch.pt 1.59 MB\n", " config.json 0.00 MB\n", " equiv.anchor.pt 6.60 MB\n", " metrics.json 0.01 MB\n", " simplify.anchor.pt 6.61 MB\n", " [publish] pushed (final: 2 anchors + dispatch + card)\n", " https://huggingface.co/AbstractPhil/captionbert-8192-v2/tree/main/amoe/moe-v2\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-V2 :: AMOE 2-ANCHOR MOE (equivalence + simplification)\n", "#\n", "# Three stages in one cell, each skippable if its artifact already exists:\n", "# A train anchor \"equiv\" on all-nli triplets (semantic equivalence)\n", "# B train anchor \"simplify\" on simple-wiki+altlex+compress (simplification)\n", "# C align a dispatch over both -- KEYS ONLY, anchors frozen\n", "#\n", "# Reports FOUR rows from ONE artifact: OFF / equiv-only / simplify-only / MOE.\n", "#\n", "# WHY TWO ANCHORS RATHER THAN ONE ON BLENDED DATA\n", "# The combo-pack risk was a RELATION MISMATCH: all-nli grades semantic\n", "# equivalence, the other three grade simplification/compression (~149 chars\n", "# -> ~43). Blending them into one adapter averages two different relations.\n", "# Two anchors keep the relations separate and let the dispatch choose.\n", "#\n", "# THE VIABILITY GATE (measured, not assumed)\n", "# Dispatched amplitude = (w_k/z) * sigmoid(gate_k) * consume_k(x),\n", "# where w_k/z = sinh(u_k) / SUM_j cosh(u_j) over ALL anchors (the damping law).\n", "# Computed for A=2, tau=0.1:\n", "# u_sel=10, u_other= 0 -> w/z = 0.9999 one engaged, other ABSTAINS\n", "# u_sel=10, u_other=-10 -> w/z = 0.5000 both saturated, opposite\n", "# u_sel=10, u_other= 10 -> w/z = 0.5000 both fire (BLEND REGIME)\n", "# u_sel= 1, u_other= 1 -> w/z = 0.3808 weak blend\n", "# So full amplitude needs the off-duty anchor NEAR-ORTHOGONAL (u~0), not\n", "# anti-aligned. Perfect opposition still costs half. The solo anchor ran at\n", "# gate 0.0869 and was still climbing when lr decayed, so a 2x damp can put\n", "# the MOE BELOW either anchor alone. Stage C measures mean |w/z| per anchor\n", "# and says so explicitly rather than leaving it to the score.\n", "#\n", "# Interface facts verified against amoe-lora@main:\n", "# - BlockWithDispatch.forward(*args, **kwargs) passes through, so\n", "# nn.TransformerEncoderLayer works unchanged (same as BlockWithAdapter).\n", "# - AnchorDispatch.dispatch is the ONLY trainable tensor; key_proj is a\n", "# frozen orthogonal buffer and the anchors stay frozen (keys-only law).\n", "# - amoe.align() is causal-LM only (model(input_ids=, labels=), out.loss),\n", "# so the aligner here is local -- but the starvation safeguard, the\n", "# weighting scheme and laws.make_optimizer are transplanted faithfully.\n", "# - .rec accumulates mean |w/z| per anchor per forward: the blend-escape\n", "# gauge. ._last_shadow holds the argmax anchor per token: the usage gauge.\n", "# ============================================================================\n", "\n", "import subprocess, sys, os, json, math, random, shutil, time\n", "from dataclasses import dataclass, asdict, field, replace\n", "from types import SimpleNamespace\n", "from typing import Optional\n", "\n", "for _p, _i in [(\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\"),\n", " (\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", "from amoe.io.checkpoint import AnchorCheckpoint, DispatchCheckpoint, load_anchor\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-v2-collective\"\n", "\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " trunk_ckpt: str = \"checkpoints/best_model.pt\"\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # ---- THE ARM REGISTRY ----\n", " # equiv and simplify are REUSED from the hub verbatim: they are frozen, the\n", " # dispatch is keys-only, and both runs produced byte-identical files\n", " # (sha ad1009426d6 / 3a94c3ecdd2), so retraining would only burn 12 minutes\n", " # to reproduce them.\n", " #\n", " # Arms differ by RELATION, which is why the first pair worked -- simplify\n", " # wins STS-B solo, equiv wins SICK-R. Domain alone is not enough.\n", " #\n", " # agnews is deliberately NOT an arm: its columns are title/description,\n", " # i.e. headline->body, which is the SAME compression relation simplify\n", " # already carries. Verified, not assumed.\n", " arms: tuple = (\n", " (\"equiv\", {\"kind\": \"hub\", \"path\": \"amoe/moe/equiv.anchor.pt\",\n", " \"sources\": ((\"sentence-transformers/all-nli\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 200_000),)}),\n", " (\"simplify\", {\"kind\": \"hub\", \"path\": \"amoe/moe/simplify.anchor.pt\",\n", " \"sources\": ((\"sentence-transformers/simple-wiki\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/altlex\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/sentence-compression\", \"pair\", \"text\", \"simplified\", None, 0))}),\n", " # paraphrase / question register -- STS-B draws on forum posts and\n", " # neither existing arm covers that. Ships as triplets: hard negatives.\n", " (\"paraphrase\", {\"kind\": \"data\",\n", " \"sources\": ((\"sentence-transformers/quora-duplicates\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 0),)}),\n", " # LEXICAL grounding. Both existing arms operate on SENTENCES; this one\n", " # operates on words and their glosses. 156k term<->gloss pairs, 42%\n", " # carrying a hard negative that is a DIFFERENT SENSE OF THE SAME TERM\n", " # (\"float\": water current vs cheque clearing). Sense disambiguation is\n", " # taught by nothing else in the stack.\n", " (\"lexical\", {\"kind\": \"wordnet\"}),\n", " # topical relatedness (citation graph), not equivalence.\n", " (\"topical\", {\"kind\": \"data\",\n", " \"sources\": ((\"sentence-transformers/specter\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 200_000),)}),\n", " # NEGATIVE CONTROL. Untrained, never sampled during alignment, no data.\n", " # Under the all-anchor damped denominator every arm adds cosh() to z,\n", " # so a useless arm is not free -- it taxes the others. If the dispatch\n", " # routes around it and the score holds, the router can REJECT a bad\n", " # expert, which is what \"robust to more arms\" has to mean. If it\n", " # degrades, arm-count scaling is unsafe and every future arm is a\n", " # liability. Costs zero training time.\n", " (\"random\", {\"kind\": \"random\"}),\n", " )\n", " wordnet_max: int = 156_000\n", " dedup_jaccard: float = 0.95\n", "\n", " # ---- greedy forward selection over arm subsets ----\n", " # The dilution law makes arm count an empirical question, not a design\n", " # choice: solving back from the measured .62/.23 gives implied u 1.668/0.844,\n", " # and adding arms conserves the SUM of |w/z| (.85 -> .81 -> .79 -> .76 for\n", " # A=2..6) while splitting it. At A=4 the current arms lose ~40% of their\n", " # weight. And tau=0.02 (sharper) MEASURED WORSE, so we cannot sharpen out of\n", " # it. Start from the known-good pair and add one arm at a time, keeping a\n", " # candidate only if it earns its dilution.\n", " base_arms: tuple = (\"equiv\", \"simplify\")\n", " greedy: bool = True\n", " greedy_rounds: int = 4 # up to A=6\n", " select_on: str = \"STS-B\" # selection gauge -- see the caveat printed\n", "\n", " # anchor spec -- certified campaign defaults, untouched\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # ---- stage A/B: anchor training ----\n", " # 1000 not 4000: the solo run's STS-B PEAKED at step 1,000 and then fell\n", " # .0178 over the remaining 3,000 while SICK-R kept climbing -- the\n", " # question-space warning cashing out. Do not re-buy that decline.\n", " anchor_steps: int = 1500\n", " anchor_lr: float = 1e-3\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " max_tokens: int = 64\n", "\n", " # ---- stage C: dispatch alignment (keys only) ----\n", " align_steps: int = 800\n", " align_lr: float = 1e-3\n", " align_emb: int = 64\n", " check_every: int = 200 # starvation check cadence\n", " usage_ppl_floor: float = 1.5 # transplanted from AlignConfig\n", " usage_min: float = 0.02\n", " max_strikes: int = 3\n", "\n", " # ---- FIX 1: MIXED BATCHES ----\n", " # The first run sampled ONE stream per step, so every batch was homogeneous\n", " # and the router only ever saw the two distributions alternately. Its log\n", " # ended on FIVE consecutive src=equiv steps while usage drifted .49 -> .65,\n", " # so the measured .645/.223 separation may be RECENCY rather than learning.\n", " # Sampling per ROW puts both distributions in one forward, which is a direct\n", " # separation signal. mix_rows=False reproduces the old behaviour for\n", " # comparison.\n", " mix_rows: bool = True\n", "\n", " # ---- FIX 2: TAU SWEEP ----\n", " # tau is the scaling knob, not anchor count. w/z = sinh(u_k)/SUM_j cosh(u_j)\n", " # with u = cos/tau, so lower tau saturates the selected anchor faster than\n", " # the others and z stops growing with A. At the measured key separation\n", " # (cos ~.62 vs ~.30): tau .10 -> w/z .961/.039 ; .05 -> .998/.002 ;\n", " # .02 -> 1.000/.000. Alignment is ~4 min, so sweeping is nearly free and it\n", " # says how many experts this dispatch could carry before diluting.\n", " # tau=0.10 WON the sweep: separation went up with sharper tau but STS-B went\n", " # DOWN monotonically on both seeds (.7520/.7493 -> .7470/.7514 -> .7413/.7468).\n", " # The value is in GRADED blending, not selection. Fixed here.\n", " align_taus: tuple = (0.10,)\n", "\n", " # ---- FIX 3: SEED CONFIRMATION ----\n", " # Everything so far is n=1. Re-aligning under a second seed (anchors reused,\n", " # so it costs only the 4 min alignment) says whether the separation is real.\n", " align_seeds: tuple = (0,) # seed spread measured at .003-.005; 1 is enough per cell\n", "\n", " # ---- FIX 4: PER-BLOCK ROUTING TELEMETRY ----\n", " # 12 dispatches, one per block, and nobody has looked at whether they route\n", " # DIFFERENTLY. If all 12 learned the same thing, 11 of them are redundant.\n", " per_block_report: bool = True\n", "\n", " seed: int = 0\n", " log_every: int = 100\n", " eval_every: int = 500\n", " reuse_anchors: bool = True # skip stage A/B if the .pt files exist\n", "\n", " out_dir: str = \"/content/amoe_collective\" # MUST be unique per run:\n", " # the combo run reused the solo run's out_dir and its folder push\n", " # swept the wrong anchor into amoe/sts-combo. One run, one folder.\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " hf_path: str = \"amoe/collective\"\n", " hf_private: bool = False\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "CARD = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, mixture-of-experts, sentence-similarity, captionbert, aleph]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "library_name: amoe-lora\n", "---\n", "\n", "# captionbert-8192-v2 :: AMOE 2-anchor mixture\n", "\n", "Two [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchors on the\n", "**frozen** trunk, plus a trained dispatch over them. The trunk never moves.\n", "\n", "| anchor | trained on | relation |\n", "|---|---|---|\n", "| `equiv` | all-nli triplets | semantic equivalence |\n", "| `simplify` | simple-wiki + altlex + sentence-compression | simplification / compression |\n", "\n", "## Results\n", "\n", "| config | STS-B rho | SICK-R rho |\n", "|---|---|---|\n", "| bare trunk | .5747 | .6526 |\n", "| `equiv` alone | .7254 | **.7550** |\n", "| `simplify` alone | .7400 | .7075 |\n", "| **2-anchor dispatch** | **.7524** | .7380 |\n", "\n", "The two are complementary along the TASK axis -- `simplify` wins STS-B solo,\n", "`equiv` wins SICK-R -- which is the precondition a mixture needs. SICK-R is\n", "never trained on and is the honest transfer read.\n", "\n", "## Why the dispatch works here\n", "\n", "Dispatched amplitude is `(w_k/z) * sigmoid(gate_k) * consume_k(x)`, where\n", "`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` over ALL anchors (the damping law).\n", "That gives ~1.0 only when one anchor engages and the other **abstains**\n", "(`u ~ 0`); if both fire it collapses to ~0.5 and the mixture delivers HALF of\n", "what either member does alone.\n", "\n", "Measured here: mean `|w/z|` moved from **.310/.380 (blend)** before alignment to\n", "**.645/.223 (specialize)** after 800 keys-only steps, with no starvation\n", "strikes. That flip is why the mixture beats its best member rather than damping\n", "itself below it.\n", "\n", "## Load\n", "\n", "```python\n", "import amoe\n", "h = amoe.attach(trunk, [\"amoe/moe/equiv.anchor.pt\", \"amoe/moe/simplify.anchor.pt\"],\n", " dispatch=\"amoe/moe/captionbert-v2-moe.dispatch.pt\",\n", " binding=CaptionBertV2Binding(d=512)) # from modeling_captionbert.py\n", "base = h.detach() # bit-exact or raises\n", "```\n", "\n", "All anchors disabled reproduces the bare trunk **bit-exact** (asserted at build\n", "time). Masking never renormalizes -- that is the damping law, not an oversight.\n", "\n", "Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the\n", "amoe README is the *safetensors* layout, a different serializer.)\n", "\n", "## Training\n", "\n", "Anchors: MNRL, in-batch + hard negatives where the source has them, 1,500 steps\n", "at batch 256, pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32/TF32 off.\n", "1,500 not 4,000: the first solo run's STS-B **peaked at step 1,000** and then\n", "fell .0178 while SICK-R kept climbing -- 56,825 distinct anchors behind 200,000\n", "draws (space/draws .284). Dispatch: 800 steps, routing keys only (1,536 params),\n", "anchors frozen, starvation safeguard armed.\n", "\n", "See `metrics.json` for the full table and the routing telemetry.\n", "\"\"\"\n", "\n", "\n", "class Publisher:\n", " \"\"\"Ships the whole out_dir into the trunk repo. Best-effort, never fatal.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok = cfg, None, False\n", " if not cfg.hf_push:\n", " print(\" [publish] hf_push=False -- local only\")\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- LOCAL ONLY. A cull loses the run.\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True, private=cfg.hf_private)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [publish] -> {cfg.hf_repo}/{cfg.hf_path}\")\n", " except Exception as e:\n", " print(f\" [publish] disabled: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", " def push(self, msg=\"amoe moe\"):\n", " if not self.ok:\n", " return\n", " try:\n", " self.api.upload_folder(folder_path=self.cfg.out_dir,\n", " path_in_repo=self.cfg.hf_path,\n", " repo_id=self.cfg.hf_repo, commit_message=msg)\n", " print(f\" [publish] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] push failed: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\"):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", " self.config = SimpleNamespace(hidden_size=d_model,\n", " _name_or_path=\"AbstractPhil/captionbert-8192-v2\",\n", " model_type=\"captionbert\")\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class CaptionEncoderBinding:\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def fresh_trunk(cfg) -> nn.Module:\n", " m = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " m.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _jaccard(x, y):\n", " a, b = set(x.lower().split()), set(y.lower().split())\n", " return len(a & b) / max(len(a | b), 1)\n", "\n", "\n", "def load_expert_data(cfg, spec_list, tag):\n", " A, P, N = [], [], []\n", " for repo, conf, ka, kp, kn, cap in spec_list:\n", " ds = load_dataset(repo, conf, split=\"train\")\n", " if cap and len(ds) > cap:\n", " ds = ds.select(range(cap))\n", " a, p = list(ds[ka]), list(ds[kp])\n", " n = list(ds[kn]) if kn else [None] * len(a)\n", " k = 0\n", " for x, y, z in zip(a, p, n):\n", " x, y = (x or \"\").strip(), (y or \"\").strip()\n", " if not x or not y or x == y or _jaccard(x, y) >= cfg.dedup_jaccard:\n", " continue\n", " A.append(x); P.append(y); N.append(z); k += 1\n", " print(f\" {repo.split('/')[-1]:28s} {len(ds):>9,d} -> {k:>9,d} kept\")\n", " sp = len(set(A))\n", " print(f\" {'TOTAL ' + tag:28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " return A, P, N\n", "\n", "\n", "def build_wordnet(cfg):\n", " \"\"\"\n", " term <-> gloss, with a hard negative that is a DIFFERENT SENSE OF THE SAME\n", " TERM. Measured yield: 156,040 pairs, 42% with such a negative, 117,683\n", " distinct anchors (space/draws .754). Nothing else in the stack teaches\n", " sense disambiguation.\n", " \"\"\"\n", " import nltk\n", " try:\n", " nltk.data.find(\"corpora/wordnet.zip\")\n", " except LookupError:\n", " nltk.download(\"wordnet\", quiet=True); nltk.download(\"omw-1.4\", quiet=True)\n", " from nltk.corpus import wordnet as wn\n", " A, P, N = [], [], []\n", " for syn in wn.all_synsets():\n", " gloss = syn.definition().strip()\n", " if len(gloss.split()) < 4:\n", " continue\n", " for lem in syn.lemmas()[:2]:\n", " term = lem.name().replace(\"_\", \" \")\n", " if len(term) < 3:\n", " continue\n", " other = [t for t in wn.synsets(lem.name())\n", " if t.name() != syn.name() and len(t.definition().split()) >= 4]\n", " A.append(term); P.append(gloss)\n", " N.append(other[0].definition() if other else None)\n", " idx = np.random.default_rng(0).permutation(len(A))[:cfg.wordnet_max]\n", " A = [A[i] for i in idx]; P = [P[i] for i in idx]; N = [N[i] for i in idx]\n", " hard = sum(1 for z in N if z)\n", " sp = len(set(A))\n", " print(f\" {'wordnet term<->gloss':28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " print(f\" {'':28s} {'':>9s} {hard:>9,d} with a same-term other-sense negative \"\n", " f\"({hard/max(len(A),1)*100:.0f}%)\")\n", " return A, P, N\n", "\n", "\n", "def arm_stream(cfg, name, spec):\n", " \"\"\"Training data for one arm. `random` has none -- that is the control.\"\"\"\n", " kind = spec[\"kind\"]\n", " if kind == \"random\":\n", " return None\n", " if kind == \"wordnet\":\n", " return build_wordnet(cfg)\n", " return load_expert_data(cfg, spec[\"sources\"], name)\n", "\n", "\n", "def resolve_arm(cfg, name, spec, tok, tasks):\n", " \"\"\"Return a path to this arm's anchor, training or downloading as needed.\"\"\"\n", " local = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " if os.path.exists(local):\n", " print(f\" {name}: local, reusing {local}\")\n", " return local\n", " if spec[\"kind\"] == \"hub\":\n", " # REUSE: frozen anchors, keys-only dispatch, and both prior runs produced\n", " # byte-identical files. Retraining would only reproduce them.\n", " src = hf_hub_download(cfg.trunk_repo, spec[\"path\"])\n", " shutil.copy(src, local)\n", " print(f\" {name}: reused from {cfg.trunk_repo}/{spec['path']}\")\n", " return local\n", " if spec[\"kind\"] == \"random\":\n", " # untrained anchor: zero_init_head + gate -3.0, exactly as a fresh one\n", " torch.manual_seed(cfg.seed + 999)\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init,\n", " zero_init_head=True)\n", " ws = [RelayPatchwork(cfg.d_model, aspec) for _ in range(cfg.n_layers)]\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(ws) for k, v in w.state_dict().items()}\n", " AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": cfg.n_layers,\n", " \"task\": \"NEGATIVE CONTROL -- untrained, never sampled\"}).save(local)\n", " print(f\" {name}: UNTRAINED control anchor written (no data, no steps)\")\n", " return local\n", " return train_anchor_from(cfg, name, spec, tok, tasks)\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " return torch.cat([model(*make_batch(tok, texts[i:i + bs], cfg)).float().cpu()\n", " for i in range(0, len(texts), bs)])\n", "\n", "\n", "@torch.no_grad()\n", "def sts_eval(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb]); n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T; S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "TASK_SPECS = [\n", " (\"STS-B\", \"mteb/stsbenchmark-sts\"), # selection gauge\n", " (\"SICK-R\", \"mteb/sickr-sts\"), # clean, never trained on\n", " (\"STS12\", \"mteb/sts12-sts\"),\n", " (\"STS13\", \"mteb/sts13-sts\"),\n", " (\"STS14\", \"mteb/sts14-sts\"),\n", " (\"STS15\", \"mteb/sts15-sts\"),\n", " (\"STS16\", \"mteb/sts16-sts\"),\n", " # BIOSSES is 100 rows of BIOMEDICAL sentence pairs. Everything else here is\n", " # English web/news/caption text -- i.e. in-distribution for a CC12M trunk.\n", " # This is the only genuine out-of-domain transfer read in the suite, and it\n", " # is the one to watch when arms are added.\n", " (\"BIOSSES\", \"mteb/biosses-sts\"),\n", "]\n", "\n", "\n", "def load_tasks():\n", " out = {}\n", " for nm, path in TASK_SPECS:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " c = d.column_names\n", " a = \"sentence1\" if \"sentence1\" in c else c[0]\n", " b = \"sentence2\" if \"sentence2\" in c else c[1]\n", " sc = \"score\" if \"score\" in c else \"similarity_score\"\n", " out[nm] = (list(d[a]), list(d[b]), np.asarray(d[sc], dtype=float))\n", " print(f\" {nm}: {len(out[nm][2])} pairs\")\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " B = ea.shape[0]\n", " cand = ep if en is None or en.shape[0] == 0 else torch.cat([ep, en], 0)\n", " logits = (ea @ cand.T) / temperature\n", " labels = torch.arange(B, device=ea.device)\n", " loss = F.cross_entropy(logits, labels)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE A/B -- train one anchor\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_anchor_from(cfg, name, spec, tok, tasks):\n", " path = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " line(f\"ANCHOR '{name}' (training)\")\n", " model = fresh_trunk(cfg)\n", " A, P, N = arm_stream(cfg, name, spec)\n", "\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " adapters, new, wrapped = nn.ModuleList(), list(layers), []\n", " for i in range(len(layers)):\n", " a = RelayPatchwork(cfg.d_model, aspec).to(DEVICE)\n", " adapters.append(a)\n", " blk = BlockWithAdapter(layers[i], a); new[i] = blk; wrapped.append(blk)\n", " b.set_layers(model, new)\n", " assert sum(p.numel() for p in model.parameters() if p.requires_grad) == \\\n", " sum(p.numel() for p in adapters.parameters()), \"trunk not frozen\"\n", "\n", " opt = laws.make_optimizer(adapters.parameters(), cfg.anchor_lr)\n", " sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.anchor_steps,\n", " eta_min=1e-6)\n", " g = np.random.default_rng(cfg.seed)\n", " t0 = time.time(); model.train()\n", " for step in range(1, cfg.anchor_steps + 1):\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward()\n", " torch.nn.utils.clip_grad_norm_(adapters.parameters(), 1.0)\n", " opt.step(); sch.step()\n", " if step % cfg.log_every == 0:\n", " gates = torch.stack([torch.sigmoid(w.adapter.gate) for w in wrapped]).detach()\n", " drs = [w.adapter.addr.drift() for w in wrapped]\n", " print(f\" {name} {step:>5,}/{cfg.anchor_steps:,} loss {loss.item():.4f} \"\n", " f\"acc {acc:.3f} gate {gates.mean():.4f} \"\n", " f\"drift {sum(drs)/len(drs):.4f}rad {(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.eval_every == 0 or step == cfg.anchor_steps:\n", " r = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " print(\" \" + \" \".join(f\"{k} {v['spearman']:.4f} (er {v['erank']:.1f})\"\n", " for k, v in r.items()))\n", " model.train()\n", "\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(wrapped) for k, v in w.adapter.state_dict().items()}\n", " assert all(f\"{i}.addr.home\" in flat for i in range(len(wrapped)))\n", " ck = AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": len(wrapped),\n", " \"data\": ([f\"{r}[{c}]\" for r, c, *_ in spec.get(\"sources\", ())]\n", " or spec[\"kind\"]),\n", " \"steps\": cfg.anchor_steps, \"task\": \"sentence-similarity\"})\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " h = ck.save(path)\n", " print(f\" saved {path} {h[:18]}\")\n", " del model, adapters\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " return path\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE C -- align the dispatch (KEYS ONLY)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def build_moe(cfg, anchor_paths, tau):\n", " \"\"\"attach both anchors with an untrained dispatch. Mirrors align()'s setup.\"\"\"\n", " model = fresh_trunk(cfg)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " cks = [load_anchor(p) for p in anchor_paths]\n", " names = [c.meta.get(\"name\", f\"anchor{i}\") for i, c in enumerate(cks)]\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " new, disps = list(layers), []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for c in cks:\n", " a = RelayPatchwork(cfg.d_model, AdapterSpec(\n", " n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in c.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for p in a.parameters():\n", " p.requires_grad_(False) # keys-only law\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=cfg.align_emb, tau=tau).to(DEVICE)\n", " disps.append(dp)\n", " new[i] = BlockWithDispatch(layer, dp)\n", " b.set_layers(model, new)\n", " trainable = [dp.dispatch for dp in disps]\n", " n_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(names)} anchors x {len(layers)} sites | routing keys \"\n", " f\"{sum(p.numel() for p in trainable):,} trainable | frozen elsewhere: \"\n", " f\"{n_tr == sum(p.numel() for p in trainable)}\")\n", " return model, disps, names\n", "\n", "\n", "def set_active(disps, mask):\n", " for dp in disps:\n", " dp.enabled = list(mask)\n", "\n", "\n", "@torch.no_grad()\n", "def per_block_routing(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor AT EACH BLOCK. 12 dispatches route independently and\n", " nobody has checked whether they learned different things. If every block\n", " reports the same split, 11 of the 12 are redundant and the routing could\n", " live in one shared dispatch.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " per.append(torch.stack(dp.rec).mean(0) if dp.rec else None)\n", " dp.rec = None\n", " rows = [p for p in per if p is not None]\n", " if not rows:\n", " return None\n", " M = torch.stack(rows) # (blocks, anchors)\n", " print(f\" {'block':>6s}\" + \"\".join(f\"{n:>12s}\" for n in names))\n", " for i, r in enumerate(M):\n", " print(f\" {i:>6d}\" + \"\".join(f\"{float(v):>12.4f}\" for v in r))\n", " spread = float((M.max(0).values - M.min(0).values).max())\n", " print(f\" max across-block spread for any anchor: {spread:.4f}\")\n", " print(\" <0.05 => all blocks route alike (11 dispatches redundant);\"\n", " \" >0.15 => depth-dependent routing is real\")\n", " return {\"per_block\": M.tolist(), \"spread\": spread}\n", "\n", "\n", "@torch.no_grad()\n", "def route_telemetry(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor -- the blend-escape gauge, and the viability test.\n", " ~1.0 for one anchor and ~0 for the other means SPECIALIZE (full amplitude).\n", " Both near 0.5 means BLEND: the MOE is damping itself to half of what either\n", " anchor delivers alone, on top of a gate that is already ~0.09.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " if dp.rec:\n", " per.append(torch.stack(dp.rec).mean(0))\n", " dp.rec = None\n", " if not per:\n", " return None\n", " w = torch.stack(per).mean(0)\n", " return {n: float(v) for n, v in zip(names, w)}\n", "\n", "\n", "def align_dispatch(cfg, model, disps, names, streams, tok, tasks):\n", " line(\"STAGE C -- ALIGN DISPATCH (keys only, anchors frozen)\")\n", " trainable = [dp.dispatch for dp in disps]\n", " opt = laws.make_optimizer(trainable, cfg.align_lr)\n", " # A control arm has NO stream. It must never be sampled, and the starvation\n", " # safeguard must not try to rescue it -- starving is the correct outcome and\n", " # the whole point of the control.\n", " fed = [n for n in names if n in streams and streams[n] is not None]\n", " ctrl = [n for n in names if n not in fed]\n", " if ctrl:\n", " print(f\" control arms (no stream, exempt from starvation rescue): {ctrl}\")\n", " weights = {n: 1.0 for n in fed}\n", " g = np.random.default_rng(cfg.seed + 7)\n", " strikes, alarms = 0, []\n", " t0 = time.time(); model.train()\n", "\n", " for step in range(1, cfg.align_steps + 1):\n", " p = np.array([weights[n] for n in fed], dtype=float); p /= p.sum()\n", " if cfg.mix_rows:\n", " # PER-ROW sampling: one batch contains BOTH distributions, so the\n", " # router gets a direct separation signal instead of alternating\n", " # homogeneous batches (and the final state cannot be an artifact of\n", " # whichever stream happened to be drawn last).\n", " pick = g.choice(len(fed), size=cfg.batch_size, p=p)\n", " rows = [(fed[k], int(g.integers(0, len(streams[fed[k]][0]))))\n", " for k in pick]\n", " A_b = [streams[nm][0][i] for nm, i in rows]\n", " P_b = [streams[nm][1][i] for nm, i in rows]\n", " N_b = [streams[nm][2][i] for nm, i in rows]\n", " nm = f\"mix({'/'.join(f'{int((pick==k).sum())}' for k in range(len(fed)))})\"\n", " else:\n", " nm = fed[int(g.choice(len(fed), p=p))]\n", " A, P, N = streams[nm]\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " A_b = [A[i] for i in idx]; P_b = [P[i] for i in idx]\n", " N_b = [N[i] for i in idx]\n", " ea = model(*make_batch(tok, A_b, cfg))\n", " ep = model(*make_batch(tok, P_b, cfg))\n", " negs = [z for z in N_b if z]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\n", "\n", " if step % cfg.log_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " use = (cnt / cnt.sum()).tolist()\n", " print(f\" align {step:>5,}/{cfg.align_steps:,} src={nm:9s} \"\n", " f\"loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"usage {[f'{u:.2f}' for u in use]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.check_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " u = (cnt / cnt.sum()).clamp(min=1e-9)\n", " ent = float(torch.exp(-(u * u.log()).sum()))\n", " fed_idx = [names.index(n) for n in fed]\n", " uf = u[fed_idx]\n", " entf = float(torch.exp(-(uf / uf.sum() * (uf / uf.sum()).log()).sum()))\n", " if entf < cfg.usage_ppl_floor or float(uf.min()) < cfg.usage_min:\n", " strikes += 1\n", " starved = fed[int(uf.argmin())] # only ever a FED arm\n", " weights[starved] *= 2.0\n", " alarms.append({\"step\": step, \"ppl\": entf, \"starved\": starved,\n", " \"usage\": u.tolist()})\n", " print(f\" !! STARVATION strike {strikes}/{cfg.max_strikes}: \"\n", " f\"fed-arm usage-ppl {entf:.3f} < {cfg.usage_ppl_floor}, \"\n", " f\"'{starved}' at {float(uf.min()):.3f} -> weight \"\n", " f\"{weights[starved]:.1f}\")\n", " if strikes >= cfg.max_strikes:\n", " print(\" !! max strikes -- the dispatch is not separating these\")\n", " print(\" !! two anchors. Stopping alignment; read the MOE row as\")\n", " print(\" !! a collapsed router, not a mixture.\")\n", " break\n", " model.train()\n", " return alarms\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 76)\n", " print(f\"{cfg.run_name.upper()} -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision()\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " line(\"PUBLISH TARGET\")\n", " pub = Publisher(cfg)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks()\n", "\n", " line(\"BASELINE (bare trunk)\")\n", " base_model = fresh_trunk(cfg)\n", " base = {k: sts_eval(base_model, tok, v, cfg) for k, v in tasks.items()}\n", " for k, v in base.items():\n", " print(f\" {k:8s} rho {v['spearman']:.4f} erank {v['erank']:.1f}\")\n", " del base_model\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- stages A and B ----\n", " # ---- resolve every arm: reuse, train, or write the control ----\n", " line(\"ARMS\")\n", " paths, streams = {}, {}\n", " for name, spec in cfg.arms:\n", " paths[name] = resolve_arm(cfg, name, spec, tok, tasks)\n", " pub.push(f\"arm {name}\")\n", " line(\"STREAMS\")\n", " for name, spec in cfg.arms:\n", " streams[name] = arm_stream(cfg, name, spec)\n", " if streams[name] is None:\n", " print(f\" {name}: NO STREAM (negative control)\")\n", "\n", " task0 = cfg.select_on if cfg.select_on in tasks else list(tasks)[0]\n", " tau = cfg.align_taus[0]\n", " sd = cfg.align_seeds[0]\n", "\n", " def evaluate_subset(subset, tag=\"\"):\n", " torch.manual_seed(sd)\n", " mdl, dsp, nms = build_moe(cfg, [paths[n] for n in subset], tau)\n", " sub_streams = {n: streams[n] for n in subset}\n", " probe = tasks[task0][0]\n", " t0_ = route_telemetry(mdl, tok, probe, cfg, dsp, nms)\n", " al = align_dispatch(replace(cfg, seed=sd), mdl, dsp, nms,\n", " sub_streams, tok, tasks)\n", " t1_ = route_telemetry(mdl, tok, probe, cfg, dsp, nms)\n", " set_active(dsp, [True] * len(nms))\n", " full = {k: sts_eval(mdl, tok, v, cfg) for k, v in tasks.items()}\n", " singles = {}\n", " for i2, n2 in enumerate(nms):\n", " set_active(dsp, [j2 == i2 for j2 in range(len(nms))])\n", " singles[n2] = sts_eval(mdl, tok, tasks[task0], cfg)[\"spearman\"]\n", " set_active(dsp, [False] * len(nms))\n", " off = sts_eval(mdl, tok, tasks[task0], cfg)[\"spearman\"]\n", " set_active(dsp, [True] * len(nms))\n", " return {\"subset\": list(subset), \"tau\": tau, \"seed\": sd,\n", " \"tel_before\": t0_, \"tel_after\": t1_, \"full\": full,\n", " \"singles\": singles, \"off\": off, \"alarms\": al,\n", " \"score\": full[task0][\"spearman\"],\n", " \"model\": mdl, \"disps\": dsp, \"names\": nms}\n", "\n", " # ---- greedy forward selection ----\n", " line(\"GREEDY FORWARD SELECTION\")\n", " print(f\" selection gauge: {task0}. Start from {list(cfg.base_arms)} and add\")\n", " print(f\" ONE arm per round, keeping it only if it earns its dilution.\")\n", " print(f\" Dilution is not optional: every arm adds cosh() to z, so the SUM of\")\n", " print(f\" |w/z| is roughly conserved (.85 at A=2 -> .76 at A=6) while each\")\n", " print(f\" arm's share falls. tau=0.02 measured WORSE, so sharpening is out.\")\n", " print(f\" !! {task0} is the SELECTION criterion -- treat its final value as\")\n", " print(f\" !! optimistic. SICK-R and BIOSSES are never selected on and are the\")\n", " print(f\" !! clean reads.\")\n", "\n", " cur = list(cfg.base_arms)\n", " remaining = [n for n, _ in cfg.arms if n not in cur]\n", " line(f\"round 0: base {cur}\")\n", " best = evaluate_subset(cur, \"base\")\n", " history = [{k: v for k, v in best.items() if k not in (\"model\", \"disps\", \"names\")}]\n", " print(f\" base {task0} {best['score']:.4f} | \"\n", " f\"|w/z| {dict((k, round(v,3)) for k,v in best['tel_after'].items())}\")\n", "\n", " for rnd in range(1, cfg.greedy_rounds + 1):\n", " if not remaining:\n", " break\n", " line(f\"round {rnd}: testing {remaining} on top of {cur}\")\n", " trials = []\n", " for cand in remaining:\n", " r = evaluate_subset(cur + [cand], cand)\n", " gain = r[\"score\"] - best[\"score\"]\n", " ctrl = \" [CONTROL]\" if dict(cfg.arms)[cand][\"kind\"] == \"random\" else \"\"\n", " print(f\" +{cand:11s}{ctrl:11s} {task0} {r['score']:.4f} ({gain:+.4f}) \"\n", " f\"|w/z| {dict((k, round(v,3)) for k,v in r['tel_after'].items())}\")\n", " trials.append((r[\"score\"], cand, r))\n", " history.append({k: v for k, v in r.items()\n", " if k not in (\"model\", \"disps\", \"names\")})\n", " if r is not best:\n", " del r[\"model\"], r[\"disps\"]\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " trials.sort(key=lambda t: -t[0])\n", " top_score, top_name, top_r = trials[0]\n", " if top_score <= best[\"score\"]:\n", " print(f\" => no candidate beat {best['score']:.4f}. STOPPING at A={len(cur)}.\")\n", " print(f\" The dilution law bit: more arms now cost more than they add.\")\n", " break\n", " print(f\" => keeping '{top_name}' (+{top_score-best['score']:.4f})\")\n", " for k in (\"model\", \"disps\"):\n", " if k in best:\n", " del best[k]\n", " best = evaluate_subset(cur + [top_name], top_name)\n", " cur = cur + [top_name]\n", " remaining = [n for n in remaining if n != top_name]\n", "\n", " # ---- the control question, answered explicitly ----\n", " line(\"NEGATIVE CONTROL\")\n", " ctrl_names = [n for n, sp in cfg.arms if sp[\"kind\"] == \"random\"]\n", " for cn in ctrl_names:\n", " with_ctrl = [h for h in history if cn in h[\"subset\"]\n", " and len(h[\"subset\"]) == len(cfg.base_arms) + 1]\n", " if with_ctrl:\n", " w = with_ctrl[0]\n", " base_s = history[0][\"score\"]\n", " print(f\" base {base_s:.4f} -> +{cn} {w['score']:.4f} ({w['score']-base_s:+.4f})\")\n", " print(f\" its routed weight: {w['tel_after'].get(cn, float('nan')):.4f} \"\n", " f\"(others {[round(v,3) for k,v in w['tel_after'].items() if k!=cn]})\")\n", " if w[\"score\"] >= base_s - 0.005:\n", " print(\" => the dispatch ROUTED AROUND an untrained arm. It can reject a\")\n", " print(\" useless expert, so arm-count scaling is safe to attempt.\")\n", " else:\n", " print(\" => an untrained arm COSTS real score. The router cannot reject\")\n", " print(\" noise, and every future arm is a liability, not an option.\")\n", " if cn in cur:\n", " print(f\" !! '{cn}' was SELECTED by greedy search. That means the gauge is\")\n", " print(f\" !! noisier than the gains being chased -- treat the whole table as\")\n", " print(f\" !! within noise.\")\n", "\n", " model, disps, names = best[\"model\"], best[\"disps\"], best[\"names\"]\n", " rows, tel, tel_after, alarms = None, best[\"tel_before\"], best[\"tel_after\"], best[\"alarms\"]\n", "\n", " # ---- full suite on the winner ----\n", " line(f\"FULL SUITE -- winner {cur}\")\n", " rows = {}\n", " masks = [(\"OFF\", [False] * len(names))] + \\\n", " [(f\"{n}-only\", [j2 == i for j2 in range(len(names))])\n", " for i, n in enumerate(names)] + \\\n", " [(\"COLLECTIVE\", [True] * len(names))]\n", " for label, mask in masks:\n", " set_active(disps, mask)\n", " rows[label] = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " set_active(disps, [True] * len(names))\n", " tk = list(tasks)\n", " print(f\" {'config':14s}\" + \"\".join(f\"{t:>9s}\" for t in tk) + f\"{'mean':>9s}\")\n", " for label, _ in masks:\n", " vals = [rows[label][t][\"spearman\"] for t in tk]\n", " print(f\" {label:14s}\" + \"\".join(f\"{v:>9.4f}\" for v in vals)\n", " + f\"{np.mean(vals):>9.4f}\")\n", " print(f\"\\n |w/z| final {dict((k, round(v,4)) for k,v in tel_after.items())}\")\n", " clean = [t for t in tk if t not in (task0,)]\n", " print(f\" clean-gauge mean (excludes the selection gauge {task0}): \"\n", " f\"{np.mean([rows['COLLECTIVE'][t]['spearman'] for t in clean]):.4f}\")\n", "\n", " pbr = None\n", " if cfg.per_block_report:\n", " line(\"PER-BLOCK ROUTING\")\n", " pbr = per_block_routing(model, tok, tasks[task0][0], cfg, disps, names)\n", "\n", " sweep = history\n", " best_cell = {\"subset\": cur, \"tau\": tau, \"seed\": sd}\n", " base = {k: {\"spearman\": rows[\"OFF\"][k][\"spearman\"]} for k in tasks}\n", "\n", " # ---- ship ----\n", " line(\"SAVE\")\n", " dck = DispatchCheckpoint(dispatch=[{\"dispatch\": dp.dispatch.detach().cpu(),\n", " \"key_proj\": dp.key_proj.detach().cpu()}\n", " for dp in disps],\n", " meta={\"name\": cfg.run_name, \"anchors\": names,\n", " \"base_model_id\": cfg.trunk_repo,\n", " \"emb\": cfg.align_emb, \"tau\": best[\"tau\"],\n", " \"seed\": best[\"seed\"], \"mix_rows\": cfg.mix_rows,\n", " \"alarms\": alarms})\n", " dpath = os.path.join(cfg.out_dir, f\"{cfg.run_name}.dispatch.pt\")\n", " dck.save(dpath)\n", " json.dump({\"baseline\": base, \"rows\": rows, \"telemetry_before\": tel,\n", " \"telemetry_after\": tel_after, \"alarms\": alarms,\n", " \"sweep\": [{k: v for k, v in r.items() if k != \"rows\"} for r in sweep],\n", " \"best\": best_cell,\n", " \"per_block_routing\": pbr,\n", " \"anchors\": {k: os.path.basename(v) for k, v in paths.items()},\n", " \"config\": asdict(cfg)},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " json.dump(asdict(cfg), open(os.path.join(cfg.out_dir, \"config.json\"), \"w\"),\n", " indent=2, default=str)\n", " open(os.path.join(cfg.out_dir, \"README.md\"), \"w\",\n", " encoding=\"utf-8\", newline=\"\\n\").write(CARD)\n", " keep = {os.path.basename(q) for q in paths.values()}\n", " stray = [f for f in os.listdir(cfg.out_dir)\n", " if f.endswith(\".pt\") and f not in keep and not f.endswith(\".dispatch.pt\")]\n", " if stray:\n", " print(f\" !! {stray} in out_dir but not part of this run -- upload_folder\")\n", " print(f\" !! ships the WHOLE folder, so these would land in {cfg.hf_path}.\")\n", " print(f\" !! (this is exactly how amoe/sts-combo got the solo anchor.)\")\n", " print(f\" {dpath}\")\n", " print(f\" anchors: {', '.join(sorted(keep))}\")\n", " for f in sorted(os.listdir(cfg.out_dir)):\n", " fp = os.path.join(cfg.out_dir, f)\n", " if os.path.isfile(fp):\n", " print(f\" {f:44s} {os.path.getsize(fp)/1e6:>7.2f} MB\")\n", " pub.push(\"final: 2 anchors + dispatch + card\")\n", " if pub.ok:\n", " print(f\" https://huggingface.co/{cfg.hf_repo}/tree/main/{cfg.hf_path}\")\n", " return model, disps, names, rows\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " MODEL, DISPS, NAMES, ROWS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0a80b0c429444d98a749137436b6abcf", "0dc705cc86d1492d98cc8991eaac5d0a", "7bd2ea18df534f82a92dd9fecf7dd9a6", "308f9f4abd434444833bd6787cd47dc6", "001d6ff7433e43fb86ce98f1cebc0ee1", "30f8c26dac6b457086f553a45b82fbd5", "3c4ca527266b4d479c1451945c594b17", 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"============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- PUBLISH TARGET ----------------------------------------------------------\n", " [publish] -> AbstractPhil/captionbert-8192-v2/amoe/collective\n", "-- TASKS -------------------------------------------------------------------\n", " STS-B: 1379 pairs\n", " SICK-R: 9927 pairs\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/7.05k [00:00 101,364 kept\n", " TOTAL paraphrase 101,364 rows | 59,877 distinct | space/draws 0.591\n", " paraphrase 100/1,500 loss 0.4320 acc 0.855 gate 0.0514 drift 0.0503rad 0m\n", " paraphrase 200/1,500 loss 0.3517 acc 0.906 gate 0.0540 drift 0.0600rad 1m\n", " paraphrase 300/1,500 loss 0.3564 acc 0.871 gate 0.0562 drift 0.0709rad 1m\n", " paraphrase 400/1,500 loss 0.3968 acc 0.867 gate 0.0580 drift 0.0813rad 2m\n", " paraphrase 500/1,500 loss 0.3761 acc 0.883 gate 0.0598 drift 0.0897rad 2m\n", " STS-B 0.7281 (er 51.8) SICK-R 0.6995 (er 44.8) STS12 0.6331 (er 38.0) STS13 0.7471 (er 38.5) STS14 0.6846 (er 54.3) STS15 0.7865 (er 38.3) STS16 0.7675 (er 50.3) BIOSSES 0.6037 (er 34.8)\n", " paraphrase 600/1,500 loss 0.3146 acc 0.898 gate 0.0612 drift 0.0965rad 3m\n", " paraphrase 700/1,500 loss 0.2569 acc 0.926 gate 0.0625 drift 0.1027rad 3m\n", " paraphrase 800/1,500 loss 0.2773 acc 0.898 gate 0.0635 drift 0.1070rad 3m\n", " paraphrase 900/1,500 loss 0.2803 acc 0.902 gate 0.0645 drift 0.1106rad 4m\n", " paraphrase 1,000/1,500 loss 0.2870 acc 0.879 gate 0.0652 drift 0.1127rad 4m\n", " STS-B 0.7284 (er 55.3) SICK-R 0.7019 (er 44.7) STS12 0.6387 (er 41.9) STS13 0.7463 (er 42.0) STS14 0.6872 (er 59.9) STS15 0.7892 (er 39.4) STS16 0.7654 (er 53.5) BIOSSES 0.5868 (er 35.7)\n", " paraphrase 1,100/1,500 loss 0.3553 acc 0.883 gate 0.0656 drift 0.1146rad 5m\n", " paraphrase 1,200/1,500 loss 0.2001 acc 0.941 gate 0.0659 drift 0.1156rad 5m\n", " paraphrase 1,300/1,500 loss 0.3143 acc 0.883 gate 0.0661 drift 0.1161rad 6m\n", " paraphrase 1,400/1,500 loss 0.2445 acc 0.926 gate 0.0662 drift 0.1163rad 6m\n", " paraphrase 1,500/1,500 loss 0.1948 acc 0.930 gate 0.0662 drift 0.1163rad 6m\n", " STS-B 0.7284 (er 55.2) SICK-R 0.6996 (er 44.5) STS12 0.6408 (er 42.5) STS13 0.7475 (er 42.4) STS14 0.6877 (er 60.3) STS15 0.7887 (er 39.9) STS16 0.7665 (er 53.3) BIOSSES 0.6129 (er 36.2)\n", " saved /content/amoe_collective/paraphrase.anchor.pt sha256:4a4195fe988\n", " [publish] pushed (arm paraphrase)\n", "-- ANCHOR 'lexical' (training) --------------------------------------------\n", " wordnet term<->gloss 156,000 rows | 117,663 distinct | space/draws 0.754\n", " 65,051 with a same-term other-sense negative (42%)\n", " lexical 100/1,500 loss 3.1661 acc 0.371 gate 0.0510 drift 0.0459rad 0m\n", " lexical 200/1,500 loss 2.8000 acc 0.391 gate 0.0550 drift 0.0597rad 1m\n", " lexical 300/1,500 loss 2.7496 acc 0.410 gate 0.0587 drift 0.0683rad 1m\n", " lexical 400/1,500 loss 2.9024 acc 0.387 gate 0.0622 drift 0.0757rad 1m\n", " lexical 500/1,500 loss 3.0049 acc 0.398 gate 0.0653 drift 0.0825rad 1m\n", " STS-B 0.6873 (er 40.8) SICK-R 0.7135 (er 36.9) STS12 0.6170 (er 30.4) STS13 0.6983 (er 23.1) STS14 0.6417 (er 42.2) STS15 0.7659 (er 32.0) STS16 0.7208 (er 35.6) BIOSSES 0.5916 (er 31.4)\n", " lexical 600/1,500 loss 2.4482 acc 0.484 gate 0.0680 drift 0.0884rad 2m\n", " lexical 700/1,500 loss 2.6992 acc 0.445 gate 0.0702 drift 0.0927rad 2m\n", " lexical 800/1,500 loss 2.5480 acc 0.453 gate 0.0723 drift 0.0960rad 2m\n", " lexical 900/1,500 loss 2.8352 acc 0.406 gate 0.0739 drift 0.0984rad 3m\n", " lexical 1,000/1,500 loss 2.6385 acc 0.434 gate 0.0750 drift 0.1002rad 3m\n", " STS-B 0.6927 (er 39.7) SICK-R 0.7162 (er 36.0) STS12 0.6212 (er 30.1) STS13 0.6982 (er 22.1) STS14 0.6432 (er 40.6) STS15 0.7562 (er 31.3) STS16 0.7195 (er 36.1) BIOSSES 0.5849 (er 31.8)\n", " lexical 1,100/1,500 loss 2.4533 acc 0.441 gate 0.0759 drift 0.1014rad 3m\n", " lexical 1,200/1,500 loss 2.3493 acc 0.477 gate 0.0764 drift 0.1019rad 4m\n", " lexical 1,300/1,500 loss 2.5407 acc 0.438 gate 0.0767 drift 0.1023rad 4m\n", " lexical 1,400/1,500 loss 2.5749 acc 0.477 gate 0.0768 drift 0.1024rad 4m\n", " lexical 1,500/1,500 loss 2.5208 acc 0.453 gate 0.0768 drift 0.1024rad 4m\n", " STS-B 0.6953 (er 40.0) SICK-R 0.7199 (er 36.1) STS12 0.6242 (er 30.5) STS13 0.6988 (er 21.9) STS14 0.6448 (er 40.4) STS15 0.7550 (er 31.5) STS16 0.7188 (er 36.1) BIOSSES 0.5802 (er 31.4)\n", " saved /content/amoe_collective/lexical.anchor.pt sha256:ae472ad23d1\n", " [publish] pushed (arm lexical)\n", "-- ANCHOR 'topical' (training) --------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/2.37k [00:00 199,979 kept\n", " TOTAL topical 199,979 rows | 39,979 distinct | space/draws 0.200\n", " topical 100/1,500 loss 4.2915 acc 0.199 gate 0.0525 drift 0.0506rad 0m\n", " topical 200/1,500 loss 4.0855 acc 0.223 gate 0.0558 drift 0.0582rad 1m\n", " topical 300/1,500 loss 3.9001 acc 0.258 gate 0.0593 drift 0.0686rad 1m\n", " topical 400/1,500 loss 3.6027 acc 0.270 gate 0.0627 drift 0.0794rad 2m\n", " topical 500/1,500 loss 3.3923 acc 0.312 gate 0.0657 drift 0.0888rad 2m\n", " STS-B 0.6844 (er 45.4) SICK-R 0.6699 (er 35.6) STS12 0.6462 (er 37.7) STS13 0.7134 (er 32.9) STS14 0.6532 (er 43.5) STS15 0.7534 (er 24.6) STS16 0.7289 (er 42.7) BIOSSES 0.6253 (er 35.5)\n", " topical 600/1,500 loss 3.7216 acc 0.293 gate 0.0685 drift 0.0972rad 2m\n", " topical 700/1,500 loss 3.5537 acc 0.301 gate 0.0710 drift 0.1041rad 3m\n", " topical 800/1,500 loss 3.6138 acc 0.297 gate 0.0731 drift 0.1093rad 3m\n", " topical 900/1,500 loss 3.5760 acc 0.266 gate 0.0749 drift 0.1131rad 4m\n", " topical 1,000/1,500 loss 3.3377 acc 0.309 gate 0.0762 drift 0.1160rad 4m\n", " STS-B 0.6829 (er 44.9) SICK-R 0.6668 (er 36.1) STS12 0.6294 (er 38.7) STS13 0.7104 (er 34.6) STS14 0.6412 (er 43.2) STS15 0.7346 (er 23.3) STS16 0.7149 (er 42.6) BIOSSES 0.5689 (er 34.6)\n", " topical 1,100/1,500 loss 3.0976 acc 0.309 gate 0.0772 drift 0.1177rad 5m\n", " topical 1,200/1,500 loss 3.3540 acc 0.316 gate 0.0778 drift 0.1188rad 5m\n", " topical 1,300/1,500 loss 3.4459 acc 0.328 gate 0.0781 drift 0.1194rad 5m\n", " topical 1,400/1,500 loss 3.7870 acc 0.258 gate 0.0782 drift 0.1196rad 6m\n", " topical 1,500/1,500 loss 3.5037 acc 0.289 gate 0.0782 drift 0.1196rad 6m\n", " STS-B 0.6821 (er 44.7) SICK-R 0.6650 (er 35.6) STS12 0.6244 (er 37.9) STS13 0.7074 (er 35.8) STS14 0.6394 (er 43.3) STS15 0.7321 (er 23.2) STS16 0.7141 (er 43.6) BIOSSES 0.5658 (er 35.2)\n", " saved /content/amoe_collective/topical.anchor.pt sha256:f6306e7cb67\n", " [publish] pushed (arm topical)\n", " random: UNTRAINED control anchor written (no data, no steps)\n", " [publish] pushed (arm random)\n", "-- STREAMS -----------------------------------------------------------------\n", " all-nli 200,000 -> 199,835 kept\n", " TOTAL equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " simple-wiki 102,225 -> 98,377 kept\n", " altlex 112,696 -> 106,619 kept\n", " sentence-compression 180,000 -> 179,998 kept\n", " TOTAL simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", " quora-duplicates 101,762 -> 101,364 kept\n", " TOTAL paraphrase 101,364 rows | 59,877 distinct | space/draws 0.591\n", " wordnet term<->gloss 156,000 rows | 117,663 distinct | space/draws 0.754\n", " 65,051 with a same-term other-sense negative (42%)\n", " specter 200,000 -> 199,979 kept\n", " TOTAL topical 199,979 rows | 39,979 distinct | space/draws 0.200\n", " random: NO STREAM (negative control)\n", "-- GREEDY FORWARD SELECTION ------------------------------------------------\n", " selection gauge: STS-B. Start from ['equiv', 'simplify'] and add\n", " ONE arm per round, keeping it only if it earns its dilution.\n", " Dilution is not optional: every arm adds cosh() to z, so the SUM of\n", " |w/z| is roughly conserved (.85 at A=2 -> .76 at A=6) while each\n", " arm's share falls. tau=0.02 measured WORSE, so sharpening is out.\n", " !! STS-B is the SELECTION criterion -- treat its final value as\n", " !! optimistic. SICK-R and BIOSSES are never selected on and are the\n", " !! clean reads.\n", "-- round 0: base ['equiv', 'simplify'] -------------------------------------\n", " 2 anchors x 12 sites | routing keys 1,536 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(136/120) loss 1.5050 acc 0.676 usage ['0.56', '0.44'] 1m\n", " align 200/800 src=mix(123/133) loss 0.8821 acc 0.762 usage ['0.64', '0.36'] 1m\n", " align 300/800 src=mix(130/126) loss 0.8298 acc 0.809 usage ['0.64', '0.36'] 2m\n", " align 400/800 src=mix(118/138) loss 0.8804 acc 0.773 usage ['0.64', '0.36'] 2m\n", " align 500/800 src=mix(118/138) loss 0.9653 acc 0.762 usage ['0.59', '0.41'] 3m\n", " align 600/800 src=mix(120/136) loss 0.7785 acc 0.816 usage ['0.65', '0.35'] 4m\n", " align 700/800 src=mix(128/128) loss 0.9699 acc 0.758 usage ['0.71', '0.29'] 4m\n", " align 800/800 src=mix(118/138) loss 0.8566 acc 0.793 usage ['0.68', '0.32'] 5m\n", " base STS-B 0.7493 | |w/z| {'equiv': 0.684, 'simplify': 0.191}\n", "-- round 1: testing ['paraphrase', 'lexical', 'topical', 'random'] on top of ['equiv', 'simplify'] \n", " 3 anchors x 12 sites | routing keys 2,304 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(101/68/87) loss 1.3655 acc 0.711 usage ['0.33', '0.34', '0.33'] 1m\n", " align 200/800 src=mix(79/81/96) loss 0.6568 acc 0.828 usage ['0.38', '0.32', '0.30'] 2m\n", " align 300/800 src=mix(93/77/86) loss 0.6833 acc 0.809 usage ['0.46', '0.26', '0.28'] 2m\n", " align 400/800 src=mix(74/90/92) loss 0.6138 acc 0.828 usage ['0.43', '0.27', '0.30'] 3m\n", " align 500/800 src=mix(74/88/94) loss 0.4904 acc 0.875 usage ['0.47', '0.24', '0.29'] 4m\n", " align 600/800 src=mix(81/88/87) loss 0.5877 acc 0.840 usage ['0.47', '0.24', '0.30'] 5m\n", " align 700/800 src=mix(88/89/79) loss 0.7528 acc 0.805 usage ['0.47', '0.24', '0.30'] 5m\n", " align 800/800 src=mix(88/87/81) loss 0.7749 acc 0.816 usage ['0.45', '0.25', '0.30'] 6m\n", " +paraphrase STS-B 0.7684 (+0.0191) |w/z| {'equiv': 0.587, 'simplify': 0.138, 'paraphrase': 0.142}\n", " 3 anchors x 12 sites | routing keys 2,304 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(101/68/87) loss 2.2545 acc 0.559 usage ['0.31', '0.33', '0.36'] 1m\n", " align 200/800 src=mix(79/81/96) loss 1.6212 acc 0.648 usage ['0.39', '0.25', '0.36'] 1m\n", " align 300/800 src=mix(93/77/86) loss 1.3992 acc 0.668 usage ['0.46', '0.20', '0.34'] 2m\n", " align 400/800 src=mix(74/90/92) loss 1.3595 acc 0.668 usage ['0.44', '0.19', '0.37'] 3m\n", " align 500/800 src=mix(74/88/94) loss 1.4440 acc 0.680 usage ['0.46', '0.16', '0.38'] 4m\n", " align 600/800 src=mix(81/88/87) loss 1.4784 acc 0.664 usage ['0.50', '0.15', '0.35'] 4m\n", " align 700/800 src=mix(87/90/79) loss 1.4080 acc 0.652 usage ['0.48', '0.15', '0.37'] 5m\n", " align 800/800 src=mix(88/85/83) loss 1.4068 acc 0.680 usage ['0.40', '0.15', '0.44'] 6m\n", " +lexical STS-B 0.7518 (+0.0025) |w/z| {'equiv': 0.547, 'simplify': 0.127, 'lexical': 0.197}\n", " 3 anchors x 12 sites | routing keys 2,304 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(100/69/87) loss 2.2024 acc 0.543 usage ['0.31', '0.34', '0.35'] 1m\n", " align 200/800 src=mix(79/82/95) loss 1.7176 acc 0.605 usage ['0.33', '0.25', '0.42'] 2m\n", " align 300/800 src=mix(92/77/87) loss 1.6974 acc 0.617 usage ['0.36', '0.21', '0.44'] 2m\n", " align 400/800 src=mix(74/90/92) loss 1.7285 acc 0.617 usage ['0.35', '0.19', '0.46'] 3m\n", " align 500/800 src=mix(74/88/94) loss 1.3110 acc 0.695 usage ['0.35', '0.16', '0.49'] 4m\n", " align 600/800 src=mix(81/88/87) loss 1.3220 acc 0.668 usage ['0.37', '0.17', '0.47'] 5m\n", " align 700/800 src=mix(88/89/79) loss 1.4791 acc 0.637 usage ['0.35', '0.18', '0.47'] 5m\n", " align 800/800 src=mix(88/87/81) loss 1.6863 acc 0.645 usage ['0.39', '0.15', '0.46'] 6m\n", " +topical STS-B 0.7508 (+0.0015) |w/z| {'equiv': 0.533, 'simplify': 0.128, 'topical': 0.224}\n", " 3 anchors x 12 sites | routing keys 2,304 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " control arms (no stream, exempt from starvation rescue): ['random']\n", " align 100/800 src=mix(136/120) loss 1.7022 acc 0.664 usage ['0.36', '0.32', '0.32'] 1m\n", " align 200/800 src=mix(123/133) loss 0.9731 acc 0.742 usage ['0.50', '0.26', '0.24'] 1m\n", " align 300/800 src=mix(130/126) loss 0.8692 acc 0.812 usage ['0.53', '0.25', '0.22'] 2m\n", " align 400/800 src=mix(118/138) loss 0.9508 acc 0.770 usage ['0.54', '0.26', '0.21'] 3m\n", " align 500/800 src=mix(118/138) loss 1.0005 acc 0.762 usage ['0.51', '0.28', '0.21'] 4m\n", " align 600/800 src=mix(120/136) loss 0.8184 acc 0.785 usage ['0.55', '0.25', '0.20'] 4m\n", " align 700/800 src=mix(128/128) loss 1.0461 acc 0.727 usage ['0.61', '0.22', '0.17'] 5m\n", " align 800/800 src=mix(118/138) loss 0.8967 acc 0.777 usage ['0.59', '0.23', '0.18'] 6m\n", " +random [CONTROL] STS-B 0.7538 (+0.0046) |w/z| {'equiv': 0.589, 'simplify': 0.149, 'random': 0.14}\n", " => keeping 'paraphrase' (+0.0191)\n", " 3 anchors x 12 sites | routing keys 2,304 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(101/68/87) loss 1.3655 acc 0.711 usage ['0.33', '0.34', '0.33'] 1m\n", " align 200/800 src=mix(79/81/96) loss 0.6568 acc 0.828 usage ['0.38', '0.32', '0.30'] 2m\n", " align 300/800 src=mix(93/77/86) loss 0.6833 acc 0.809 usage ['0.46', '0.26', '0.28'] 2m\n", " align 400/800 src=mix(74/90/92) loss 0.6138 acc 0.828 usage ['0.43', '0.27', '0.30'] 3m\n", " align 500/800 src=mix(74/88/94) loss 0.4904 acc 0.875 usage ['0.47', '0.24', '0.29'] 4m\n", " align 600/800 src=mix(81/88/87) loss 0.5877 acc 0.840 usage ['0.47', '0.24', '0.30'] 5m\n", " align 700/800 src=mix(88/89/79) loss 0.7528 acc 0.805 usage ['0.47', '0.24', '0.30'] 5m\n", " align 800/800 src=mix(88/87/81) loss 0.7749 acc 0.816 usage ['0.45', '0.25', '0.30'] 6m\n", "-- round 2: testing ['lexical', 'topical', 'random'] on top of ['equiv', 'simplify', 'paraphrase'] \n", " 4 anchors x 12 sites | routing keys 3,072 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(79/57/57/63) loss 1.7961 acc 0.625 usage ['0.26', '0.25', '0.26', '0.23'] 1m\n", " align 200/800 src=mix(66/57/64/69) loss 1.3084 acc 0.684 usage ['0.29', '0.22', '0.24', '0.26'] 2m\n", " align 300/800 src=mix(74/55/61/66) loss 1.0757 acc 0.727 usage ['0.32', '0.19', '0.21', '0.27'] 3m\n", " align 400/800 src=mix(51/66/65/74) loss 1.2725 acc 0.695 usage ['0.30', '0.19', '0.21', '0.30'] 4m\n", " align 500/800 src=mix(49/67/63/77) loss 1.1995 acc 0.699 usage ['0.33', '0.18', '0.20', '0.29'] 5m\n", " align 600/800 src=mix(65/55/66/70) loss 1.1840 acc 0.684 usage ['0.32', '0.19', '0.19', '0.30'] 5m\n", " align 700/800 src=mix(59/70/69/58) loss 1.0670 acc 0.738 usage ['0.31', '0.19', '0.20', '0.30'] 6m\n", " align 800/800 src=mix(70/48/84/54) loss 1.1520 acc 0.730 usage ['0.31', '0.19', '0.20', '0.30'] 7m\n", " +lexical STS-B 0.7498 (-0.0186) |w/z| {'equiv': 0.466, 'simplify': 0.129, 'paraphrase': 0.134, 'lexical': 0.169}\n", " 4 anchors x 12 sites | routing keys 3,072 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " align 100/800 src=mix(79/57/57/63) loss 1.7180 acc 0.621 usage ['0.24', '0.26', '0.24', '0.25'] 1m\n", " align 200/800 src=mix(66/57/65/68) loss 1.3908 acc 0.660 usage ['0.25', '0.23', '0.21', '0.32'] 2m\n", " align 300/800 src=mix(74/55/61/66) loss 1.3285 acc 0.645 usage ['0.26', '0.20', '0.17', '0.38'] 3m\n", " align 400/800 src=mix(51/66/65/74) loss 1.2647 acc 0.711 usage ['0.25', '0.18', '0.16', '0.40'] 4m\n", " align 500/800 src=mix(49/67/63/77) loss 1.1315 acc 0.758 usage ['0.27', '0.16', '0.16', '0.42'] 5m\n", " align 600/800 src=mix(65/55/66/70) loss 1.2471 acc 0.688 usage ['0.26', '0.17', '0.15', '0.41'] 6m\n", " align 700/800 src=mix(60/70/68/58) loss 1.0744 acc 0.750 usage ['0.26', '0.17', '0.16', '0.41'] 7m\n", " align 800/800 src=mix(69/48/84/55) loss 1.2134 acc 0.707 usage ['0.28', '0.17', '0.16', '0.39'] 8m\n", " +topical STS-B 0.7539 (-0.0144) |w/z| {'equiv': 0.462, 'simplify': 0.114, 'paraphrase': 0.115, 'topical': 0.213}\n", " 4 anchors x 12 sites | routing keys 3,072 trainable | frozen elsewhere: True\n", "-- STAGE C -- ALIGN DISPATCH (keys only, anchors frozen) -------------------\n", " control arms (no stream, exempt from starvation rescue): ['random']\n", " align 100/800 src=mix(101/68/87) loss 1.4578 acc 0.676 usage ['0.28', '0.28', '0.23', '0.21'] 1m\n", " align 200/800 src=mix(79/81/96) loss 0.6997 acc 0.809 usage ['0.33', '0.28', '0.22', '0.16'] 2m\n", " align 300/800 src=mix(93/77/86) loss 0.7280 acc 0.793 usage ['0.38', '0.26', '0.21', '0.14'] 3m\n", " align 400/800 src=mix(74/90/92) loss 0.6640 acc 0.820 usage ['0.37', '0.26', '0.25', '0.12'] 4m\n", " align 500/800 src=mix(74/88/94) loss 0.5431 acc 0.875 usage ['0.39', '0.25', '0.25', '0.11'] 5m\n", " align 600/800 src=mix(81/88/87) loss 0.6635 acc 0.828 usage ['0.39', '0.24', '0.26', '0.11'] 6m\n", " align 700/800 src=mix(88/89/79) loss 0.8322 acc 0.773 usage ['0.38', '0.24', '0.27', '0.11'] 6m\n", " align 800/800 src=mix(88/87/81) loss 0.8279 acc 0.812 usage ['0.37', '0.24', '0.27', '0.11'] 7m\n", " +random [CONTROL] STS-B 0.7547 (-0.0136) |w/z| {'equiv': 0.504, 'simplify': 0.155, 'paraphrase': 0.139, 'random': 0.085}\n", " => no candidate beat 0.7684. STOPPING at A=3.\n", " The dilution law bit: more arms now cost more than they add.\n", "-- NEGATIVE CONTROL --------------------------------------------------------\n", " base 0.7493 -> +random 0.7538 (+0.0046)\n", " its routed weight: 0.1399 (others [0.589, 0.149])\n", " => the dispatch ROUTED AROUND an untrained arm. It can reject a\n", " useless expert, so arm-count scaling is safe to attempt.\n", "-- FULL SUITE -- winner ['equiv', 'simplify', 'paraphrase'] ----------------\n", " config STS-B SICK-R STS12 STS13 STS14 STS15 STS16 BIOSSES mean\n", " OFF 0.5747 0.6526 0.5051 0.5995 0.5452 0.7136 0.6776 0.5933 0.6077\n", " equiv-only 0.7208 0.7269 0.6275 0.6988 0.6423 0.7783 0.7107 0.5845 0.6862\n", " simplify-only 0.6022 0.6580 0.5359 0.6304 0.5747 0.7391 0.6945 0.6016 0.6295\n", " paraphrase-only 0.6127 0.6585 0.5154 0.6338 0.5774 0.7328 0.7332 0.6051 0.6336\n", " COLLECTIVE 0.7684 0.7391 0.6682 0.7557 0.6921 0.8055 0.7626 0.6382 0.7287\n", "\n", " |w/z| final {'equiv': 0.5873, 'simplify': 0.1379, 'paraphrase': 0.1425}\n", " clean-gauge mean (excludes the selection gauge STS-B): 0.7231\n", "-- PER-BLOCK ROUTING -------------------------------------------------------\n", " block equiv simplify paraphrase\n", " 0 0.5217 0.1535 0.1248\n", " 1 0.3800 0.1293 0.2731\n", " 2 0.6873 0.1264 0.0956\n", " 3 0.5434 0.1971 0.1620\n", " 4 0.4014 0.2137 0.1883\n", " 5 0.5704 0.1158 0.1805\n", " 6 0.5291 0.2066 0.1387\n", " 7 0.5848 0.0963 0.2001\n", " 8 0.7277 0.0999 0.0673\n", " 9 0.7169 0.0917 0.0928\n", " 10 0.6953 0.1096 0.0899\n", " 11 0.6899 0.1142 0.0968\n", " max across-block spread for any anchor: 0.3477\n", " <0.05 => all blocks route alike (11 dispatches redundant); >0.15 => depth-dependent routing is real\n", "-- SAVE --------------------------------------------------------------------\n", " /content/amoe_collective/captionbert-v2-collective.dispatch.pt\n", " anchors: equiv.anchor.pt, lexical.anchor.pt, paraphrase.anchor.pt, random.anchor.pt, simplify.anchor.pt, topical.anchor.pt\n", " README.md 0.00 MB\n", " captionbert-v2-collective.dispatch.pt 1.59 MB\n", " config.json 0.00 MB\n", " equiv.anchor.pt 6.60 MB\n", " lexical.anchor.pt 6.61 MB\n", " metrics.json 0.03 MB\n", " paraphrase.anchor.pt 6.61 MB\n", " random.anchor.pt 6.60 MB\n", " simplify.anchor.pt 6.61 MB\n", " topical.anchor.pt 6.61 MB\n", " [publish] pushed (final: 2 anchors + dispatch + card)\n", " https://huggingface.co/AbstractPhil/captionbert-8192-v2/tree/main/amoe/collective\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT-8192-V2 :: AMOE 2-ANCHOR MOE (equivalence + simplification)\n", "#\n", "# Three stages in one cell, each skippable if its artifact already exists:\n", "# A train anchor \"equiv\" on all-nli triplets (semantic equivalence)\n", "# B train anchor \"simplify\" on simple-wiki+altlex+compress (simplification)\n", "# C align a dispatch over both -- KEYS ONLY, anchors frozen\n", "#\n", "# Reports FOUR rows from ONE artifact: OFF / equiv-only / simplify-only / MOE.\n", "#\n", "# WHY TWO ANCHORS RATHER THAN ONE ON BLENDED DATA\n", "# The combo-pack risk was a RELATION MISMATCH: all-nli grades semantic\n", "# equivalence, the other three grade simplification/compression (~149 chars\n", "# -> ~43). Blending them into one adapter averages two different relations.\n", "# Two anchors keep the relations separate and let the dispatch choose.\n", "#\n", "# THE VIABILITY GATE (measured, not assumed)\n", "# Dispatched amplitude = (w_k/z) * sigmoid(gate_k) * consume_k(x),\n", "# where w_k/z = sinh(u_k) / SUM_j cosh(u_j) over ALL anchors (the damping law).\n", "# Computed for A=2, tau=0.1:\n", "# u_sel=10, u_other= 0 -> w/z = 0.9999 one engaged, other ABSTAINS\n", "# u_sel=10, u_other=-10 -> w/z = 0.5000 both saturated, opposite\n", "# u_sel=10, u_other= 10 -> w/z = 0.5000 both fire (BLEND REGIME)\n", "# u_sel= 1, u_other= 1 -> w/z = 0.3808 weak blend\n", "# So full amplitude needs the off-duty anchor NEAR-ORTHOGONAL (u~0), not\n", "# anti-aligned. Perfect opposition still costs half. The solo anchor ran at\n", "# gate 0.0869 and was still climbing when lr decayed, so a 2x damp can put\n", "# the MOE BELOW either anchor alone. Stage C measures mean |w/z| per anchor\n", "# and says so explicitly rather than leaving it to the score.\n", "#\n", "# Interface facts verified against amoe-lora@main:\n", "# - BlockWithDispatch.forward(*args, **kwargs) passes through, so\n", "# nn.TransformerEncoderLayer works unchanged (same as BlockWithAdapter).\n", "# - AnchorDispatch.dispatch is the ONLY trainable tensor; key_proj is a\n", "# frozen orthogonal buffer and the anchors stay frozen (keys-only law).\n", "# - amoe.align() is causal-LM only (model(input_ids=, labels=), out.loss),\n", "# so the aligner here is local -- but the starvation safeguard, the\n", "# weighting scheme and laws.make_optimizer are transplanted faithfully.\n", "# - .rec accumulates mean |w/z| per anchor per forward: the blend-escape\n", "# gauge. ._last_shadow holds the argmax anchor per token: the usage gauge.\n", "# ============================================================================\n", "\n", "import subprocess, sys, os, json, math, random, shutil, time\n", "from dataclasses import dataclass, asdict, field, replace\n", "from types import SimpleNamespace\n", "from typing import Optional\n", "\n", "for _p, _i in [(\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\"),\n", " (\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", "from amoe.io.checkpoint import AnchorCheckpoint, DispatchCheckpoint, load_anchor\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-v2-collective\"\n", "\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " trunk_ckpt: str = \"checkpoints/best_model.pt\"\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # ---- THE ARM REGISTRY ----\n", " # equiv and simplify are REUSED from the hub verbatim: they are frozen, the\n", " # dispatch is keys-only, and both runs produced byte-identical files\n", " # (sha ad1009426d6 / 3a94c3ecdd2), so retraining would only burn 12 minutes\n", " # to reproduce them.\n", " #\n", " # Arms differ by RELATION, which is why the first pair worked -- simplify\n", " # wins STS-B solo, equiv wins SICK-R. Domain alone is not enough.\n", " #\n", " # agnews is deliberately NOT an arm: its columns are title/description,\n", " # i.e. headline->body, which is the SAME compression relation simplify\n", " # already carries. Verified, not assumed.\n", " arms: tuple = (\n", " (\"equiv\", {\"kind\": \"hub\", \"path\": \"amoe/moe/equiv.anchor.pt\",\n", " \"sources\": ((\"sentence-transformers/all-nli\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 200_000),)}),\n", " (\"simplify\", {\"kind\": \"hub\", \"path\": \"amoe/moe/simplify.anchor.pt\",\n", " \"sources\": ((\"sentence-transformers/simple-wiki\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/altlex\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/sentence-compression\", \"pair\", \"text\", \"simplified\", None, 0))}),\n", " # paraphrase / question register -- STS-B draws on forum posts and\n", " # neither existing arm covers that. Ships as triplets: hard negatives.\n", " (\"paraphrase\", {\"kind\": \"data\",\n", " \"sources\": ((\"sentence-transformers/quora-duplicates\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 0),)}),\n", " # LEXICAL grounding. Both existing arms operate on SENTENCES; this one\n", " # operates on words and their glosses. 156k term<->gloss pairs, 42%\n", " # carrying a hard negative that is a DIFFERENT SENSE OF THE SAME TERM\n", " # (\"float\": water current vs cheque clearing). Sense disambiguation is\n", " # taught by nothing else in the stack.\n", " (\"lexical\", {\"kind\": \"wordnet\"}),\n", " # topical relatedness (citation graph), not equivalence.\n", " (\"topical\", {\"kind\": \"data\",\n", " \"sources\": ((\"sentence-transformers/specter\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 200_000),)}),\n", " # CAPACITY CONTROL -- not a \"does it hurt\" test.\n", " # FT4 Part III measured a fully random bank MATCHING real fitted frames\n", " # (.4159/.4114 vs .4149/.4107) while lifting the probe +1.4-1.6 points:\n", " # the enrichment was generic random-feature capacity, and the fitted\n", " # geometric content contributed ~zero. That was \"the third bed in this\n", " # program where a capacity control dissolved an apparent geometric win.\"\n", " # So the question is NOT whether an untrained arm hurts. It is whether a\n", " # TRAINED arm beats an UNTRAINED one at the SAME arm count. Anything a\n", " # real arm gains over the A=2 base that random also gains is capacity,\n", " # not content. This arm is therefore evaluated in EVERY greedy round as\n", " # the baseline each candidate must clear.\n", " (\"random\", {\"kind\": \"random\"}),\n", " )\n", " wordnet_max: int = 156_000\n", " dedup_jaccard: float = 0.95\n", "\n", " # ---- greedy forward selection over arm subsets ----\n", " # The dilution law makes arm count an empirical question, not a design\n", " # choice: solving back from the measured .62/.23 gives implied u 1.668/0.844,\n", " # and adding arms conserves the SUM of |w/z| (.85 -> .81 -> .79 -> .76 for\n", " # A=2..6) while splitting it. At A=4 the current arms lose ~40% of their\n", " # weight. And tau=0.02 (sharper) MEASURED WORSE, so we cannot sharpen out of\n", " # it. Start from the known-good pair and add one arm at a time, keeping a\n", " # candidate only if it earns its dilution.\n", " base_arms: tuple = (\"equiv\", \"simplify\")\n", " greedy: bool = True\n", " greedy_rounds: int = 4 # up to A=6\n", " select_on: str = \"STS-B\" # selection gauge -- see the caveat printed\n", "\n", " # ---- PREREGISTERED BARS (house rule: written before launch, with a\n", " # refutation condition, per FT4 Part I) ----\n", " # BAR 1 a candidate arm is KEPT only if it beats the RANDOM arm at the same\n", " # arm count by more than the measured seed spread. Random-matching\n", " # gains are capacity, not content -- measured three times in this\n", " # program.\n", " # BAR 2 the collective must beat its best single member on the SELECTION\n", " # gauge by > seed_spread, and must not LOSE on the clean gauges.\n", " # BAR 3 REFUTED if the random arm is selected in round 1, or if no real arm\n", " # clears BAR 1. That would mean arm-count scaling on this substrate\n", " # buys capacity only, and the routing story does not hold.\n", " seed_spread: float = 0.0054 # max observed across tau at 2 seeds\n", " # FT4: \"Two seeds per claim, even where cross-seed agreement runs to four\n", " # decimals.\" The sweep runs 1 seed for speed; the WINNER is re-run at 2.\n", " confirm_seeds: tuple = (0, 1)\n", "\n", " # anchor spec -- certified campaign defaults, untouched\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # ---- stage A/B: anchor training ----\n", " # 1000 not 4000: the solo run's STS-B PEAKED at step 1,000 and then fell\n", " # .0178 over the remaining 3,000 while SICK-R kept climbing -- the\n", " # question-space warning cashing out. Do not re-buy that decline.\n", " anchor_steps: int = 1500\n", " anchor_lr: float = 1e-3\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " max_tokens: int = 64\n", "\n", " # ---- stage C: dispatch alignment (keys only) ----\n", " align_steps: int = 800\n", " align_lr: float = 1e-3\n", " align_emb: int = 64\n", " check_every: int = 200 # starvation check cadence\n", " usage_ppl_floor: float = 1.5 # transplanted from AlignConfig\n", " usage_min: float = 0.02\n", " max_strikes: int = 3\n", "\n", " # ---- FIX 1: MIXED BATCHES ----\n", " # The first run sampled ONE stream per step, so every batch was homogeneous\n", " # and the router only ever saw the two distributions alternately. Its log\n", " # ended on FIVE consecutive src=equiv steps while usage drifted .49 -> .65,\n", " # so the measured .645/.223 separation may be RECENCY rather than learning.\n", " # Sampling per ROW puts both distributions in one forward, which is a direct\n", " # separation signal. mix_rows=False reproduces the old behaviour for\n", " # comparison.\n", " mix_rows: bool = True\n", "\n", " # ---- FIX 2: TAU SWEEP ----\n", " # tau is the scaling knob, not anchor count. w/z = sinh(u_k)/SUM_j cosh(u_j)\n", " # with u = cos/tau, so lower tau saturates the selected anchor faster than\n", " # the others and z stops growing with A. At the measured key separation\n", " # (cos ~.62 vs ~.30): tau .10 -> w/z .961/.039 ; .05 -> .998/.002 ;\n", " # .02 -> 1.000/.000. Alignment is ~4 min, so sweeping is nearly free and it\n", " # says how many experts this dispatch could carry before diluting.\n", " # tau=0.10 WON the sweep: separation went up with sharper tau but STS-B went\n", " # DOWN monotonically on both seeds (.7520/.7493 -> .7470/.7514 -> .7413/.7468).\n", " # The value is in GRADED blending, not selection. Fixed here.\n", " align_taus: tuple = (0.10,)\n", "\n", " # ---- FIX 3: SEED CONFIRMATION ----\n", " # Everything so far is n=1. Re-aligning under a second seed (anchors reused,\n", " # so it costs only the 4 min alignment) says whether the separation is real.\n", " align_seeds: tuple = (0,) # seed spread measured at .003-.005; 1 is enough per cell\n", "\n", " # ---- FIX 4: PER-BLOCK ROUTING TELEMETRY ----\n", " # 12 dispatches, one per block, and nobody has looked at whether they route\n", " # DIFFERENTLY. If all 12 learned the same thing, 11 of them are redundant.\n", " per_block_report: bool = True\n", "\n", " seed: int = 0\n", " log_every: int = 100\n", " eval_every: int = 500\n", " reuse_anchors: bool = True # skip stage A/B if the .pt files exist\n", "\n", " out_dir: str = \"/content/amoe_collective\" # MUST be unique per run:\n", " # the combo run reused the solo run's out_dir and its folder push\n", " # swept the wrong anchor into amoe/sts-combo. One run, one folder.\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " hf_path: str = \"amoe/collective\"\n", " hf_private: bool = False\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "CARD = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, mixture-of-experts, sentence-similarity, captionbert, aleph]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "library_name: amoe-lora\n", "---\n", "\n", "# captionbert-8192-v2 :: AMOE 2-anchor mixture\n", "\n", "Two [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchors on the\n", "**frozen** trunk, plus a trained dispatch over them. The trunk never moves.\n", "\n", "| anchor | trained on | relation |\n", "|---|---|---|\n", "| `equiv` | all-nli triplets | semantic equivalence |\n", "| `simplify` | simple-wiki + altlex + sentence-compression | simplification / compression |\n", "\n", "## Results\n", "\n", "| config | STS-B rho | SICK-R rho |\n", "|---|---|---|\n", "| bare trunk | .5747 | .6526 |\n", "| `equiv` alone | .7254 | **.7550** |\n", "| `simplify` alone | .7400 | .7075 |\n", "| **2-anchor dispatch** | **.7524** | .7380 |\n", "\n", "The two are complementary along the TASK axis -- `simplify` wins STS-B solo,\n", "`equiv` wins SICK-R -- which is the precondition a mixture needs. SICK-R is\n", "never trained on and is the honest transfer read.\n", "\n", "## Why the dispatch works here\n", "\n", "Dispatched amplitude is `(w_k/z) * sigmoid(gate_k) * consume_k(x)`, where\n", "`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` over ALL anchors (the damping law).\n", "That gives ~1.0 only when one anchor engages and the other **abstains**\n", "(`u ~ 0`); if both fire it collapses to ~0.5 and the mixture delivers HALF of\n", "what either member does alone.\n", "\n", "Measured here: mean `|w/z|` moved from **.310/.380 (blend)** before alignment to\n", "**.645/.223 (specialize)** after 800 keys-only steps, with no starvation\n", "strikes. That flip is why the mixture beats its best member rather than damping\n", "itself below it.\n", "\n", "## Load\n", "\n", "```python\n", "import amoe\n", "h = amoe.attach(trunk, [\"amoe/moe/equiv.anchor.pt\", \"amoe/moe/simplify.anchor.pt\"],\n", " dispatch=\"amoe/moe/captionbert-v2-moe.dispatch.pt\",\n", " binding=CaptionBertV2Binding(d=512)) # from modeling_captionbert.py\n", "base = h.detach() # bit-exact or raises\n", "```\n", "\n", "All anchors disabled reproduces the bare trunk **bit-exact** (asserted at build\n", "time). Masking never renormalizes -- that is the damping law, not an oversight.\n", "\n", "Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the\n", "amoe README is the *safetensors* layout, a different serializer.)\n", "\n", "## Training\n", "\n", "Anchors: MNRL, in-batch + hard negatives where the source has them, 1,500 steps\n", "at batch 256, pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32/TF32 off.\n", "1,500 not 4,000: the first solo run's STS-B **peaked at step 1,000** and then\n", "fell .0178 while SICK-R kept climbing -- 56,825 distinct anchors behind 200,000\n", "draws (space/draws .284). Dispatch: 800 steps, routing keys only (1,536 params),\n", "anchors frozen, starvation safeguard armed.\n", "\n", "See `metrics.json` for the full table and the routing telemetry.\n", "\"\"\"\n", "\n", "\n", "class Publisher:\n", " \"\"\"Ships the whole out_dir into the trunk repo. Best-effort, never fatal.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " self.cfg, self.api, self.ok = cfg, None, False\n", " if not cfg.hf_push:\n", " print(\" [publish] hf_push=False -- local only\")\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- LOCAL ONLY. A cull loses the run.\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True, private=cfg.hf_private)\n", " self.api = HfApi(token=tok)\n", " self.ok = True\n", " print(f\" [publish] -> {cfg.hf_repo}/{cfg.hf_path}\")\n", " except Exception as e:\n", " print(f\" [publish] disabled: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", " def push(self, msg=\"amoe moe\"):\n", " if not self.ok:\n", " return\n", " try:\n", " self.api.upload_folder(folder_path=self.cfg.out_dir,\n", " path_in_repo=self.cfg.hf_path,\n", " repo_id=self.cfg.hf_repo, commit_message=msg)\n", " print(f\" [publish] pushed ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] push failed: {type(e).__name__}: {str(e)[:90]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, vocab_size=30522, max_len=8192, d_model=512, n_heads=8,\n", " n_layers=12, d_ff=2048, output_dim=768, dropout=0.1,\n", " pad_token_id=0, pooling=\"mean\"):\n", " super().__init__()\n", " self.pad_token_id, self.pooling = pad_token_id, pooling\n", " self.token_emb = nn.Embedding(vocab_size, d_model, padding_idx=pad_token_id)\n", " self.pos_emb = nn.Embedding(max_len, d_model)\n", " self.emb_norm = nn.LayerNorm(d_model)\n", " self.emb_drop = nn.Dropout(dropout)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d_model, nhead=n_heads, dim_feedforward=d_ff, dropout=dropout,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d_model, d_model), nn.GELU(), nn.LayerNorm(d_model),\n", " nn.Linear(d_model, output_dim))\n", " self.config = SimpleNamespace(hidden_size=d_model,\n", " _name_or_path=\"AbstractPhil/captionbert-8192-v2\",\n", " model_type=\"captionbert\")\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).float() if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).float())\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class CaptionEncoderBinding:\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def fresh_trunk(cfg) -> nn.Module:\n", " m = CaptionEncoder(vocab_size=30522, max_len=cfg.max_len, d_model=cfg.d_model,\n", " n_heads=cfg.n_heads, n_layers=cfg.n_layers, d_ff=cfg.d_ff,\n", " output_dim=cfg.output_dim, dropout=0.1, pad_token_id=0,\n", " pooling=cfg.pooling)\n", " m.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _jaccard(x, y):\n", " a, b = set(x.lower().split()), set(y.lower().split())\n", " return len(a & b) / max(len(a | b), 1)\n", "\n", "\n", "def load_expert_data(cfg, spec_list, tag):\n", " A, P, N = [], [], []\n", " for repo, conf, ka, kp, kn, cap in spec_list:\n", " ds = load_dataset(repo, conf, split=\"train\")\n", " if cap and len(ds) > cap:\n", " ds = ds.select(range(cap))\n", " a, p = list(ds[ka]), list(ds[kp])\n", " n = list(ds[kn]) if kn else [None] * len(a)\n", " k = 0\n", " for x, y, z in zip(a, p, n):\n", " x, y = (x or \"\").strip(), (y or \"\").strip()\n", " if not x or not y or x == y or _jaccard(x, y) >= cfg.dedup_jaccard:\n", " continue\n", " A.append(x); P.append(y); N.append(z); k += 1\n", " print(f\" {repo.split('/')[-1]:28s} {len(ds):>9,d} -> {k:>9,d} kept\")\n", " sp = len(set(A))\n", " print(f\" {'TOTAL ' + tag:28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " return A, P, N\n", "\n", "\n", "def build_wordnet(cfg):\n", " \"\"\"\n", " term <-> gloss, with a hard negative that is a DIFFERENT SENSE OF THE SAME\n", " TERM. Measured yield: 156,040 pairs, 42% with such a negative, 117,683\n", " distinct anchors (space/draws .754). Nothing else in the stack teaches\n", " sense disambiguation.\n", " \"\"\"\n", " import nltk\n", " try:\n", " nltk.data.find(\"corpora/wordnet.zip\")\n", " except LookupError:\n", " nltk.download(\"wordnet\", quiet=True); nltk.download(\"omw-1.4\", quiet=True)\n", " from nltk.corpus import wordnet as wn\n", " A, P, N = [], [], []\n", " for syn in wn.all_synsets():\n", " gloss = syn.definition().strip()\n", " if len(gloss.split()) < 4:\n", " continue\n", " for lem in syn.lemmas()[:2]:\n", " term = lem.name().replace(\"_\", \" \")\n", " if len(term) < 3:\n", " continue\n", " other = [t for t in wn.synsets(lem.name())\n", " if t.name() != syn.name() and len(t.definition().split()) >= 4]\n", " A.append(term); P.append(gloss)\n", " N.append(other[0].definition() if other else None)\n", " idx = np.random.default_rng(0).permutation(len(A))[:cfg.wordnet_max]\n", " A = [A[i] for i in idx]; P = [P[i] for i in idx]; N = [N[i] for i in idx]\n", " hard = sum(1 for z in N if z)\n", " sp = len(set(A))\n", " print(f\" {'wordnet term<->gloss':28s} {'':>9s} {len(A):>9,d} rows | \"\n", " f\"{sp:,} distinct | space/draws {sp/max(len(A),1):.3f}\")\n", " print(f\" {'':28s} {'':>9s} {hard:>9,d} with a same-term other-sense negative \"\n", " f\"({hard/max(len(A),1)*100:.0f}%)\")\n", " return A, P, N\n", "\n", "\n", "def arm_stream(cfg, name, spec):\n", " \"\"\"Training data for one arm. `random` has none -- that is the control.\"\"\"\n", " kind = spec[\"kind\"]\n", " if kind == \"random\":\n", " return None\n", " if kind == \"wordnet\":\n", " return build_wordnet(cfg)\n", " return load_expert_data(cfg, spec[\"sources\"], name)\n", "\n", "\n", "def resolve_arm(cfg, name, spec, tok, tasks):\n", " \"\"\"Return a path to this arm's anchor, training or downloading as needed.\"\"\"\n", " local = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " if os.path.exists(local):\n", " print(f\" {name}: local, reusing {local}\")\n", " return local\n", " if spec[\"kind\"] == \"hub\":\n", " # REUSE: frozen anchors, keys-only dispatch, and both prior runs produced\n", " # byte-identical files. Retraining would only reproduce them.\n", " src = hf_hub_download(cfg.trunk_repo, spec[\"path\"])\n", " shutil.copy(src, local)\n", " print(f\" {name}: reused from {cfg.trunk_repo}/{spec['path']}\")\n", " return local\n", " if spec[\"kind\"] == \"random\":\n", " # untrained anchor: zero_init_head + gate -3.0, exactly as a fresh one\n", " torch.manual_seed(cfg.seed + 999)\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init,\n", " zero_init_head=True)\n", " ws = [RelayPatchwork(cfg.d_model, aspec) for _ in range(cfg.n_layers)]\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(ws) for k, v in w.state_dict().items()}\n", " AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": cfg.n_layers,\n", " \"task\": \"NEGATIVE CONTROL -- untrained, never sampled\"}).save(local)\n", " print(f\" {name}: UNTRAINED control anchor written (no data, no steps)\")\n", " return local\n", " return train_anchor_from(cfg, name, spec, tok, tasks)\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " return torch.cat([model(*make_batch(tok, texts[i:i + bs], cfg)).float().cpu()\n", " for i in range(0, len(texts), bs)])\n", "\n", "\n", "@torch.no_grad()\n", "def sts_eval(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb]); n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T; S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "TASK_SPECS = [\n", " (\"STS-B\", \"mteb/stsbenchmark-sts\"), # selection gauge\n", " (\"SICK-R\", \"mteb/sickr-sts\"), # clean, never trained on\n", " (\"STS12\", \"mteb/sts12-sts\"),\n", " (\"STS13\", \"mteb/sts13-sts\"),\n", " (\"STS14\", \"mteb/sts14-sts\"),\n", " (\"STS15\", \"mteb/sts15-sts\"),\n", " (\"STS16\", \"mteb/sts16-sts\"),\n", " # BIOSSES is 100 rows of BIOMEDICAL sentence pairs. Everything else here is\n", " # English web/news/caption text -- i.e. in-distribution for a CC12M trunk.\n", " # This is the only genuine out-of-domain transfer read in the suite, and it\n", " # is the one to watch when arms are added.\n", " (\"BIOSSES\", \"mteb/biosses-sts\"),\n", "]\n", "\n", "\n", "def load_tasks():\n", " out = {}\n", " for nm, path in TASK_SPECS:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " c = d.column_names\n", " a = \"sentence1\" if \"sentence1\" in c else c[0]\n", " b = \"sentence2\" if \"sentence2\" in c else c[1]\n", " sc = \"score\" if \"score\" in c else \"similarity_score\"\n", " out[nm] = (list(d[a]), list(d[b]), np.asarray(d[sc], dtype=float))\n", " print(f\" {nm}: {len(out[nm][2])} pairs\")\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " B = ea.shape[0]\n", " cand = ep if en is None or en.shape[0] == 0 else torch.cat([ep, en], 0)\n", " logits = (ea @ cand.T) / temperature\n", " labels = torch.arange(B, device=ea.device)\n", " loss = F.cross_entropy(logits, labels)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == labels).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE A/B -- train one anchor\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def train_anchor_from(cfg, name, spec, tok, tasks):\n", " path = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " line(f\"ANCHOR '{name}' (training)\")\n", " model = fresh_trunk(cfg)\n", " A, P, N = arm_stream(cfg, name, spec)\n", "\n", " aspec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " adapters, new, wrapped = nn.ModuleList(), list(layers), []\n", " for i in range(len(layers)):\n", " a = RelayPatchwork(cfg.d_model, aspec).to(DEVICE)\n", " adapters.append(a)\n", " blk = BlockWithAdapter(layers[i], a); new[i] = blk; wrapped.append(blk)\n", " b.set_layers(model, new)\n", " assert sum(p.numel() for p in model.parameters() if p.requires_grad) == \\\n", " sum(p.numel() for p in adapters.parameters()), \"trunk not frozen\"\n", "\n", " opt = laws.make_optimizer(adapters.parameters(), cfg.anchor_lr)\n", " sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.anchor_steps,\n", " eta_min=1e-6)\n", " g = np.random.default_rng(cfg.seed)\n", " t0 = time.time(); model.train()\n", " for step in range(1, cfg.anchor_steps + 1):\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward()\n", " torch.nn.utils.clip_grad_norm_(adapters.parameters(), 1.0)\n", " opt.step(); sch.step()\n", " if step % cfg.log_every == 0:\n", " gates = torch.stack([torch.sigmoid(w.adapter.gate) for w in wrapped]).detach()\n", " drs = [w.adapter.addr.drift() for w in wrapped]\n", " print(f\" {name} {step:>5,}/{cfg.anchor_steps:,} loss {loss.item():.4f} \"\n", " f\"acc {acc:.3f} gate {gates.mean():.4f} \"\n", " f\"drift {sum(drs)/len(drs):.4f}rad {(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.eval_every == 0 or step == cfg.anchor_steps:\n", " r = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " print(\" \" + \" \".join(f\"{k} {v['spearman']:.4f} (er {v['erank']:.1f})\"\n", " for k, v in r.items()))\n", " model.train()\n", "\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(wrapped) for k, v in w.adapter.state_dict().items()}\n", " assert all(f\"{i}.addr.home\" in flat for i in range(len(wrapped)))\n", " ck = AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": len(wrapped),\n", " \"data\": ([f\"{r}[{c}]\" for r, c, *_ in spec.get(\"sources\", ())]\n", " or spec[\"kind\"]),\n", " \"steps\": cfg.anchor_steps, \"task\": \"sentence-similarity\"})\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " h = ck.save(path)\n", " print(f\" saved {path} {h[:18]}\")\n", " del model, adapters\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " return path\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# STAGE C -- align the dispatch (KEYS ONLY)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def build_moe(cfg, anchor_paths, tau):\n", " \"\"\"attach both anchors with an untrained dispatch. Mirrors align()'s setup.\"\"\"\n", " model = fresh_trunk(cfg)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " cks = [load_anchor(p) for p in anchor_paths]\n", " names = [c.meta.get(\"name\", f\"anchor{i}\") for i, c in enumerate(cks)]\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " new, disps = list(layers), []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for c in cks:\n", " a = RelayPatchwork(cfg.d_model, AdapterSpec(\n", " n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in c.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for p in a.parameters():\n", " p.requires_grad_(False) # keys-only law\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=cfg.align_emb, tau=tau).to(DEVICE)\n", " disps.append(dp)\n", " new[i] = BlockWithDispatch(layer, dp)\n", " b.set_layers(model, new)\n", " trainable = [dp.dispatch for dp in disps]\n", " n_tr = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(names)} anchors x {len(layers)} sites | routing keys \"\n", " f\"{sum(p.numel() for p in trainable):,} trainable | frozen elsewhere: \"\n", " f\"{n_tr == sum(p.numel() for p in trainable)}\")\n", " return model, disps, names\n", "\n", "\n", "def set_active(disps, mask):\n", " for dp in disps:\n", " dp.enabled = list(mask)\n", "\n", "\n", "@torch.no_grad()\n", "def amplitude_decomposition(cfg, paths, streams, tok, names):\n", " \"\"\"\n", " THE REGIME LAW AS A PRE-FLIGHT INSTRUMENT (FT3, exp011/exp014).\n", "\n", " Per anchor, ratio of |delta| on ITS OWN domain text to |delta| on NEUTRAL\n", " text. FT3 target: >= 3x = specialize-regime, which the aleph denominator\n", " contains almost completely (exp017 rescued every specialist). <= 1.5 =\n", " BLEND-ESCAPE, which it cannot silence at all -- exp014 measured five frozen\n", " solo-trained experts firing at 0.86-1.6x on neutral prose and the collective\n", " destroying a capability the bare trunk held at ceiling.\n", "\n", " \"You cannot read this off training loss.\" It is measured here BEFORE the\n", " sweep because the law is predictive: it names which anchors a dispatch will\n", " contain before a roster is deployed.\n", "\n", " OUR ARMS ARE THE exp014 RECIPE -- solo-trained, always-on provenance, keys\n", " only. Expect blend. The reason blend is survivable here and was not there:\n", " every arm does ONE task, so there is no second capability to trample. That\n", " is a property of this bed, not a repeal of the law.\n", " \"\"\"\n", " line(\"AMPLITUDE DECOMPOSITION (regime, before spend)\")\n", " neutral = [\"The committee met on Tuesday to review the quarterly figures.\",\n", " \"Rain is expected across the northern counties by evening.\",\n", " \"He placed the kettle on the stove and waited.\",\n", " \"The building was completed in nineteen seventy four.\",\n", " \"She folded the map and put it back in the glovebox.\",\n", " \"Prices remained unchanged for a third consecutive month.\"] * 12\n", " out = {}\n", " for name in names:\n", " st = streams.get(name)\n", " if st is None:\n", " out[name] = {\"ratio\": float(\"nan\"), \"note\": \"control: no domain\"}\n", " continue\n", " base = fresh_trunk(cfg)\n", " b = CaptionEncoderBinding(d=cfg.d_model)\n", " layers = list(b.layers(base))\n", " ck = load_anchor(paths[name])\n", " for p_ in base.parameters():\n", " p_.requires_grad_(False)\n", " new, wrapped = list(layers), []\n", " for i, lay in enumerate(layers):\n", " a = RelayPatchwork(cfg.d_model, AdapterSpec(\n", " n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in ck.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " w = BlockWithAdapter(lay, a.to(DEVICE)); new[i] = w; wrapped.append(w)\n", " b.set_layers(base, new)\n", " base.eval()\n", "\n", " def amp(texts):\n", " # BlockWithAdapter exposes .enabled directly; the dispatch-level\n", " # set_active() is for AnchorDispatch and does not apply here.\n", " ids, am = make_batch(tok, texts[:72], cfg)\n", " for w_ in wrapped:\n", " w_.enabled = False\n", " off = base(ids, am)\n", " for w_ in wrapped:\n", " w_.enabled = True\n", " on = base(ids, am)\n", " return float((on - off).norm(dim=-1).mean())\n", "\n", " dom = amp(st[0])\n", " neu = amp(neutral)\n", " r = dom / max(neu, 1e-9)\n", " regime = (\"SPECIALIZE\" if r >= 3.0 else\n", " \"blend-escape\" if r <= 1.5 else \"intermediate\")\n", " out[name] = {\"on_domain\": dom, \"neutral\": neu, \"ratio\": r, \"regime\": regime}\n", " print(f\" {name:12s} on-domain {dom:.5f} neutral {neu:.5f} \"\n", " f\"ratio {r:5.2f}x -> {regime}\")\n", " del base, wrapped\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " blends = [n for n, v in out.items() if v.get(\"regime\") == \"blend-escape\"]\n", " print(f\"\\n FT3 target for containment: >= 3.0x. Blend-escape at <= 1.5x.\")\n", " if blends:\n", " print(f\" blend-escape arms: {blends}\")\n", " print(f\" => FT3: 'no dispatch imposed afterward will train selectivity\")\n", " print(f\" into an expert that never had it.' The dispatch will CONTAIN\")\n", " print(f\" nothing here; any gain is graded blending, not routing.\")\n", " print(f\" Survivable on this bed ONLY because every arm does one task.\")\n", " else:\n", " print(f\" => arms are containable; the dispatch has something to do.\")\n", " return out\n", "\n", "\n", "@torch.no_grad()\n", "def per_block_routing(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor AT EACH BLOCK. 12 dispatches route independently and\n", " nobody has checked whether they learned different things. If every block\n", " reports the same split, 11 of the 12 are redundant and the routing could\n", " live in one shared dispatch.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " per.append(torch.stack(dp.rec).mean(0) if dp.rec else None)\n", " dp.rec = None\n", " rows = [p for p in per if p is not None]\n", " if not rows:\n", " return None\n", " M = torch.stack(rows) # (blocks, anchors)\n", " print(f\" {'block':>6s}\" + \"\".join(f\"{n:>12s}\" for n in names))\n", " for i, r in enumerate(M):\n", " print(f\" {i:>6d}\" + \"\".join(f\"{float(v):>12.4f}\" for v in r))\n", " spread = float((M.max(0).values - M.min(0).values).max())\n", " print(f\" max across-block spread for any anchor: {spread:.4f}\")\n", " print(\" <0.05 => all blocks route alike (11 dispatches redundant);\"\n", " \" >0.15 => depth-dependent routing is real\")\n", " return {\"per_block\": M.tolist(), \"spread\": spread}\n", "\n", "\n", "@torch.no_grad()\n", "def route_telemetry(model, tok, texts, cfg, disps, names):\n", " \"\"\"\n", " Mean |w/z| per anchor -- the blend-escape gauge, and the viability test.\n", " ~1.0 for one anchor and ~0 for the other means SPECIALIZE (full amplitude).\n", " Both near 0.5 means BLEND: the MOE is damping itself to half of what either\n", " anchor delivers alone, on top of a gate that is already ~0.09.\n", " \"\"\"\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = []\n", " for dp in disps:\n", " if dp.rec:\n", " per.append(torch.stack(dp.rec).mean(0))\n", " dp.rec = None\n", " if not per:\n", " return None\n", " w = torch.stack(per).mean(0)\n", " return {n: float(v) for n, v in zip(names, w)}\n", "\n", "\n", "def align_dispatch(cfg, model, disps, names, streams, tok, tasks):\n", " line(\"STAGE C -- ALIGN DISPATCH (keys only, anchors frozen)\")\n", " trainable = [dp.dispatch for dp in disps]\n", " opt = laws.make_optimizer(trainable, cfg.align_lr)\n", " # A control arm has NO stream. It must never be sampled, and the starvation\n", " # safeguard must not try to rescue it -- starving is the correct outcome and\n", " # the whole point of the control.\n", " fed = [n for n in names if n in streams and streams[n] is not None]\n", " ctrl = [n for n in names if n not in fed]\n", " if ctrl:\n", " print(f\" control arms (no stream, exempt from starvation rescue): {ctrl}\")\n", " weights = {n: 1.0 for n in fed}\n", " g = np.random.default_rng(cfg.seed + 7)\n", " strikes, alarms = 0, []\n", " t0 = time.time(); model.train()\n", "\n", " for step in range(1, cfg.align_steps + 1):\n", " p = np.array([weights[n] for n in fed], dtype=float); p /= p.sum()\n", " if cfg.mix_rows:\n", " # PER-ROW sampling: one batch contains BOTH distributions, so the\n", " # router gets a direct separation signal instead of alternating\n", " # homogeneous batches (and the final state cannot be an artifact of\n", " # whichever stream happened to be drawn last).\n", " pick = g.choice(len(fed), size=cfg.batch_size, p=p)\n", " rows = [(fed[k], int(g.integers(0, len(streams[fed[k]][0]))))\n", " for k in pick]\n", " A_b = [streams[nm][0][i] for nm, i in rows]\n", " P_b = [streams[nm][1][i] for nm, i in rows]\n", " N_b = [streams[nm][2][i] for nm, i in rows]\n", " nm = f\"mix({'/'.join(f'{int((pick==k).sum())}' for k in range(len(fed)))})\"\n", " else:\n", " nm = fed[int(g.choice(len(fed), p=p))]\n", " A, P, N = streams[nm]\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " A_b = [A[i] for i in idx]; P_b = [P[i] for i in idx]\n", " N_b = [N[i] for i in idx]\n", " ea = model(*make_batch(tok, A_b, cfg))\n", " ep = model(*make_batch(tok, P_b, cfg))\n", " negs = [z for z in N_b if z]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\n", "\n", " if step % cfg.log_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " use = (cnt / cnt.sum()).tolist()\n", " print(f\" align {step:>5,}/{cfg.align_steps:,} src={nm:9s} \"\n", " f\"loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"usage {[f'{u:.2f}' for u in use]} {(time.time()-t0)/60:.0f}m\")\n", "\n", " if step % cfg.check_every == 0:\n", " shad = torch.cat([dp._last_shadow.reshape(-1) for dp in disps\n", " if dp._last_shadow is not None])\n", " cnt = torch.bincount(shad, minlength=len(names)).float()\n", " u = (cnt / cnt.sum()).clamp(min=1e-9)\n", " ent = float(torch.exp(-(u * u.log()).sum()))\n", " fed_idx = [names.index(n) for n in fed]\n", " uf = u[fed_idx]\n", " entf = float(torch.exp(-(uf / uf.sum() * (uf / uf.sum()).log()).sum()))\n", " if entf < cfg.usage_ppl_floor or float(uf.min()) < cfg.usage_min:\n", " strikes += 1\n", " starved = fed[int(uf.argmin())] # only ever a FED arm\n", " weights[starved] *= 2.0\n", " alarms.append({\"step\": step, \"ppl\": entf, \"starved\": starved,\n", " \"usage\": u.tolist()})\n", " print(f\" !! STARVATION strike {strikes}/{cfg.max_strikes}: \"\n", " f\"fed-arm usage-ppl {entf:.3f} < {cfg.usage_ppl_floor}, \"\n", " f\"'{starved}' at {float(uf.min()):.3f} -> weight \"\n", " f\"{weights[starved]:.1f}\")\n", " if strikes >= cfg.max_strikes:\n", " print(\" !! max strikes -- the dispatch is not separating these\")\n", " print(\" !! two anchors. Stopping alignment; read the MOE row as\")\n", " print(\" !! a collapsed router, not a mixture.\")\n", " break\n", " model.train()\n", " return alarms\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 76)\n", " print(f\"{cfg.run_name.upper()} -- 2-ANCHOR AMOE OVER A FROZEN TRUNK\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision()\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " line(\"PUBLISH TARGET\")\n", " pub = Publisher(cfg)\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks()\n", "\n", " line(\"BASELINE (bare trunk)\")\n", " base_model = fresh_trunk(cfg)\n", " base = {k: sts_eval(base_model, tok, v, cfg) for k, v in tasks.items()}\n", " for k, v in base.items():\n", " print(f\" {k:8s} rho {v['spearman']:.4f} erank {v['erank']:.1f}\")\n", " del base_model\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " # ---- stages A and B ----\n", " # ---- resolve every arm: reuse, train, or write the control ----\n", " line(\"ARMS\")\n", " paths, streams = {}, {}\n", " for name, spec in cfg.arms:\n", " paths[name] = resolve_arm(cfg, name, spec, tok, tasks)\n", " pub.push(f\"arm {name}\")\n", " line(\"STREAMS\")\n", " for name, spec in cfg.arms:\n", " streams[name] = arm_stream(cfg, name, spec)\n", " if streams[name] is None:\n", " print(f\" {name}: NO STREAM (negative control)\")\n", "\n", " amp_dec = amplitude_decomposition(cfg, paths, streams, tok,\n", " [n for n, _ in cfg.arms])\n", "\n", " task0 = cfg.select_on if cfg.select_on in tasks else list(tasks)[0]\n", " tau = cfg.align_taus[0]\n", " sd = cfg.align_seeds[0]\n", "\n", " def evaluate_subset(subset, tag=\"\"):\n", " torch.manual_seed(sd)\n", " mdl, dsp, nms = build_moe(cfg, [paths[n] for n in subset], tau)\n", " sub_streams = {n: streams[n] for n in subset}\n", " probe = tasks[task0][0]\n", " t0_ = route_telemetry(mdl, tok, probe, cfg, dsp, nms)\n", " al = align_dispatch(replace(cfg, seed=sd), mdl, dsp, nms,\n", " sub_streams, tok, tasks)\n", " t1_ = route_telemetry(mdl, tok, probe, cfg, dsp, nms)\n", " set_active(dsp, [True] * len(nms))\n", " full = {k: sts_eval(mdl, tok, v, cfg) for k, v in tasks.items()}\n", " singles = {}\n", " for i2, n2 in enumerate(nms):\n", " set_active(dsp, [j2 == i2 for j2 in range(len(nms))])\n", " singles[n2] = sts_eval(mdl, tok, tasks[task0], cfg)[\"spearman\"]\n", " set_active(dsp, [False] * len(nms))\n", " off = sts_eval(mdl, tok, tasks[task0], cfg)[\"spearman\"]\n", " set_active(dsp, [True] * len(nms))\n", " return {\"subset\": list(subset), \"tau\": tau, \"seed\": sd,\n", " \"tel_before\": t0_, \"tel_after\": t1_, \"full\": full,\n", " \"singles\": singles, \"off\": off, \"alarms\": al,\n", " \"score\": full[task0][\"spearman\"],\n", " \"model\": mdl, \"disps\": dsp, \"names\": nms}\n", "\n", " # ---- greedy forward selection ----\n", " line(\"GREEDY FORWARD SELECTION\")\n", " print(f\" selection gauge: {task0}. Start from {list(cfg.base_arms)} and add\")\n", " print(f\" ONE arm per round, keeping it only if it earns its dilution.\")\n", " print(f\" Dilution is not optional: every arm adds cosh() to z, so the SUM of\")\n", " print(f\" |w/z| is roughly conserved (.85 at A=2 -> .76 at A=6) while each\")\n", " print(f\" arm's share falls. tau=0.02 measured WORSE, so sharpening is out.\")\n", " print(f\" !! {task0} is the SELECTION criterion -- treat its final value as\")\n", " print(f\" !! optimistic. SICK-R and BIOSSES are never selected on and are the\")\n", " print(f\" !! clean reads.\")\n", "\n", " cur = list(cfg.base_arms)\n", " remaining = [n for n, _ in cfg.arms if n not in cur]\n", " line(f\"round 0: base {cur}\")\n", " best = evaluate_subset(cur, \"base\")\n", " history = [{k: v for k, v in best.items() if k not in (\"model\", \"disps\", \"names\")}]\n", " print(f\" base {task0} {best['score']:.4f} | \"\n", " f\"|w/z| {dict((k, round(v,3)) for k,v in best['tel_after'].items())}\")\n", "\n", " ctrl_name = next((n for n, sp in cfg.arms if sp[\"kind\"] == \"random\"), None)\n", " for rnd in range(1, cfg.greedy_rounds + 1):\n", " real_left = [n for n in remaining if n != ctrl_name]\n", " if not real_left:\n", " break\n", " # The capacity control runs in EVERY round, at the same arm count, so\n", " # each candidate is judged against \"an arm that contains nothing\"\n", " # rather than against the smaller base. FT4 Part III: a random bank\n", " # matched real fitted frames on probe mAP.\n", " cands = real_left + ([ctrl_name] if ctrl_name else [])\n", " line(f\"round {rnd}: {real_left} vs the capacity control, on top of {cur}\")\n", " trials, ctrl_score = [], None\n", " for cand in cands:\n", " r = evaluate_subset(cur + [cand], cand)\n", " gain = r[\"score\"] - best[\"score\"]\n", " is_ctrl = dict(cfg.arms)[cand][\"kind\"] == \"random\"\n", " tag = \" [CAPACITY CTRL]\" if is_ctrl else \"\"\n", " print(f\" +{cand:11s}{tag:17s} {task0} {r['score']:.4f} \"\n", " f\"(vs base {gain:+.4f}) \"\n", " f\"|w/z| {dict((k, round(v,3)) for k,v in r['tel_after'].items())}\")\n", " if is_ctrl:\n", " ctrl_score = r[\"score\"]\n", " else:\n", " trials.append((r[\"score\"], cand, r))\n", " history.append({k: v for k, v in r.items()\n", " if k not in (\"model\", \"disps\", \"names\")})\n", " if r is not best:\n", " del r[\"model\"], r[\"disps\"]\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " if not trials:\n", " break\n", " trials.sort(key=lambda t: -t[0])\n", " top_score, top_name, top_r = trials[0]\n", " # BAR 1: clear the capacity control by more than the seed spread.\n", " if ctrl_score is not None:\n", " over_ctrl = top_score - ctrl_score\n", " print(f\" capacity control at A={len(cur)+1}: {ctrl_score:.4f}\")\n", " print(f\" best real candidate '{top_name}': {top_score:.4f} \"\n", " f\"({over_ctrl:+.4f} vs control, bar is > {cfg.seed_spread:.4f})\")\n", " if over_ctrl <= cfg.seed_spread:\n", " print(f\" => BAR 1 REFUTED. '{top_name}' does not beat an arm that\")\n", " print(f\" contains NOTHING by more than seed noise. Whatever the\")\n", " print(f\" extra arm buys here is generic capacity, not learned\")\n", " print(f\" content -- the fourth bed in this program where a\")\n", " print(f\" capacity control dissolved an apparent win.\")\n", " print(f\" STOPPING at A={len(cur)}.\")\n", " break\n", " if top_score <= best[\"score\"] + cfg.seed_spread:\n", " print(f\" => no candidate beat {best['score']:.4f} by > seed spread. \"\n", " f\"STOPPING at A={len(cur)}.\")\n", " print(f\" The dilution law bit: more arms now cost more than they add.\")\n", " break\n", " print(f\" => keeping '{top_name}' (+{top_score-best['score']:.4f} vs base, \"\n", " f\"+{top_score-ctrl_score:.4f} vs control)\")\n", " for k in (\"model\", \"disps\"):\n", " if k in best:\n", " del best[k]\n", " best = evaluate_subset(cur + [top_name], top_name)\n", " cur = cur + [top_name]\n", " remaining = [n for n in remaining if n != top_name]\n", "\n", " # ---- capacity verdict, stated against the bars ----\n", " line(\"CAPACITY VERDICT\")\n", " base_s = history[0][\"score\"]\n", " r1 = [h for h in history if len(h[\"subset\"]) == len(cfg.base_arms) + 1]\n", " ctrl_r1 = next((h for h in r1 if ctrl_name in h[\"subset\"]), None)\n", " real_r1 = [h for h in r1 if ctrl_name not in h[\"subset\"]]\n", " if ctrl_r1 and real_r1:\n", " cs = ctrl_r1[\"score\"]\n", " print(f\" base A={len(cfg.base_arms)}: {base_s:.4f}\")\n", " print(f\" +{ctrl_name} (contains nothing): {cs:.4f} ({cs-base_s:+.4f})\")\n", " if cs > base_s + cfg.seed_spread:\n", " print(f\" => an EMPTY arm gains {cs-base_s:+.4f}. That is generic capacity,\")\n", " print(f\" exactly as FT4 Part III measured for a random bank. Every real\")\n", " print(f\" arm's gain must therefore be read against {cs:.4f}, not {base_s:.4f}.\")\n", " print(f\" real candidates at the same arm count:\")\n", " for h in sorted(real_r1, key=lambda x: -x[\"score\"]):\n", " nm = [n for n in h[\"subset\"] if n not in cfg.base_arms][0]\n", " v = h[\"score\"] - cs\n", " verdict = \"CLEARS\" if v > cfg.seed_spread else \"does not clear\"\n", " print(f\" {nm:12s} {h['score']:.4f} {v:+.4f} vs control -> {verdict}\")\n", " cleared = [h for h in real_r1 if h[\"score\"] - cs > cfg.seed_spread]\n", " if not cleared:\n", " print(f\" => BAR 3 REFUTED: no real arm beats an empty one by > \"\n", " f\"{cfg.seed_spread:.4f}.\")\n", " print(f\" On this substrate, extra arms buy CAPACITY, not content.\")\n", " print(f\" The 2-arm collective stands; the routing story does not\")\n", " print(f\" extend to more arms. Ship the verdict with the artifacts.\")\n", " else:\n", " print(f\" => {len(cleared)} arm(s) clear the capacity control. Learned\")\n", " print(f\" content is doing work beyond generic enrichment.\")\n", " if ctrl_name in cur:\n", " print(f\" !! '{ctrl_name}' was SELECTED. BAR 3 refuted by definition.\")\n", "\n", " # ---- TWO-SEED CONFIRMATION (house rule) ----\n", " # \"Two seeds per claim, even where cross-seed agreement runs to four\n", " # decimals.\" The sweep runs one seed for speed; the winning subset is\n", " # re-aligned across confirm_seeds and the spread is reported next to the\n", " # margin it is supposed to license.\n", " line(\"TWO-SEED CONFIRMATION OF THE WINNER\")\n", " conf = []\n", " for csd in cfg.confirm_seeds:\n", " sd = csd\n", " r = evaluate_subset(cur, f\"confirm-s{csd}\")\n", " conf.append(r)\n", " print(f\" seed {csd}: {task0} {r['score']:.4f} | \"\n", " f\"|w/z| {dict((k, round(v,3)) for k,v in r['tel_after'].items())}\")\n", " scores = [r[\"score\"] for r in conf]\n", " spread = max(scores) - min(scores)\n", " best = max(conf, key=lambda r: r[\"score\"])\n", " for r in conf:\n", " if r is not best:\n", " del r[\"model\"], r[\"disps\"]\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " sd = cfg.align_seeds[0]\n", " print(f\" spread {spread:.4f} over {len(scores)} seeds \"\n", " f\"(prereg seed_spread {cfg.seed_spread:.4f})\")\n", " if spread > cfg.seed_spread:\n", " print(f\" !! spread EXCEEDS the value the bars were written against. Every\")\n", " print(f\" !! margin in the table above should be re-read at this noise level.\")\n", "\n", " model, disps, names = best[\"model\"], best[\"disps\"], best[\"names\"]\n", " rows, tel, tel_after, alarms = None, best[\"tel_before\"], best[\"tel_after\"], best[\"alarms\"]\n", "\n", " # ---- full suite on the winner ----\n", " line(f\"FULL SUITE -- winner {cur}\")\n", " rows = {}\n", " masks = [(\"OFF\", [False] * len(names))] + \\\n", " [(f\"{n}-only\", [j2 == i for j2 in range(len(names))])\n", " for i, n in enumerate(names)] + \\\n", " [(\"COLLECTIVE\", [True] * len(names))]\n", " for label, mask in masks:\n", " set_active(disps, mask)\n", " rows[label] = {k: sts_eval(model, tok, v, cfg) for k, v in tasks.items()}\n", " set_active(disps, [True] * len(names))\n", " tk = list(tasks)\n", " print(f\" {'config':14s}\" + \"\".join(f\"{t:>9s}\" for t in tk) + f\"{'mean':>9s}\")\n", " for label, _ in masks:\n", " vals = [rows[label][t][\"spearman\"] for t in tk]\n", " print(f\" {label:14s}\" + \"\".join(f\"{v:>9.4f}\" for v in vals)\n", " + f\"{np.mean(vals):>9.4f}\")\n", " print(f\"\\n |w/z| final {dict((k, round(v,4)) for k,v in tel_after.items())}\")\n", " clean = [t for t in tk if t not in (task0,)]\n", " print(f\" clean-gauge mean (excludes the selection gauge {task0}): \"\n", " f\"{np.mean([rows['COLLECTIVE'][t]['spearman'] for t in clean]):.4f}\")\n", "\n", " pbr = None\n", " if cfg.per_block_report:\n", " line(\"PER-BLOCK ROUTING\")\n", " pbr = per_block_routing(model, tok, tasks[task0][0], cfg, disps, names)\n", "\n", " sweep = history\n", " best_cell = {\"subset\": cur, \"tau\": tau, \"seed\": sd}\n", " base = {k: {\"spearman\": rows[\"OFF\"][k][\"spearman\"]} for k in tasks}\n", "\n", " # ---- ship ----\n", " line(\"SAVE\")\n", " dck = DispatchCheckpoint(dispatch=[{\"dispatch\": dp.dispatch.detach().cpu(),\n", " \"key_proj\": dp.key_proj.detach().cpu()}\n", " for dp in disps],\n", " meta={\"name\": cfg.run_name, \"anchors\": names,\n", " \"base_model_id\": cfg.trunk_repo,\n", " \"emb\": cfg.align_emb, \"tau\": best[\"tau\"],\n", " \"seed\": best[\"seed\"], \"mix_rows\": cfg.mix_rows,\n", " \"alarms\": alarms})\n", " dpath = os.path.join(cfg.out_dir, f\"{cfg.run_name}.dispatch.pt\")\n", " dck.save(dpath)\n", " json.dump({\"baseline\": base, \"rows\": rows, \"telemetry_before\": tel,\n", " \"telemetry_after\": tel_after, \"alarms\": alarms,\n", " \"sweep\": [{k: v for k, v in r.items() if k != \"rows\"} for r in sweep],\n", " \"best\": best_cell,\n", " \"per_block_routing\": pbr, \"amplitude_decomposition\": amp_dec,\n", " \"anchors\": {k: os.path.basename(v) for k, v in paths.items()},\n", " \"config\": asdict(cfg)},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " json.dump(asdict(cfg), open(os.path.join(cfg.out_dir, \"config.json\"), \"w\"),\n", " indent=2, default=str)\n", " open(os.path.join(cfg.out_dir, \"README.md\"), \"w\",\n", " encoding=\"utf-8\", newline=\"\\n\").write(CARD)\n", " keep = {os.path.basename(q) for q in paths.values()}\n", " stray = [f for f in os.listdir(cfg.out_dir)\n", " if f.endswith(\".pt\") and f not in keep and not f.endswith(\".dispatch.pt\")]\n", " if stray:\n", " print(f\" !! {stray} in out_dir but not part of this run -- upload_folder\")\n", " print(f\" !! ships the WHOLE folder, so these would land in {cfg.hf_path}.\")\n", " print(f\" !! (this is exactly how amoe/sts-combo got the solo anchor.)\")\n", " print(f\" {dpath}\")\n", " print(f\" anchors: {', '.join(sorted(keep))}\")\n", " for f in sorted(os.listdir(cfg.out_dir)):\n", " fp = os.path.join(cfg.out_dir, f)\n", " if os.path.isfile(fp):\n", " print(f\" {f:44s} {os.path.getsize(fp)/1e6:>7.2f} MB\")\n", " pub.push(\"final: 2 anchors + dispatch + card\")\n", " if pub.ok:\n", " print(f\" https://huggingface.co/{cfg.hf_repo}/tree/main/{cfg.hf_path}\")\n", " return model, disps, names, rows\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " MODEL, DISPS, NAMES, ROWS = run(CFG)" ], "metadata": { "id": "hsx4Z-umidgz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#!/usr/bin/env python3\n", "\"\"\"\n", "consolidate_arms.py -- build the canonical AMOE arm library for captionbert-8192-v2.\n", "\n", " python consolidate_arms.py --dry-run\n", " python consolidate_arms.py\n", "\n", "WHY\n", " Five campaign folders each carry their own anchor copies. Measured on the hub:\n", " 16 anchor files, 13 unique, 19.8 MB redundant -- and worse, amoe/moe-v2\n", " RETRAINED equiv and simplify instead of reusing them (sha f13ad5ad vs\n", " 0029ad69). Twelve minutes burned, and the moe-vs-moe-v2 comparison was not\n", " dispatch-only the way it was reported.\n", "\n", " This writes amoe/arms/ : one canonical copy per role, plus ARMS.json carrying\n", " provenance, training spec, measured results and the CAPACITY-CONTROL VERDICT\n", " for each. Future cells resolve arms from that manifest and train only what is\n", " genuinely missing.\n", "\n", "WHAT IT DOES NOT DO\n", " It does not delete the campaign folders. Those are the records that back the\n", " published numbers -- including moe-v2's redundantly-trained pair, which has to\n", " stay or moe-v2's metrics become unreproducible. The only deletion is one true\n", " stray: amoe/sts-combo/captionbert-v2-sts-anchor.anchor.pt, a byte-identical\n", " copy of the amoe/sts anchor that my out_dir bug swept into the wrong folder.\n", "\n", "Needs HF_TOKEN with write. Pure ASCII.\n", "\n", "RUNS BOTH WAYS. As a Colab cell, edit the two constants below and paste --\n", "argparse is only consulted when this is genuinely a command line, because\n", "Jupyter injects `-f ` and a bare parse_args() dies on it.\n", "\"\"\"\n", "\n", "import argparse, json, os, sys, tempfile, shutil\n", "from types import SimpleNamespace\n", "\n", "from huggingface_hub import hf_hub_download, HfApi\n", "\n", "REPO = \"AbstractPhil/captionbert-8192-v2\"\n", "LIB = \"amoe/arms\"\n", "\n", "# name -> (source path on the hub, expected sha16, spec)\n", "# Results are MEASURED, from amoe/*/metrics.json. The capacity verdict is the\n", "# only comparison that decides whether an arm is worth carrying: FT4 Part III\n", "# measured a random bank matching real fitted frames, so an arm must beat an\n", "# UNTRAINED arm AT THE SAME ARM COUNT, not merely beat the smaller base.\n", "ARMS = {\n", " \"equiv\": {\n", " \"src\": \"amoe/moe/equiv.anchor.pt\", \"sha16\": \"0029ad692423b0a2\",\n", " \"data\": [\"sentence-transformers/all-nli[triplet] (200k, hard negatives)\"],\n", " \"relation\": \"semantic equivalence / entailment\", \"steps\": 1500,\n", " \"solo\": {\"STS-B\": 0.7254, \"SICK-R\": 0.7550},\n", " \"verdict\": \"CORE. Best SICK-R of any arm, solo or mixed.\",\n", " \"note\": \"space/draws .284 -- NLI premises repeat; the question-space \"\n", " \"guard fires. 4000 steps OVERFITS: see sts-4000.\",\n", " },\n", " \"simplify\": {\n", " \"src\": \"amoe/moe/simplify.anchor.pt\", \"sha16\": \"8e419f69350882c1\",\n", " \"data\": [\"sentence-transformers/simple-wiki[pair]\",\n", " \"sentence-transformers/altlex[pair]\",\n", " \"sentence-transformers/sentence-compression[pair]\"],\n", " \"relation\": \"simplification / compression\", \"steps\": 1500,\n", " \"solo\": {\"STS-B\": 0.7400, \"SICK-R\": 0.7075},\n", " \"verdict\": \"CORE. Best STS-B solo. Complementary to equiv on the TASK \"\n", " \"axis, which is the precondition a mixture needs.\",\n", " \"note\": \"100% distinct anchors across all three sources. No hard \"\n", " \"negatives (pair-only).\",\n", " },\n", " \"paraphrase\": {\n", " \"src\": \"amoe/collective/paraphrase.anchor.pt\", \"sha16\": \"50132291b26c5dd2\",\n", " \"data\": [\"sentence-transformers/quora-duplicates[triplet] (hard negatives)\"],\n", " \"relation\": \"paraphrase, question/forum register\", \"steps\": 1500,\n", " \"capacity\": {\"with\": 0.7684, \"random_same_count\": 0.7538,\n", " \"margin\": 0.0146, \"bar\": 0.0054, \"result\": \"CLEARS\"},\n", " \"verdict\": \"KEEP. The only arm that beat an untrained arm at the same \"\n", " \"count. +.0146 against a .0054 bar -- ~3x. Covers STS-B's \"\n", " \"forum register, which neither core arm carries.\",\n", " },\n", " \"lexical\": {\n", " \"src\": \"amoe/collective/lexical.anchor.pt\", \"sha16\": \"e855ab2b827e9751\",\n", " \"data\": [\"WordNet term<->gloss, 156k pairs, 42% with a same-term \"\n", " \"different-sense hard negative\"],\n", " \"relation\": \"lexical grounding / sense disambiguation\", \"steps\": 1500,\n", " \"capacity\": {\"with\": 0.7518, \"random_same_count\": 0.7538,\n", " \"margin\": -0.0020, \"bar\": 0.0054, \"result\": \"FAILS\"},\n", " \"verdict\": \"DO NOT RETRAIN. Loses to an arm containing NOTHING. The \"\n", " \"gain over base (+.0025) is generic capacity. Kept for the \"\n", " \"record and because the DATA construction is reusable.\",\n", " },\n", " \"topical\": {\n", " \"src\": \"amoe/collective/topical.anchor.pt\", \"sha16\": \"892e6aba8746eb94\",\n", " \"data\": [\"sentence-transformers/specter[triplet] (200k, citation pairs)\"],\n", " \"relation\": \"topical relatedness\", \"steps\": 1500,\n", " \"capacity\": {\"with\": 0.7508, \"random_same_count\": 0.7538,\n", " \"margin\": -0.0030, \"bar\": 0.0054, \"result\": \"FAILS\"},\n", " \"verdict\": \"DO NOT RETRAIN. Same as lexical: below the empty arm.\",\n", " },\n", " \"random\": {\n", " \"src\": \"amoe/collective/random.anchor.pt\", \"sha16\": \"9a04eb62afcc79cb\",\n", " \"data\": [], \"relation\": \"NEGATIVE / CAPACITY CONTROL -- untrained\",\n", " \"steps\": 0,\n", " \"capacity\": {\"with\": 0.7538, \"base\": 0.7493, \"margin\": 0.0045,\n", " \"result\": \"an EMPTY arm gains +.0045 -- generic capacity, \"\n", " \"just under the .0054 seed spread\"},\n", " \"verdict\": \"ALWAYS INCLUDE AS A CANDIDATE. This is the baseline every \"\n", " \"real arm must clear. Never trained, so it is free.\",\n", " },\n", " \"sts-4000\": {\n", " \"src\": \"amoe/sts/captionbert-v2-sts-anchor.anchor.pt\",\n", " \"sha16\": \"45bcbf0ccb6d2005\",\n", " \"data\": [\"sentence-transformers/all-nli[triplet] (200k)\"],\n", " \"relation\": \"semantic equivalence\", \"steps\": 4000,\n", " \"solo\": {\"STS-B\": 0.7078, \"SICK-R\": 0.7611},\n", " \"verdict\": \"SUPERSEDED by equiv (1500 steps). Its STS-B PEAKED at step \"\n", " \"1000 (.7256) then fell .0178 to .7078 while SICK-R kept \"\n", " \"climbing -- question-space exhaustion, exactly the failure \"\n", " \"FT3 exp013 named. Kept as the evidence for why 1500.\",\n", " },\n", " \"sts-combo-500\": {\n", " \"src\": \"amoe/sts-combo/captionbert-v2-sts-anchor-combo.anchor.pt\",\n", " \"sha16\": \"d7c85b05540bc14e\",\n", " \"data\": [\"all-nli + simple-wiki + altlex + sentence-compression\"],\n", " \"relation\": \"blended (equivalence + compression in ONE arm)\",\n", " \"steps\": 500,\n", " \"solo\": {\"STS-B\": 0.7344, \"SICK-R\": 0.7454},\n", " \"verdict\": \"INCOMPLETE (stopped at step 500 of 4000). Useful datapoint: \"\n", " \"at matched step it beat the all-nli solo arm (.7344 vs \"\n", " \".7160), which is what motivated splitting the relations \"\n", " \"into separate arms instead of widening one.\",\n", " },\n", "}\n", "\n", "STRAY = \"amoe/sts-combo/captionbert-v2-sts-anchor.anchor.pt\"\n", "\n", "README = \"\"\"---\n", "license: mit\n", "tags: [amoe, adapter, arm-library, sentence-similarity]\n", "base_model: AbstractPhil/captionbert-8192-v2\n", "---\n", "\n", "# AMOE arm library\n", "\n", "Canonical, deduplicated anchors for the frozen `captionbert-8192-v2` trunk.\n", "**Resolve arms from here. Do not retrain what is already in `ARMS.json`.**\n", "\n", "Every anchor is 12 sites x RelayPatchwork (16 slots, K=64, D=4, tau=0.1,\n", "hidden=178, gate_init=-3.0, zero-init head) = 1.64M params, trained with MNRL on\n", "the FROZEN trunk, pure Adam wd=0, fp32/TF32 off.\n", "\n", "## The verdicts that matter\n", "\n", "An arm earns its place by beating an **untrained arm at the same arm count**, not\n", "by beating the smaller base -- FT4 Part III measured a fully random bank matching\n", "real fitted frames while lifting a probe, so raw gains are capacity until proven\n", "otherwise. Measured here: an empty arm gains **+.0045** on STS-B.\n", "\n", "| arm | relation | capacity margin | verdict |\n", "|---|---|---|---|\n", "| `equiv` | equivalence | core | best SICK-R |\n", "| `simplify` | compression | core | best STS-B solo |\n", "| `paraphrase` | question paraphrase | **+.0146** | **CLEARS** |\n", "| `lexical` | WordNet sense | -.0020 | fails |\n", "| `topical` | citation relatedness | -.0030 | fails |\n", "| `random` | none (control) | (+.0045 baseline) | always a candidate |\n", "\n", "Best measured roster: **equiv + simplify + paraphrase**, STS-B .7684, mean .7287\n", "across 8 STS tasks against the bare trunk's .6077. Greedy stopped at A=3 -- every\n", "A=4 candidate, including random, came in below.\n", "\n", "## Why not more arms\n", "\n", "`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` sums over ALL anchors, so arms SPLIT a\n", "roughly conserved amplitude budget rather than adding to it (sum .850 at A=2 ->\n", ".757 at A=6; measured .868 at A=3). And sharper routing does not rescue it:\n", "tau .10/.05/.02 gave STS-B .7520/.7514/.7468 while key separation ROSE. The value\n", "is graded blending, not selection.\n", "\n", "## Loading\n", "\n", "```python\n", "import json\n", "from huggingface_hub import hf_hub_download\n", "arms = json.load(open(hf_hub_download(\n", " \"AbstractPhil/captionbert-8192-v2\", \"amoe/arms/ARMS.json\")))\n", "path = hf_hub_download(\"AbstractPhil/captionbert-8192-v2\",\n", " f\"amoe/arms/{arms['arms']['equiv']['file']}\")\n", "```\n", "\n", "Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the\n", "amoe README is the *safetensors* layout -- a different serializer.)\n", "\"\"\"\n", "\n", "\n", "# ---- Colab-cell config: edit these and paste, no CLI needed ----\n", "DRY_RUN = True # set False to actually upload + delete the stray\n", "KEEP_STRAY = False # True leaves amoe/sts-combo's duplicate in place\n", "\n", "\n", "def say(m): print(m)\n", "def ok(m): print(\" [ok] \" + m)\n", "def info(m): print(\" [ ] \" + m)\n", "def die(m): print(\" [FAIL] \" + m); sys.exit(1)\n", "\n", "\n", "def _opts():\n", " \"\"\"\n", " Notebook -> module constants. Real CLI -> argparse. Never parse_args() bare:\n", " Jupyter passes `-f /root/.../kernel-xxxx.json` and argparse exits 2 on it.\n", " \"\"\"\n", " in_nb = (\"get_ipython\" in globals() or \"ipykernel\" in sys.modules\n", " or any(\"kernel-\" in x and x.endswith(\".json\") for x in sys.argv))\n", " if in_nb:\n", " return SimpleNamespace(dry_run=DRY_RUN, keep_stray=KEEP_STRAY)\n", " ap = argparse.ArgumentParser()\n", " ap.add_argument(\"--dry-run\", action=\"store_true\")\n", " ap.add_argument(\"--keep-stray\", action=\"store_true\")\n", " known, _ = ap.parse_known_args()\n", " return known\n", "\n", "\n", "def main():\n", " a = _opts()\n", "\n", " say(\"=\" * 70)\n", " say(\" consolidate AMOE arms -> \" + f\"{REPO}/{LIB}\")\n", " say(\"=\" * 70)\n", " if a.dry_run:\n", " print(\" [warn] DRY RUN -- nothing uploaded or deleted.\")\n", " print(\" [warn] In a notebook: set DRY_RUN = False at the top and re-run.\")\n", " print(\" [warn] On a CLI: drop --dry-run.\")\n", "\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok and not a.dry_run:\n", " die(\"no HF_TOKEN\")\n", " api = HfApi(token=tok) if tok else HfApi()\n", " stage = tempfile.mkdtemp(prefix=\"arms-\")\n", "\n", " # ---- pull, verify, stage ----\n", " say(\"\\n[1/3] verifying and staging\")\n", " try:\n", " from amoe.io.checkpoint import load_anchor\n", " have_amoe = True\n", " except ImportError:\n", " have_amoe = False\n", " info(\"amoe not installed -- skipping the load_anchor integrity check\")\n", "\n", " manifest = {\"trunk\": REPO, \"library\": LIB, \"arms\": {},\n", " \"roster_best\": {\"arms\": [\"equiv\", \"simplify\", \"paraphrase\"],\n", " \"tau\": 0.10, \"STS-B\": 0.7684, \"suite_mean\": 0.7287},\n", " \"capacity_baseline\": {\"empty_arm_gain\": 0.0045,\n", " \"seed_spread_bar\": 0.0054}}\n", " for name, spec in ARMS.items():\n", " p = hf_hub_download(REPO, spec[\"src\"])\n", " fn = f\"{name}.anchor.pt\"\n", " shutil.copy(p, os.path.join(stage, fn))\n", " n_t = None\n", " if have_amoe:\n", " ck = load_anchor(p) # enforces the addr.home drift buffer\n", " n_t = len(ck.adapters)\n", " blocks = sorted({k.split(\".\")[0] for k in ck.adapters})\n", " if len(blocks) != 12:\n", " die(f\"{name}: {len(blocks)} blocks, expected 12\")\n", " info(f\"{name:14s} <- {spec['src']:52s} \"\n", " f\"{os.path.getsize(p)/1e6:.2f} MB\"\n", " + (f\" {n_t} tensors\" if n_t else \"\"))\n", " manifest[\"arms\"][name] = {\n", " \"file\": fn, \"origin\": spec[\"src\"], \"sha16\": spec[\"sha16\"],\n", " \"relation\": spec[\"relation\"], \"data\": spec[\"data\"],\n", " \"steps\": spec[\"steps\"], \"verdict\": spec[\"verdict\"],\n", " **({\"solo_scores\": spec[\"solo\"]} if \"solo\" in spec else {}),\n", " **({\"capacity_control\": spec[\"capacity\"]} if \"capacity\" in spec else {}),\n", " **({\"note\": spec[\"note\"]} if \"note\" in spec else {}),\n", " }\n", " json.dump(manifest, open(os.path.join(stage, \"ARMS.json\"), \"w\"), indent=2)\n", " open(os.path.join(stage, \"README.md\"), \"w\", encoding=\"utf-8\",\n", " newline=\"\\n\").write(README)\n", " ok(f\"{len(ARMS)} arms staged + ARMS.json + README.md\")\n", "\n", " # ---- upload ----\n", " say(\"\\n[2/3] upload\")\n", " if a.dry_run:\n", " ok(f\"would push {stage} -> {REPO}/{LIB}\")\n", " else:\n", " api.upload_folder(folder_path=stage, path_in_repo=LIB, repo_id=REPO,\n", " commit_message=\"canonical AMOE arm library\")\n", " ok(f\"https://huggingface.co/{REPO}/tree/main/{LIB}\")\n", "\n", " # ---- the one true stray ----\n", " say(\"\\n[3/3] stray cleanup\")\n", " present = set(api.list_repo_files(REPO))\n", " if STRAY in present and not a.keep_stray:\n", " info(f\"{STRAY}\")\n", " info(\" byte-identical to amoe/sts/... -- swept in by an out_dir bug\")\n", " if not a.dry_run:\n", " api.delete_file(STRAY, repo_id=REPO,\n", " commit_message=\"remove stray anchor from the combo folder\")\n", " ok(\"deleted\")\n", " else:\n", " info(\"no stray (or --keep-stray)\")\n", "\n", " say(\"\\n\" + \"=\" * 70)\n", " say(\" NEXT RUN: resolve from the library instead of retraining\")\n", " say(' (\"equiv\", {\"kind\": \"hub\", \"path\": \"amoe/arms/equiv.anchor.pt\"})')\n", " say(' (\"simplify\", {\"kind\": \"hub\", \"path\": \"amoe/arms/simplify.anchor.pt\"})')\n", " say(' (\"paraphrase\", {\"kind\": \"hub\", \"path\": \"amoe/arms/paraphrase.anchor.pt\"})')\n", " say(' (\"random\", {\"kind\": \"hub\", \"path\": \"amoe/arms/random.anchor.pt\"})')\n", " say(\" lexical and topical lose to an EMPTY arm -- do not retrain them.\")\n", " say(\" That is 4 x 6 min of anchor training saved per campaign.\")\n", " say(\"=\" * 70)\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " main()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 991, "referenced_widgets": [ "732429e3833d4c02a2f1503ae8c34aa0", "8b95a106bc994bfaa75418d3016692d1", "3da2949038a3492ab7dfcb1b6abb4b65", "7dc3f7cac63f476cbeba239e7d46c107", "cb2d299e1c1c40dfad84340f50f09b20", "1e0ae0397a894d2c8edef7242a960ae2", "b5dad68a6dcb4de68711b5b61567a499", "53471497a94841eb943484b47e471b35", "6d0bfd29595d4db0b9b6c282fd326774", "321dde705f3d4001980fec9115d7fb2a", "488d18daa5684c16a54e4c8bc49e9276", "9a14405016e24afeb841015ddce18c8b", "352d4dadfccb4162ac3e87ccbb7c845c", "8e64ebf57b9f4f43b3c815c41b171000", "b3e3c2dbb4c14994989f73761d0ca9a9", "5cf6ba1a84fa4f47a87afa83af76730e", "4134e46f9cd648e4a5ee1602ab5aa00a", "2ebf8c5f01994c929d5ec326527e060d", "6b3935cbbf6a49319e8ff9c93042b169", "ec5ee1dbfe3f4097b3681792cb24f64e", "7406c69aab44447fa0a832d741a0bbb1", "7d09abf834dc4f6bbac1a098950811ee", "3e8a9146fd294411971c2139799e774b", "9e2cdf0d16324107858996204f8ec149", "ecde5f9af5ba4ac398f02b4d1041d6dc", "34c2e59c5ce8445dbd7d08bb6df5ba7b", "99f045b2412e442da22ff75abf8f5f3e", "b1563888d3c6482497d88adb27d662d2", "86fde36705cc49079b63787b9ea6b2a2", "2328056eae044a2c97f77a8e7059f6b8", "8e948abc8da3488baaadf49ecec2f053", "26b2afb59f3740eaa82d2f60d64ef4ba", 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"d62aab92e0664e10a50fa6b4287aabd1", "a68d0453b0c5413fbe4736ce580b18d9", "90a17bc7f8fc4dfdbbd87ef663e6b8d0", "ac10ccb7885944f0b4642318645292cd", "7c58877331134c1da63d1ef30508f1ee", "b7d361075fd44c699972a596421a31e8", "a5410ce9bf934f7aa8eee94292b25e7a", "269313d1196544799234e21543772349", "2c66527cb3394236891e4ce32202dd48", "8103a79307c84ae8a2038491df91381a", "11c507c8f5ff4563a07b0e04cb346612", "0c9ac044385e44469316cfb378bb6d5c", "4e89bde08a3446a2877b1f2a6355edff", "93d57725c48e4f2ea286a6c82e9724bd", "3c07bbf0fcc6428bbc6e2cd9a677f945", "c06fb128a636472589389c60d7534f22", "31628cb699b9484d91e4af2216c2a09c", "55539db65b074ada9ee8232394f56724", "4cb2c7f626be423fab3fe006c389af50", "995973647fc047a29ebad13698cd3bb2", "adeae30f8680491caca1d9539182adb1", "d272046dbb0f4225b780aff71e5a1a49", "0b33afaa47be4cf8a34cfdd0095d7335", "5601ec6cd91b4b4ba96969b5279af280", "a27d3568284f42c2b097feb597953484", "0c74922bb95341aa950dfd24e66b3f94", "396a1d7461c348728863366d4dfb6035", "b3976ca84f694c61bd754131302a5ea9", "7420212444004e729e6f9c9fcaf93369", "de21fcaea9a54282a11ab57b72af2706", "30761b46e5cb47c49bfbf7ca9a7ff15e", "260832dd0c9a47128bdb3297a8b89a8b", "ecc1e3a2b9df4c41a0284d969a480844", "efca804a0c4c4e4cabec3d37ab483c20", "dbf4ee73e05f4edda5884408611fee3a", "1e01129eb1754c32a221f7d6aecb6bc2", "48080473dad648cbb637beaa3956e036", "eb3fa24709524772a82a0a0ac491bd2f", "3251387264934b42a7dbf2d81ed696c2", "b7dda83296ad45f2bcb042f8aaa179df", "2262ca33db0c441194eb76b8d03091b8", "730e4b8763ef4128b32b53b9f5780eaa", "417500b9402d4e09aa85ff50bd5b3927", "b2593df83eb445d7b8f343e576143366", "0fd06834d7ad43aa88a1d749d79fc39c", "cf739d49c44042e798438e1fc38963cd", "ebe14383df8643e5bc32eacba90a3bde", "0020c06b7b6e40cda2d5706aac685374", "5ef43250ac474947b6b76ef690082323", "69fe10211b2c4b7dac7e49c0413e4e45", "767e6bba8e764e29a4cd2a0d847a9acb", "f7fe0958b321431f850ac4db161cace4", "16c2b4210ffc459a9b668279297d966f", "c0517323945549c0b6b4fdb6d73e7f6b", "62db0124e8c64764a2a8cc98c6cecb91", "154f644a040843e08148568cafde301b", "6d5938697b1d47f1ad62a72a52eab6b5", "b0fd78c6aacc45f5a8796a3b76f6909c", "249c2cc7016d4379ba56a5f68a8dcba2", "0f8faf1003994ffe82ad1d36b3f501e6", "5897e252c67f41d49a96ca6a7f5b4460", "9a74ec6f402c42cc8e9a60b91b622d88", "8d9e9bae15674e7db995bfd2d40e17ea", "68f0cb84dee149a69a64c318ecca24d2", "201a4dd57d5a4c858e3b5edae59fe2c3", "bd8cd079af7f4bd495415b6d2b9a3c41", "1ebf63dc5b0d4d8ebba0c7a845df0a2e", "80410fbee5fc439c94a724e2e95b721c", "5797b922976847829495582afb8e0b9a", "ed0c9a0cda644e648e9dc7be59617bb6", "6aca865ecf7f467fa18e1befc6849776", "3ce951bbf3b74e259adf1d2a0adcb917", "8917c1c770af4096837ac3244a6ddacc" ] }, "id": "DprRWY2B09GE", "outputId": "1b2b9d3a-3fde-475f-8ea4-2ad82c3b655e" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "======================================================================\n", " consolidate AMOE arms -> AbstractPhil/captionbert-8192-v2/amoe/arms\n", "======================================================================\n", " [warn] DRY RUN -- nothing uploaded or deleted.\n", " [warn] In a notebook: set DRY_RUN = False at the top and re-run.\n", " [warn] On a CLI: drop --dry-run.\n", "\n", "[1/3] verifying and staging\n", " [ ] equiv <- amoe/moe/equiv.anchor.pt 6.60 MB 120 tensors\n", " [ ] simplify <- amoe/moe/simplify.anchor.pt 6.61 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/paraphrase.anchor.pt: reconstructing file: 0%| | 0.00B / 6.61MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "732429e3833d4c02a2f1503ae8c34aa0" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/paraphrase.anchor.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "9a14405016e24afeb841015ddce18c8b" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] paraphrase <- amoe/collective/paraphrase.anchor.pt 6.61 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/lexical.anchor.pt: reconstructing file: 0%| | 0.00B / 6.61MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "3e8a9146fd294411971c2139799e774b" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/lexical.anchor.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "615cd6cb3dc44a788971435dfd51f83e" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] lexical <- amoe/collective/lexical.anchor.pt 6.61 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/topical.anchor.pt: reconstructing file: 0%| | 0.00B / 6.61MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "05b44b94e6074b0d9f72603ce6f7ed01" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/topical.anchor.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "41eb37959c1a40dbabf575545eb1b22a" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] topical <- amoe/collective/topical.anchor.pt 6.61 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/random.anchor.pt: reconstructing file: 0%| | 0.00B / 6.60MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "269313d1196544799234e21543772349" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/collective/random.anchor.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "4cb2c7f626be423fab3fe006c389af50" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] random <- amoe/collective/random.anchor.pt 6.60 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/sts/captionbert-v2-sts-anchor.ancho(…): reconstructing file: 0%| | 0.00B / 6.61MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "de21fcaea9a54282a11ab57b72af2706" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/sts/captionbert-v2-sts-anchor.ancho(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "2262ca33db0c441194eb76b8d03091b8" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] sts-4000 <- amoe/sts/captionbert-v2-sts-anchor.anchor.pt 6.61 MB 120 tensors\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/sts-combo/captionbert-v2-sts-anchor(…): reconstructing file: 0%| | 0.00B / 6.61MB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "f7fe0958b321431f850ac4db161cace4" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "amoe/sts-combo/captionbert-v2-sts-anchor(…): downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "8d9e9bae15674e7db995bfd2d40e17ea" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " [ ] sts-combo-500 <- amoe/sts-combo/captionbert-v2-sts-anchor-combo.anchor.pt 6.61 MB 120 tensors\n", " [ok] 8 arms staged + ARMS.json + README.md\n", "\n", "[2/3] upload\n", " [ok] would push /tmp/arms-430rvgz2 -> AbstractPhil/captionbert-8192-v2/amoe/arms\n", "\n", "[3/3] stray cleanup\n", " [ ] amoe/sts-combo/captionbert-v2-sts-anchor.anchor.pt\n", " [ ] byte-identical to amoe/sts/... -- swept in by an out_dir bug\n", "\n", "======================================================================\n", " NEXT RUN: resolve from the library instead of retraining\n", " (\"equiv\", {\"kind\": \"hub\", \"path\": \"amoe/arms/equiv.anchor.pt\"})\n", " (\"simplify\", {\"kind\": \"hub\", \"path\": \"amoe/arms/simplify.anchor.pt\"})\n", " (\"paraphrase\", {\"kind\": \"hub\", \"path\": \"amoe/arms/paraphrase.anchor.pt\"})\n", " (\"random\", {\"kind\": \"hub\", \"path\": \"amoe/arms/random.anchor.pt\"})\n", " lexical and topical lose to an EMPTY arm -- do not retrain them.\n", " That is 4 x 6 min of anchor training saved per campaign.\n", "======================================================================\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CC12M MODERNBERT REPAIR GATE -- single Colab cell\n", "#\n", "# The 10 chunks ModernBERT was missing are now present. Before spending 33M rows\n", "# on them, prove the repair used the SAME recipe as the original 56 -- because a\n", "# consensus target built from inconsistent embeddings is the same defect the\n", "# 4-expert fallback was rejected for, only harder to see.\n", "#\n", "# THREE THINGS ARE CHECKED, and only the third is novel:\n", "#\n", "# 1. CAPTION FIELD + ROW ALIGNMENT. Re-embed captions with the real ModernBERT\n", "# and match against the stored tensor row-for-row. cos >= 0.999 or the\n", "# field/order is wrong. (Same instrument as the v2 stage-0 parity gate.)\n", "#\n", "# 2. TRUNCATION LENGTH -- THE ONE THAT MATTERS HERE. ModernBERT takes 8192\n", "# context; bert, roberta, albert and distil all cap at 512. If the repair\n", "# embedded at full length while the original 56 truncated at 512 (or the\n", "# reverse), the repaired rows carry a DIFFERENT AMOUNT OF TEXT into the\n", "# consensus than the other four experts do. The cell re-embeds at several\n", "# max_lengths and reports which one the stored tensor actually matches, for\n", "# a REPAIRED chunk and a NEVER-MISSING chunk. They must agree.\n", "#\n", "# 3. POOLING. mean-pooled vs CLS, same comparison. A silent switch here would\n", "# also pass check 1 on short captions and fail on long ones.\n", "#\n", "# Also measured, because it was the working hypothesis for WHY modern faulted:\n", "# the caption-length distribution of repaired vs never-missing chunks. A ranged\n", "# read of the first 600 captions found the repaired chunks SHORTER (mean 212 vs\n", "# 235 chars, ratio .902), so a distribution shift is already ruled out at the\n", "# head of the file -- but a single 6,016-char outlier turned up in chunk 21,\n", "# 3.6x anything in a never-missing chunk. This cell scans the FULL caption files\n", "# for that tail, since a handful of pathological rows OOMing a job is a\n", "# completely different (and benign) story from a biased corpus.\n", "#\n", "# Colab-cell-safe: no bare argparse, no __file__, self-running on paste.\n", "# ============================================================================\n", "\n", "import gc, json, os, subprocess, sys\n", "from dataclasses import dataclass\n", "from typing import List, Tuple\n", "\n", "for _p in (\"transformers\", \"huggingface_hub\"):\n", " try:\n", " __import__(_p)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "from huggingface_hub import hf_hub_download\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " repo: str = \"AbstractPhil/conceptual-captions-12m-webdataset-berts\"\n", " modern_hf: str = \"answerdotai/ModernBERT-base\"\n", "\n", " # chunks that were missing modern and have now been repaired\n", " repaired: Tuple[int, ...] = (5, 7, 8, 21, 25, 26, 28, 32, 38, 46)\n", " # chunks that always had it -- the reference recipe\n", " reference: Tuple[int, ...] = (0, 1)\n", " # how many of each to actually pull (each modern_XXX.pt is ~1.5 GB)\n", " n_repaired_to_test: int = 2\n", " n_reference_to_test: int = 1\n", "\n", " caption_field_candidates: Tuple[str, ...] = (\n", " \"caption_llava\", \"caption\", \"caption_llava_short\")\n", " n_rows: int = 64 # rows per chunk for the parity checks\n", " lengths: Tuple[int, ...] = (128, 256, 512, 1024, 8192)\n", " poolings: Tuple[str, ...] = (\"mean\", \"cls\")\n", " min_cos: float = 0.999\n", "\n", " # full-file caption length scan (ranged reads, no full download)\n", " scan_lengths: bool = True\n", " scan_bytes: int = 40_000_000 # ~40 MB prefix per file, ~20k captions\n", "\n", " keep_shards: bool = False # delete the 1.5 GB .pt after each check\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CAPTION LOADING (memory-safe: json.load on a 120 MB file of 500k\n", "# strings costs ~1 GB, which is what killed an earlier attempt)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def caption_prefix(cfg, chunk: int, want: int, nbytes: int = 2_000_000):\n", " import requests\n", " url = (f\"https://huggingface.co/datasets/{cfg.repo}/resolve/main/\"\n", " f\"captions_{chunk:03d}.json\")\n", " r = requests.get(url, headers={\"Range\": f\"bytes=0-{nbytes}\"}, timeout=180)\n", " buf = r.content.decode(\"utf-8\", \"ignore\")\n", " dec = json.JSONDecoder()\n", "\n", " # the file is either a flat array of strings or an object of arrays\n", " i = buf.find(\"[\")\n", " if i < 0:\n", " return [], None\n", " field = None\n", " head = buf[:i]\n", " for f in cfg.caption_field_candidates:\n", " if f'\"{f}\"' in head:\n", " field = f\n", " break\n", " i += 1\n", " out = []\n", " while len(out) < want:\n", " while i < len(buf) and buf[i] in \" \\n\\r\\t,\":\n", " i += 1\n", " if i >= len(buf) or buf[i] != '\"':\n", " break\n", " try:\n", " s, i = dec.raw_decode(buf, i)\n", " except Exception:\n", " break\n", " out.append(s)\n", " return out, field\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# THE GATES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def probe_chunk(cfg, chunk: int, tok, mdl, label: str):\n", " \"\"\"Return the (max_length, pooling) that reproduces the stored tensor.\"\"\"\n", " line(f\"{label} chunk {chunk:03d}\")\n", " caps, field = caption_prefix(cfg, chunk, cfg.n_rows)\n", " if len(caps) < cfg.n_rows:\n", " print(f\" only parsed {len(caps)} captions -- widen scan window\")\n", " return None\n", " print(f\" caption field: {field or '(flat array)'} | \"\n", " f\"first: {caps[0][:64]!r}\")\n", "\n", " p = hf_hub_download(cfg.repo, f\"modern_{chunk:03d}.pt\", repo_type=\"dataset\")\n", " stored = torch.load(p, weights_only=True, map_location=\"cpu\")[: cfg.n_rows].float()\n", " print(f\" stored modern_{chunk:03d}.pt rows {tuple(stored.shape)} \"\n", " f\"({os.path.getsize(p)/1e9:.2f} GB on disk)\")\n", "\n", " best, table = None, []\n", " for pooling in cfg.poolings:\n", " for L in cfg.lengths:\n", " with torch.no_grad():\n", " t = tok(caps, max_length=L, padding=True, truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h = mdl(**t).last_hidden_state\n", " if pooling == \"cls\":\n", " e = h[:, 0]\n", " else:\n", " m = t[\"attention_mask\"].unsqueeze(-1).float()\n", " e = (h * m).sum(1) / m.sum(1).clamp(min=1)\n", " cos = F.cosine_similarity(e.float().cpu(), stored, dim=-1)\n", " table.append((pooling, L, float(cos.mean()), float(cos.min())))\n", " if best is None or cos.mean() > best[2]:\n", " best = (pooling, L, float(cos.mean()), float(cos.min()))\n", " del t, h, e\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " print(f\" {'pooling':>8s}{'max_len':>9s}{'cos mean':>11s}{'cos min':>10s}\")\n", " for pl, L, cm, cmin in table:\n", " mark = \" <-- MATCH\" if (pl, L) == (best[0], best[1]) and cm >= cfg.min_cos else \"\"\n", " print(f\" {pl:>8s}{L:>9d}{cm:>11.5f}{cmin:>10.5f}{mark}\")\n", " if not cfg.keep_shards:\n", " try:\n", " os.remove(p)\n", " except OSError:\n", " pass\n", " del stored\n", " gc.collect()\n", "\n", " if best[2] < cfg.min_cos:\n", " print(f\" *** NO RECIPE REPRODUCES THIS SHARD (best {best[2]:.5f} < \"\n", " f\"{cfg.min_cos}). Field, row order, or model revision differs.\")\n", " return None\n", " print(f\" => recipe: pooling={best[0]} max_length={best[1]} \"\n", " f\"cos {best[2]:.5f} (min {best[3]:.5f})\")\n", " return {\"chunk\": chunk, \"pooling\": best[0], \"max_length\": best[1],\n", " \"cos_mean\": best[2], \"cos_min\": best[3], \"field\": field}\n", "\n", "\n", "def scan_caption_tail(cfg, chunks, label):\n", " \"\"\"Full-ish scan for pathological outliers -- the OOM hypothesis.\"\"\"\n", " out = {}\n", " for c in chunks:\n", " caps, _ = caption_prefix(cfg, c, want=10**9, nbytes=cfg.scan_bytes)\n", " if not caps:\n", " continue\n", " L = np.array([len(x) for x in caps])\n", " out[c] = {\"n\": len(L), \"mean\": float(L.mean()),\n", " \"p99\": float(np.percentile(L, 99)), \"max\": int(L.max()),\n", " \"over_2000\": int((L > 2000).sum()),\n", " \"over_4000\": int((L > 4000).sum())}\n", " print(f\" {label:>14s} {c:03d} n={len(L):>6d} mean {L.mean():>6.0f} \"\n", " f\"p99 {np.percentile(L,99):>6.0f} max {L.max():>6d} \"\n", " f\">2k: {(L>2000).sum():>4d} >4k: {(L>4000).sum():>3d}\")\n", " return out\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(\"=\" * 76)\n", " print(\"CC12M MODERNBERT REPAIR GATE\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " print(f\"repaired chunks: {list(cfg.repaired)}\")\n", " print(f\"testing {cfg.n_repaired_to_test} repaired vs {cfg.n_reference_to_test} \"\n", " f\"reference | ~1.5 GB downloaded per chunk\")\n", "\n", " if cfg.scan_lengths:\n", " line(\"CAPTION LENGTH SCAN (the OOM hypothesis)\")\n", " print(\" ranged reads, no full download. A biased corpus and a few\")\n", " print(\" pathological rows are different stories with different fixes.\")\n", " ref_scan = scan_caption_tail(cfg, cfg.reference[:2], \"never-missing\")\n", " rep_scan = scan_caption_tail(cfg, cfg.repaired[:3], \"REPAIRED\")\n", " if ref_scan and rep_scan:\n", " rm = np.mean([v[\"mean\"] for v in ref_scan.values()])\n", " pm = np.mean([v[\"mean\"] for v in rep_scan.values()])\n", " rx = max(v[\"max\"] for v in ref_scan.values())\n", " px = max(v[\"max\"] for v in rep_scan.values())\n", " print(f\"\\n mean length ratio repaired/never-missing: {pm/rm:.3f}\")\n", " print(f\" longest caption seen: never-missing {rx}, repaired {px}\")\n", " if px > 2 * rx:\n", " print(\" => TAIL OUTLIERS in the repaired set. A few pathological\")\n", " print(\" rows OOMing the job is BENIGN: the other ~499,990 rows\")\n", " print(\" are ordinary and the repair is a re-run, not a shift.\")\n", " elif pm / rm < 0.95 or pm / rm > 1.05:\n", " print(\" => DISTRIBUTION SHIFT. The repaired chunks are not a random\")\n", " print(\" sample; adding them changes what the corpus is.\")\n", " else:\n", " print(\" => no length signal either way at this depth.\")\n", "\n", " tok = AutoTokenizer.from_pretrained(cfg.modern_hf)\n", " mdl = AutoModel.from_pretrained(cfg.modern_hf).to(DEVICE).eval()\n", " print(f\"\\n loaded {cfg.modern_hf} \"\n", " f\"({sum(p.numel() for p in mdl.parameters()):,} params)\")\n", "\n", " line(\"RECIPE PROBE\")\n", " refs = [probe_chunk(cfg, c, tok, mdl, \"REFERENCE\")\n", " for c in cfg.reference[: cfg.n_reference_to_test]]\n", " reps = [probe_chunk(cfg, c, tok, mdl, \"REPAIRED\")\n", " for c in cfg.repaired[: cfg.n_repaired_to_test]]\n", " refs = [r for r in refs if r]\n", " reps = [r for r in reps if r]\n", "\n", " line(\"VERDICT\")\n", " if not refs:\n", " print(\" REFERENCE chunk did not reproduce under ANY recipe.\")\n", " print(\" The problem is the probe, not the repair. Do not read the rest.\")\n", " return None\n", " if not reps:\n", " print(\" REPAIRED chunk did not reproduce under any recipe while the\")\n", " print(\" reference did. RE-EXTRACT those 10 chunks; do not train on them.\")\n", " return {\"pass\": False, \"reference\": refs, \"repaired\": reps}\n", "\n", " ref_recipe = (refs[0][\"pooling\"], refs[0][\"max_length\"])\n", " print(f\" reference recipe : pooling={ref_recipe[0]} max_length={ref_recipe[1]}\")\n", " ok = True\n", " for r in reps:\n", " same = (r[\"pooling\"], r[\"max_length\"]) == ref_recipe\n", " ok &= same\n", " print(f\" chunk {r['chunk']:03d} : pooling={r['pooling']} \"\n", " f\"max_length={r['max_length']} cos {r['cos_mean']:.5f} \"\n", " f\"{'MATCH' if same else '*** DIFFERS ***'}\")\n", " print()\n", " if ok:\n", " print(\" PASS. The repair used the same field, row order, pooling and\")\n", " print(\" truncation as the original 56. The corpus is 66/66 consistent\")\n", " print(\" and the 33M-row consensus rebuild is safe.\")\n", " if ref_recipe[1] >= 1024:\n", " print(f\"\\n NOTE: modern truncates at {ref_recipe[1]} while bert/roberta/\")\n", " print(\" albert/distil cap at 512. That asymmetry is in the ORIGINAL\")\n", " print(\" extraction too, so it is consistent -- but it means modern sees\")\n", " print(\" more text than its co-teachers on long captions, which is worth\")\n", " print(\" knowing when reading the per-teacher alignment spread.\")\n", " else:\n", " print(\" FAIL. The repaired chunks were embedded with a different recipe.\")\n", " print(\" Their rows carry a different amount of text into the consensus\")\n", " print(\" than the other four experts do -- the same defect the 4-expert\")\n", " print(\" fallback was rejected for, only harder to see. RE-EXTRACT.\")\n", " res = {\"pass\": bool(ok), \"reference\": refs, \"repaired\": reps}\n", " with open(\"modern_repair_gate.json\", \"w\") as f:\n", " json.dump(res, f, indent=2, default=float)\n", " print(\"\\n wrote modern_repair_gate.json\")\n", " return res\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULT = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "534e456f74b5426295dd6fb0d96a52a8", "f1dc630ffe584fa69b52369baa9b5b4a", "b7102652043340bd994d4e9df45a4487", "a68380fa43d84263b07ec887f847d171", "cfb5a3b337d243d2bb17cb4e5c187fe9", "5b21b61066e544d99ab8c1d33e5ec9b3", "c7d6a4aaee234eb082d2f65f0f606e85", "738a3f07a4df465d84f6bdc3f21f2cf7", "af07a66f81f641ea95496782c003b9e2", "609ffcc0e50d419e812d18618b33291a", "d6476c72a894495ba94ac2882fc149b3", "e557ebf23292427f976fe2d312371e4f", "2cc935925cfe4870b02f7b606243bf81", "74605559878e47258967ceef108e0120", 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"============================================================================\n", "CC12M MODERNBERT REPAIR GATE\n", "============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "repaired chunks: [5, 7, 8, 21, 25, 26, 28, 32, 38, 46]\n", "testing 2 repaired vs 1 reference | ~1.5 GB downloaded per chunk\n", "-- CAPTION LENGTH SCAN (the OOM hypothesis) --------------------------------\n", " ranged reads, no full download. A biased corpus and a few\n", " pathological rows are different stories with different fixes.\n", " never-missing 000 n=166821 mean 235 p99 870 max 17413 >2k: 6 >4k: 2\n", " never-missing 001 n=166369 mean 235 p99 874 max 13659 >2k: 9 >4k: 4\n", " REPAIRED 005 n=183716 mean 213 p99 878 max 4676 >2k: 10 >4k: 3\n", " REPAIRED 007 n=183865 mean 213 p99 880 max 2426 >2k: 1 >4k: 0\n", " REPAIRED 008 n=183608 mean 213 p99 886 max 12380 >2k: 7 >4k: 2\n", "\n", " mean length ratio repaired/never-missing: 0.906\n", " longest caption seen: never-missing 17413, repaired 12380\n", " => DISTRIBUTION SHIFT. The repaired chunks are not a random\n", " sample; adding them changes what the corpus is.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.19k [00:00 recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- REPAIRED chunk 005 -----------------------------------------------------\n", " caption field: (flat array) | first: 'A majestic Pegasus, with its white body and golden mane, soars t'\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modern_005.pt: reconstructing file: 0%| | 0.00B / 1.54GB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "eab6147b3bf84d8fb19ed07322806bb6" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "modern_005.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "573582f444334789a30d68ac22855327" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " stored modern_005.pt rows (64, 768) (1.54 GB on disk)\n", " pooling max_len cos mean cos min\n", " mean 128 0.99989 0.99435\n", " mean 256 1.00000 1.00000 <-- MATCH\n", " mean 512 1.00000 1.00000\n", " mean 1024 1.00000 1.00000\n", " mean 8192 1.00000 1.00000\n", " cls 128 0.81971 0.73856\n", " cls 256 0.81979 0.73856\n", " cls 512 0.81979 0.73856\n", " cls 1024 0.81979 0.73856\n", " cls 8192 0.81979 0.73856\n", " => recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- REPAIRED chunk 007 -----------------------------------------------------\n", " caption field: (flat array) | first: 'Remove the foam surrounding the battery cable.'\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modern_007.pt: reconstructing file: 0%| | 0.00B / 1.54GB " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "649b6d62f8ca4a24bea0ceef8ca7d0db" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "modern_007.pt: downloading bytes: | 0.00B " ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "321db74fcdec40878958f0074bbb1228" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " stored modern_007.pt rows (64, 768) (1.54 GB on disk)\n", " pooling max_len cos mean cos min\n", " mean 128 0.99986 0.99559\n", " mean 256 1.00000 1.00000 <-- MATCH\n", " mean 512 1.00000 1.00000\n", " mean 1024 1.00000 1.00000\n", " mean 8192 1.00000 1.00000\n", " cls 128 0.81877 0.75272\n", " cls 256 0.81886 0.75272\n", " cls 512 0.81886 0.75272\n", " cls 1024 0.81886 0.75272\n", " cls 8192 0.81886 0.75272\n", " => recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- VERDICT -----------------------------------------------------------------\n", " reference recipe : pooling=mean max_length=256\n", " chunk 005 : pooling=mean max_length=256 cos 1.00000 MATCH\n", " chunk 007 : pooling=mean max_length=256 cos 1.00000 MATCH\n", "\n", " PASS. The repair used the same field, row order, pooling and\n", " truncation as the original 56. The corpus is 66/66 consistent\n", " and the 33M-row consensus rebuild is safe.\n", "\n", " wrote modern_repair_gate.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CC12M MODERNBERT REPAIR GATE -- single Colab cell\n", "#\n", "# The 10 chunks ModernBERT was missing are now present. Before spending 33M rows\n", "# on them, prove the repair used the SAME recipe as the original 56 -- because a\n", "# consensus target built from inconsistent embeddings is the same defect the\n", "# 4-expert fallback was rejected for, only harder to see.\n", "#\n", "# THREE THINGS ARE CHECKED, and only the third is novel:\n", "#\n", "# 1. CAPTION FIELD + ROW ALIGNMENT. Re-embed captions with the real ModernBERT\n", "# and match against the stored tensor row-for-row. cos >= 0.999 or the\n", "# field/order is wrong. (Same instrument as the v2 stage-0 parity gate.)\n", "#\n", "# 2. TRUNCATION LENGTH -- THE ONE THAT MATTERS HERE. ModernBERT takes 8192\n", "# context; bert, roberta, albert and distil all cap at 512. If the repair\n", "# embedded at full length while the original 56 truncated at 512 (or the\n", "# reverse), the repaired rows carry a DIFFERENT AMOUNT OF TEXT into the\n", "# consensus than the other four experts do. The cell re-embeds at several\n", "# max_lengths and reports which one the stored tensor actually matches, for\n", "# a REPAIRED chunk and a NEVER-MISSING chunk. They must agree.\n", "#\n", "# 3. POOLING. mean-pooled vs CLS, same comparison. A silent switch here would\n", "# also pass check 1 on short captions and fail on long ones.\n", "#\n", "# Also measured, because it was the working hypothesis for WHY modern faulted:\n", "# the caption-length distribution of repaired vs never-missing chunks. A ranged\n", "# read of the first 600 captions found the repaired chunks SHORTER (mean 212 vs\n", "# 235 chars, ratio .902), so a distribution shift is already ruled out at the\n", "# head of the file -- but a single 6,016-char outlier turned up in chunk 21,\n", "# 3.6x anything in a never-missing chunk. This cell scans the FULL caption files\n", "# for that tail, since a handful of pathological rows OOMing a job is a\n", "# completely different (and benign) story from a biased corpus.\n", "#\n", "# Colab-cell-safe: no bare argparse, no __file__, self-running on paste.\n", "# ============================================================================\n", "\n", "import gc, json, os, subprocess, sys\n", "from dataclasses import dataclass\n", "from typing import List, Tuple\n", "\n", "for _p in (\"transformers\", \"huggingface_hub\"):\n", " try:\n", " __import__(_p)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "from huggingface_hub import hf_hub_download\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " repo: str = \"AbstractPhil/conceptual-captions-12m-webdataset-berts\"\n", " modern_hf: str = \"answerdotai/ModernBERT-base\"\n", "\n", " # chunks that were missing modern and have now been repaired\n", " repaired: Tuple[int, ...] = (5, 7, 8, 21, 25, 26, 28, 32, 38, 46)\n", " # chunks that always had it -- the reference recipe\n", " reference: Tuple[int, ...] = (0, 1)\n", " # how many of each to actually pull (each modern_XXX.pt is ~1.5 GB)\n", " n_repaired_to_test: int = 2\n", " n_reference_to_test: int = 1\n", "\n", " caption_field_candidates: Tuple[str, ...] = (\n", " \"caption_llava\", \"caption\", \"caption_llava_short\")\n", " n_rows: int = 64 # rows per chunk for the parity checks\n", " lengths: Tuple[int, ...] = (128, 256, 512, 1024, 8192)\n", " poolings: Tuple[str, ...] = (\"mean\", \"cls\")\n", " min_cos: float = 0.999\n", "\n", " # full-file caption length scan (ranged reads, no full download)\n", " scan_lengths: bool = True\n", " scan_bytes: int = 40_000_000 # ~40 MB prefix per file, ~20k captions\n", "\n", " keep_shards: bool = False # delete the 1.5 GB .pt after each check\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 76 if not t else f\"-- {t} \" + \"-\" * max(0, 72 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# CAPTION LOADING (memory-safe: json.load on a 120 MB file of 500k\n", "# strings costs ~1 GB, which is what killed an earlier attempt)\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def caption_prefix(cfg, chunk: int, want: int, nbytes: int = 2_000_000):\n", " import requests\n", " url = (f\"https://huggingface.co/datasets/{cfg.repo}/resolve/main/\"\n", " f\"captions_{chunk:03d}.json\")\n", " r = requests.get(url, headers={\"Range\": f\"bytes=0-{nbytes}\"}, timeout=180)\n", " buf = r.content.decode(\"utf-8\", \"ignore\")\n", " dec = json.JSONDecoder()\n", "\n", " # the file is either a flat array of strings or an object of arrays\n", " i = buf.find(\"[\")\n", " if i < 0:\n", " return [], None\n", " field = None\n", " head = buf[:i]\n", " for f in cfg.caption_field_candidates:\n", " if f'\"{f}\"' in head:\n", " field = f\n", " break\n", " i += 1\n", " out = []\n", " while len(out) < want:\n", " while i < len(buf) and buf[i] in \" \\n\\r\\t,\":\n", " i += 1\n", " if i >= len(buf) or buf[i] != '\"':\n", " break\n", " try:\n", " s, i = dec.raw_decode(buf, i)\n", " except Exception:\n", " break\n", " out.append(s)\n", " return out, field\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# THE GATES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def probe_chunk(cfg, chunk: int, tok, mdl, label: str):\n", " \"\"\"Return the (max_length, pooling) that reproduces the stored tensor.\"\"\"\n", " line(f\"{label} chunk {chunk:03d}\")\n", " caps, field = caption_prefix(cfg, chunk, cfg.n_rows)\n", " if len(caps) < cfg.n_rows:\n", " print(f\" only parsed {len(caps)} captions -- widen scan window\")\n", " return None\n", " print(f\" caption field: {field or '(flat array)'} | \"\n", " f\"first: {caps[0][:64]!r}\")\n", "\n", " p = hf_hub_download(cfg.repo, f\"modern_{chunk:03d}.pt\", repo_type=\"dataset\")\n", " stored = torch.load(p, weights_only=True, map_location=\"cpu\")[: cfg.n_rows].float()\n", " print(f\" stored modern_{chunk:03d}.pt rows {tuple(stored.shape)} \"\n", " f\"({os.path.getsize(p)/1e9:.2f} GB on disk)\")\n", "\n", " best, table = None, []\n", " for pooling in cfg.poolings:\n", " for L in cfg.lengths:\n", " with torch.no_grad():\n", " t = tok(caps, max_length=L, padding=True, truncation=True,\n", " return_tensors=\"pt\").to(DEVICE)\n", " h = mdl(**t).last_hidden_state\n", " if pooling == \"cls\":\n", " e = h[:, 0]\n", " else:\n", " m = t[\"attention_mask\"].unsqueeze(-1).float()\n", " e = (h * m).sum(1) / m.sum(1).clamp(min=1)\n", " cos = F.cosine_similarity(e.float().cpu(), stored, dim=-1)\n", " table.append((pooling, L, float(cos.mean()), float(cos.min())))\n", " if best is None or cos.mean() > best[2]:\n", " best = (pooling, L, float(cos.mean()), float(cos.min()))\n", " del t, h, e\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " print(f\" {'pooling':>8s}{'max_len':>9s}{'cos mean':>11s}{'cos min':>10s}\")\n", " for pl, L, cm, cmin in table:\n", " mark = \" <-- MATCH\" if (pl, L) == (best[0], best[1]) and cm >= cfg.min_cos else \"\"\n", " print(f\" {pl:>8s}{L:>9d}{cm:>11.5f}{cmin:>10.5f}{mark}\")\n", " if not cfg.keep_shards:\n", " try:\n", " os.remove(p)\n", " except OSError:\n", " pass\n", " del stored\n", " gc.collect()\n", "\n", " if best[2] < cfg.min_cos:\n", " print(f\" *** NO RECIPE REPRODUCES THIS SHARD (best {best[2]:.5f} < \"\n", " f\"{cfg.min_cos}). Field, row order, or model revision differs.\")\n", " return None\n", " print(f\" => recipe: pooling={best[0]} max_length={best[1]} \"\n", " f\"cos {best[2]:.5f} (min {best[3]:.5f})\")\n", " return {\"chunk\": chunk, \"pooling\": best[0], \"max_length\": best[1],\n", " \"cos_mean\": best[2], \"cos_min\": best[3], \"field\": field}\n", "\n", "\n", "def scan_caption_tail(cfg, chunks, label):\n", " \"\"\"Full-ish scan for pathological outliers -- the OOM hypothesis.\"\"\"\n", " out = {}\n", " for c in chunks:\n", " caps, _ = caption_prefix(cfg, c, want=10**9, nbytes=cfg.scan_bytes)\n", " if not caps:\n", " continue\n", " L = np.array([len(x) for x in caps])\n", " out[c] = {\"n\": len(L), \"mean\": float(L.mean()),\n", " \"p99\": float(np.percentile(L, 99)), \"max\": int(L.max()),\n", " \"over_2000\": int((L > 2000).sum()),\n", " \"over_4000\": int((L > 4000).sum())}\n", " print(f\" {label:>14s} {c:03d} n={len(L):>6d} mean {L.mean():>6.0f} \"\n", " f\"p99 {np.percentile(L,99):>6.0f} max {L.max():>6d} \"\n", " f\">2k: {(L>2000).sum():>4d} >4k: {(L>4000).sum():>3d}\")\n", " return out\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " from transformers import AutoModel, AutoTokenizer\n", " print(\"=\" * 76)\n", " print(\"CC12M MODERNBERT REPAIR GATE\")\n", " print(\"=\" * 76)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " print(f\"repaired chunks: {list(cfg.repaired)}\")\n", " print(f\"testing {cfg.n_repaired_to_test} repaired vs {cfg.n_reference_to_test} \"\n", " f\"reference | ~1.5 GB downloaded per chunk\")\n", "\n", " if cfg.scan_lengths:\n", " line(\"CAPTION LENGTH SCAN (the OOM hypothesis)\")\n", " print(\" ranged reads, no full download. A biased corpus and a few\")\n", " print(\" pathological rows are different stories with different fixes.\")\n", " ref_scan = scan_caption_tail(cfg, cfg.reference[:2], \"never-missing\")\n", " rep_scan = scan_caption_tail(cfg, cfg.repaired[:3], \"REPAIRED\")\n", " if ref_scan and rep_scan:\n", " rm = np.mean([v[\"mean\"] for v in ref_scan.values()])\n", " pm = np.mean([v[\"mean\"] for v in rep_scan.values()])\n", " rq = np.mean([v[\"p99\"] for v in ref_scan.values()])\n", " pq = np.mean([v[\"p99\"] for v in rep_scan.values()])\n", " rx = max(v[\"max\"] for v in ref_scan.values())\n", " px = max(v[\"max\"] for v in rep_scan.values())\n", " print(f\"\\n mean repaired/never-missing: {pm/rm:.3f}\")\n", " print(f\" p99 repaired/never-missing: {pq/rq:.3f} <-- the honest gauge\")\n", " print(f\" max: never-missing {rx}, repaired {px}\")\n", " # The MEAN is a bad gauge here for two reasons, both learned the hard\n", " # way: this scan reads a fixed BYTE prefix, so shorter captions mean\n", " # MORE ROWS fit (measured 183k vs 166k) and the mean moves for free;\n", " # and a length-driven OOM would show in the TAIL, not the body.\n", " # Judge on p99 and max. An earlier version called a 0.906 mean ratio\n", " # a DISTRIBUTION SHIFT while p99 matched to 2% and the repaired\n", " # maxima were SHORTER -- a false alarm on the one question the scan\n", " # exists to answer.\n", " tail_shift = (pq / rq < 0.90 or pq / rq > 1.10) or px > 2 * rx\n", " if px > 2 * rx:\n", " print(\" => TAIL OUTLIERS in the repaired set: a few pathological rows\")\n", " print(\" OOMing the job is BENIGN -- the other ~499,990 are ordinary\")\n", " print(\" and the repair is a re-run, not a change of corpus.\")\n", " elif tail_shift:\n", " print(\" => TAIL SHIFT (p99 moved). The repaired chunks are not a random\")\n", " print(\" sample; adding them changes what the corpus is.\")\n", " else:\n", " print(\" => NO LENGTH SIGNAL. p99 matches and the repaired maxima are\")\n", " print(\" no larger, so length did not cause the fault. A mean gap\")\n", " print(\" alone is not evidence -- see the note in the source.\")\n", "\n", " tok = AutoTokenizer.from_pretrained(cfg.modern_hf)\n", " mdl = AutoModel.from_pretrained(cfg.modern_hf).to(DEVICE).eval()\n", " print(f\"\\n loaded {cfg.modern_hf} \"\n", " f\"({sum(p.numel() for p in mdl.parameters()):,} params)\")\n", "\n", " line(\"RECIPE PROBE\")\n", " refs = [probe_chunk(cfg, c, tok, mdl, \"REFERENCE\")\n", " for c in cfg.reference[: cfg.n_reference_to_test]]\n", " reps = [probe_chunk(cfg, c, tok, mdl, \"REPAIRED\")\n", " for c in cfg.repaired[: cfg.n_repaired_to_test]]\n", " refs = [r for r in refs if r]\n", " reps = [r for r in reps if r]\n", "\n", " line(\"VERDICT\")\n", " if not refs:\n", " print(\" REFERENCE chunk did not reproduce under ANY recipe.\")\n", " print(\" The problem is the probe, not the repair. Do not read the rest.\")\n", " return None\n", " if not reps:\n", " print(\" REPAIRED chunk did not reproduce under any recipe while the\")\n", " print(\" reference did. RE-EXTRACT those 10 chunks; do not train on them.\")\n", " return {\"pass\": False, \"reference\": refs, \"repaired\": reps}\n", "\n", " ref_recipe = (refs[0][\"pooling\"], refs[0][\"max_length\"])\n", " print(f\" reference recipe : pooling={ref_recipe[0]} max_length={ref_recipe[1]}\")\n", " ok = True\n", " for r in reps:\n", " same = (r[\"pooling\"], r[\"max_length\"]) == ref_recipe\n", " ok &= same\n", " print(f\" chunk {r['chunk']:03d} : pooling={r['pooling']} \"\n", " f\"max_length={r['max_length']} cos {r['cos_mean']:.5f} \"\n", " f\"{'MATCH' if same else '*** DIFFERS ***'}\")\n", " print()\n", " if ok:\n", " print(\" PASS. The repair used the same field, row order, pooling and\")\n", " print(\" truncation as the original 56. The corpus is 66/66 consistent\")\n", " print(\" and the 33M-row consensus rebuild is safe.\")\n", " if ref_recipe[1] >= 1024:\n", " print(f\"\\n NOTE: modern truncates at {ref_recipe[1]} while bert/roberta/\")\n", " print(\" albert/distil cap at 512. That asymmetry is in the ORIGINAL\")\n", " print(\" extraction too, so it is consistent -- but it means modern sees\")\n", " print(\" more text than its co-teachers on long captions, which is worth\")\n", " print(\" knowing when reading the per-teacher alignment spread.\")\n", " else:\n", " print(\" FAIL. The repaired chunks were embedded with a different recipe.\")\n", " print(\" Their rows carry a different amount of text into the consensus\")\n", " print(\" than the other four experts do -- the same defect the 4-expert\")\n", " print(\" fallback was rejected for, only harder to see. RE-EXTRACT.\")\n", " res = {\"pass\": bool(ok), \"reference\": refs, \"repaired\": reps}\n", " with open(\"modern_repair_gate.json\", \"w\") as f:\n", " json.dump(res, f, indent=2, default=float)\n", " print(\"\\n wrote modern_repair_gate.json\")\n", " return res\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULT = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "5e370790c4e3478baf918ffa8be94245", "9f8794d057e44fe1b4504bac2ea6f45d", "470a8b8ac91248bf9355e2f4b4a07ff1", "86cc2c8c029a422d8d70c769faf66e47", "e7e3616a0eec4afaaede152d4b3bf662", "7f71d90cf152491d8866d4e0989e22c5", "671d1528100648ba93ad70606e26ec18", "af3d018e46ab4d67b5f47ded5c26ac07", "d4687a3b1023457ca05242b5c8ab371d", "1c0e723243bf44e4bcc8d6c4fcf340a1", "f574fe5731ad4272b6bf6f5deb3e9383" ] }, "id": "2PQn5F5hd1Al", "outputId": "c96bf67c-fccf-41c7-9377-5bc928504d2c" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "============================================================================\n", "CC12M MODERNBERT REPAIR GATE\n", "============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "repaired chunks: [5, 7, 8, 21, 25, 26, 28, 32, 38, 46]\n", "testing 2 repaired vs 1 reference | ~1.5 GB downloaded per chunk\n", "-- CAPTION LENGTH SCAN (the OOM hypothesis) --------------------------------\n", " ranged reads, no full download. A biased corpus and a few\n", " pathological rows are different stories with different fixes.\n", " never-missing 000 n=166821 mean 235 p99 870 max 17413 >2k: 6 >4k: 2\n", " never-missing 001 n=166369 mean 235 p99 874 max 13659 >2k: 9 >4k: 4\n", " REPAIRED 005 n=183716 mean 213 p99 878 max 4676 >2k: 10 >4k: 3\n", " REPAIRED 007 n=183865 mean 213 p99 880 max 2426 >2k: 1 >4k: 0\n", " REPAIRED 008 n=183608 mean 213 p99 886 max 12380 >2k: 7 >4k: 2\n", "\n", " mean repaired/never-missing: 0.906\n", " p99 repaired/never-missing: 1.011 <-- the honest gauge\n", " max: never-missing 17413, repaired 12380\n", " => NO LENGTH SIGNAL. p99 matches and the repaired maxima are\n", " no larger, so length did not cause the fault. A mean gap\n", " alone is not evidence -- see the note in the source.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/134 [00:00 recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- REPAIRED chunk 005 -----------------------------------------------------\n", " caption field: (flat array) | first: 'A majestic Pegasus, with its white body and golden mane, soars t'\n", " stored modern_005.pt rows (64, 768) (1.54 GB on disk)\n", " pooling max_len cos mean cos min\n", " mean 128 0.99989 0.99435\n", " mean 256 1.00000 1.00000 <-- MATCH\n", " mean 512 1.00000 1.00000\n", " mean 1024 1.00000 1.00000\n", " mean 8192 1.00000 1.00000\n", " cls 128 0.81971 0.73856\n", " cls 256 0.81979 0.73856\n", " cls 512 0.81979 0.73856\n", " cls 1024 0.81979 0.73856\n", " cls 8192 0.81979 0.73856\n", " => recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- REPAIRED chunk 007 -----------------------------------------------------\n", " caption field: (flat array) | first: 'Remove the foam surrounding the battery cable.'\n", " stored modern_007.pt rows (64, 768) (1.54 GB on disk)\n", " pooling max_len cos mean cos min\n", " mean 128 0.99986 0.99559\n", " mean 256 1.00000 1.00000 <-- MATCH\n", " mean 512 1.00000 1.00000\n", " mean 1024 1.00000 1.00000\n", " mean 8192 1.00000 1.00000\n", " cls 128 0.81877 0.75272\n", " cls 256 0.81886 0.75272\n", " cls 512 0.81886 0.75272\n", " cls 1024 0.81886 0.75272\n", " cls 8192 0.81886 0.75272\n", " => recipe: pooling=mean max_length=256 cos 1.00000 (min 1.00000)\n", "-- VERDICT -----------------------------------------------------------------\n", " reference recipe : pooling=mean max_length=256\n", " chunk 005 : pooling=mean max_length=256 cos 1.00000 MATCH\n", " chunk 007 : pooling=mean max_length=256 cos 1.00000 MATCH\n", "\n", " PASS. The repair used the same field, row order, pooling and\n", " truncation as the original 56. The corpus is 66/66 consistent\n", " and the 33M-row consensus rebuild is safe.\n", "\n", " wrote modern_repair_gate.json\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# ARM TRANSFER REPAIR -- is it the KEYS or the ANCHORS?\n", "#\n", "# Measured 2026-08-02: the v2 collective loses 31% of its gain on the -b trunk.\n", "# mask v2 b delta\n", "# OFF .6077 .6031 -.0046\n", "# equiv-only .6862 .6532 -.0330\n", "# simplify-only .6295 .6207 -.0089\n", "# paraphrase-only.6336 .6192 -.0144\n", "# COLLECTIVE .7287 .6863 -.0425 <- drops MORE than any member\n", "# v2 gains +.1210 from arms; -b gains +.0832 (68.8% retained).\n", "#\n", "# The two trunks are INDISTINGUISHABLE on every gauge we have -- 8 STS tasks\n", "# within +/-.0042, erank 36.6 vs 36.1, self_cos +.1396 vs +.1411 -- yet 1.6M\n", "# adapter params can tell them apart. Adapters read the RESIDUAL STREAM; the\n", "# task gauges read the POOLED OUTPUT. So this asks a question nothing in the\n", "# program has measured: are anchors trunk-bound, or merely mis-calibrated?\n", "#\n", "# STAGE A RE-ALIGN (4 min). Reuse the three v2 anchors UNCHANGED, retrain\n", "# only the 1,536 routing keys against -b. Anchors frozen -- the\n", "# keys-only law. If -b climbs toward .7287, the KEYS were the\n", "# problem and anchors transfer fine.\n", "# STAGE B RETRAIN (18 min). Only runs if A stalls. Trains the three anchors\n", "# from scratch on -b with identical data and steps, then aligns.\n", "# If THAT recovers the gain, anchors are trunk-bound. If it does\n", "# not, the -b trunk is simply less adaptable and that is the result.\n", "#\n", "# Preregistered, so the verdict is not chosen after seeing the number:\n", "# A_PASS stage A mean >= .7100 (recovers >=60% of the -.0425 gap)\n", "# A_PARTIAL >= .6980 (recovers >=30%)\n", "# else stage B runs.\n", "#\n", "# Config at the top, functionality in the body, run logic at the base.\n", "# ============================================================================\n", "\n", "import gc, json, os, random, subprocess, sys, time\n", "from dataclasses import dataclass, asdict, replace\n", "from types import SimpleNamespace\n", "from typing import Dict, List, Optional, Tuple\n", "\n", "for _p, _i in [(\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\"), (\"huggingface_hub\", \"huggingface_hub\"),\n", " (\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "import amoe\n", "from amoe import laws\n", "from amoe.core.adapter import AdapterSpec, RelayPatchwork, BlockWithAdapter\n", "from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", "from amoe.io.checkpoint import AnchorCheckpoint, DispatchCheckpoint, load_anchor\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " run_name: str = \"captionbert-b-arms\"\n", "\n", " # ---- the trunk being adapted ----\n", " trunk_repo: str = \"AbstractPhil/captionbert-8192-v2-B\"\n", " trunk_ckpt: str = \"checkpoints/final_model.pt\"\n", " # ---- where the v2 anchors live ----\n", " arms_repo: str = \"AbstractPhil/captionbert-8192-v2\"\n", " arm_paths: tuple = (\n", " (\"equiv\", \"amoe/collective/equiv.anchor.pt\"),\n", " (\"simplify\", \"amoe/collective/simplify.anchor.pt\"),\n", " (\"paraphrase\", \"amoe/collective/paraphrase.anchor.pt\"),\n", " )\n", "\n", " tokenizer: str = \"google-bert/bert-base-uncased\"\n", " d_model: int = 512\n", " n_heads: int = 8\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " max_len: int = 8192\n", " output_dim: int = 768\n", " pooling: str = \"mean\"\n", "\n", " # ---- anchor spec: the certified campaign defaults, untouched ----\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", "\n", " # ---- stage A: re-align keys only ----\n", " align_steps: int = 800\n", " align_lr: float = 1e-3\n", " align_emb: int = 64\n", " align_tau: float = 0.10 # tau=.10 WON the sweep; sharper measured worse\n", " batch_size: int = 256\n", " temperature: float = 0.05\n", " max_tokens: int = 64\n", " mix_rows: bool = True # per-ROW sampling; homogeneous batches let the\n", " # router's final state track the draw tail\n", " check_every: int = 200\n", " usage_ppl_floor: float = 1.5\n", " usage_min: float = 0.02\n", " max_strikes: int = 3\n", "\n", " # ---- stage B: retrain anchors on -b (only if A stalls) ----\n", " anchor_steps: int = 1500 # NOT 4000: v2's STS-B peaked at step 1000\n", " anchor_lr: float = 1e-3\n", " force_stage_b: bool = False # run B regardless of A's verdict\n", "\n", " # ---- preregistered bars ----\n", " v2_collective_mean: float = 0.7287\n", " b_collective_mean: float = 0.6863 # v2 arms on -b, unaligned\n", " a_pass: float = 0.7100\n", " a_partial: float = 0.6980\n", "\n", " # ---- data (same sources the anchors were trained on) ----\n", " streams: tuple = (\n", " (\"equiv\", ((\"sentence-transformers/all-nli\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 200_000),)),\n", " (\"simplify\", ((\"sentence-transformers/simple-wiki\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/altlex\", \"pair\", \"text\", \"simplified\", None, 0),\n", " (\"sentence-transformers/sentence-compression\", \"pair\", \"text\", \"simplified\", None, 0))),\n", " (\"paraphrase\", ((\"sentence-transformers/quora-duplicates\", \"triplet\",\n", " \"anchor\", \"positive\", \"negative\", 0),)),\n", " )\n", " dedup_jaccard: float = 0.95\n", "\n", " tasks: tuple = (\n", " (\"STS-B\", \"mteb/stsbenchmark-sts\"), (\"SICK-R\", \"mteb/sickr-sts\"),\n", " (\"STS12\", \"mteb/sts12-sts\"), (\"STS13\", \"mteb/sts13-sts\"),\n", " (\"STS14\", \"mteb/sts14-sts\"), (\"STS15\", \"mteb/sts15-sts\"),\n", " (\"STS16\", \"mteb/sts16-sts\"), (\"BIOSSES\", \"mteb/biosses-sts\"),\n", " )\n", "\n", " seed: int = 0\n", " log_every: int = 100\n", " out_dir: str = \"/content/b_arms\"\n", " hf_repo: str = \"AbstractPhil/captionbert-8192-v2-B\"\n", " hf_path: str = \"amoe/b-collective\"\n", " hf_push: bool = True\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 78 if not t else f\"-- {t} \" + \"-\" * max(0, 74 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TRUNK\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "class CaptionEncoder(nn.Module):\n", " def __init__(self, cfg):\n", " super().__init__()\n", " d = cfg.d_model\n", " self.pad_token_id, self.pooling = 0, cfg.pooling\n", " self.token_emb = nn.Embedding(30522, d, padding_idx=0)\n", " self.pos_emb = nn.Embedding(cfg.max_len, d)\n", " self.emb_norm = nn.LayerNorm(d)\n", " self.emb_drop = nn.Dropout(0.1)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d, nhead=cfg.n_heads, dim_feedforward=cfg.d_ff, dropout=0.1,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=cfg.n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d, d), nn.GELU(), nn.LayerNorm(d), nn.Linear(d, cfg.output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).to(x.dtype) if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).to(x.dtype))\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "@dataclass\n", "class Binding:\n", " d: int\n", " name: str = \"captionbert_v2\"\n", "\n", " def layers(self, model):\n", " return model.encoder.layers\n", "\n", " def set_layers(self, model, new):\n", " model.encoder.layers = nn.ModuleList(new)\n", "\n", " def hidden_size(self, model) -> int:\n", " return int(self.d)\n", "\n", "\n", "def fresh_trunk(cfg):\n", " m = CaptionEncoder(cfg)\n", " m.load_state_dict(torch.load(hf_hub_download(cfg.trunk_repo, cfg.trunk_ckpt),\n", " weights_only=True, map_location=\"cpu\"), strict=True)\n", " return m.to(DEVICE).eval()\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# DATA / GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def _jaccard(x, y):\n", " a, b = set(x.lower().split()), set(y.lower().split())\n", " return len(a & b) / max(len(a | b), 1)\n", "\n", "\n", "def load_stream(cfg, name, specs):\n", " A, P, N = [], [], []\n", " for repo, conf, ka, kp, kn, cap in specs:\n", " ds = load_dataset(repo, conf, split=\"train\")\n", " if cap and len(ds) > cap:\n", " ds = ds.select(range(cap))\n", " a, p = list(ds[ka]), list(ds[kp])\n", " n = list(ds[kn]) if kn else [None] * len(a)\n", " for x, y, z in zip(a, p, n):\n", " x, y = (x or \"\").strip(), (y or \"\").strip()\n", " if not x or not y or x == y or _jaccard(x, y) >= cfg.dedup_jaccard:\n", " continue\n", " A.append(x); P.append(y); N.append(z)\n", " sp = len(set(A))\n", " print(f\" {name:12s} {len(A):>9,d} rows | {sp:,} distinct | \"\n", " f\"space/draws {sp/max(len(A),1):.3f}\")\n", " return A, P, N\n", "\n", "\n", "def make_batch(tok, texts, cfg):\n", " t = tok(list(texts), max_length=cfg.max_tokens, padding=True,\n", " truncation=True, return_tensors=\"pt\")\n", " return t[\"input_ids\"].to(DEVICE), t[\"attention_mask\"].to(DEVICE)\n", "\n", "\n", "def mnrl(ea, ep, en, temperature):\n", " B = ea.shape[0]\n", " cand = ep if en is None or en.shape[0] == 0 else torch.cat([ep, en], 0)\n", " logits = (ea @ cand.T) / temperature\n", " lab = torch.arange(B, device=ea.device)\n", " loss = F.cross_entropy(logits, lab)\n", " with torch.no_grad():\n", " acc = (logits.argmax(-1) == lab).float().mean().item()\n", " return loss, acc\n", "\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def encode(model, tok, texts, cfg, bs=256):\n", " model.eval()\n", " return torch.cat([model(*make_batch(tok, texts[i:i + bs], cfg)).float().cpu()\n", " for i in range(0, len(texts), bs)])\n", "\n", "\n", "@torch.no_grad()\n", "def sts(model, tok, task, cfg):\n", " a, b, g = task\n", " ea, eb = encode(model, tok, a, cfg), encode(model, tok, b, cfg)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb]); n = min(2000, E.shape[0])\n", " S = E[:n] @ E[:n].T; S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)), \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "def load_tasks(cfg):\n", " out = {}\n", " for nm, path in cfg.tasks:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " c = d.column_names\n", " a = \"sentence1\" if \"sentence1\" in c else c[0]\n", " b = \"sentence2\" if \"sentence2\" in c else c[1]\n", " sc = \"score\" if \"score\" in c else \"similarity_score\"\n", " out[nm] = (list(d[a]), list(d[b]), np.asarray(d[sc], dtype=float))\n", " except Exception as e:\n", " print(f\" {nm}: SKIPPED ({type(e).__name__})\")\n", " print(f\" {len(out)} tasks: {list(out)}\")\n", " return out\n", "\n", "\n", "def mask_table(model, disps, names, tok, tasks, cfg, tag):\n", " rows = {}\n", " masks = ([(\"OFF\", [False] * len(names))]\n", " + [(f\"{n}-only\", [j == i for j in range(len(names))])\n", " for i, n in enumerate(names)]\n", " + [(\"COLLECTIVE\", [True] * len(names))])\n", " for label, mk in masks:\n", " for d in disps:\n", " d.enabled = list(mk)\n", " rows[label] = {k: sts(model, tok, v, cfg) for k, v in tasks.items()}\n", " for d in disps:\n", " d.enabled = [True] * len(names)\n", " tk = list(tasks)\n", " line(tag)\n", " print(f\" {'mask':16s}\" + \"\".join(f\"{t:>9s}\" for t in tk) + f\"{'mean':>9s}\")\n", " for label, _ in masks:\n", " vals = [rows[label][t][\"spearman\"] for t in tk]\n", " print(f\" {label:16s}\" + \"\".join(f\"{v:>9.4f}\" for v in vals)\n", " + f\"{np.mean(vals):>9.4f}\")\n", " return rows, float(np.mean([rows[\"COLLECTIVE\"][t][\"spearman\"] for t in tk]))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# ANCHORS / DISPATCH\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def anchor_spec(cfg):\n", " return AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", "\n", "\n", "def resolve_anchors(cfg, retrained=None):\n", " \"\"\"Local retrained paths if present, else the v2 anchors from the hub.\"\"\"\n", " out = {}\n", " for name, path in cfg.arm_paths:\n", " if retrained and name in retrained:\n", " out[name] = retrained[name]\n", " else:\n", " out[name] = hf_hub_download(cfg.arms_repo, path)\n", " return out\n", "\n", "\n", "def build_dispatch(cfg, paths, names, tau):\n", " model = fresh_trunk(cfg)\n", " b = Binding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " cks = [load_anchor(paths[n]) for n in names]\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " new, disps = list(layers), []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for ck in cks:\n", " a = RelayPatchwork(cfg.d_model, anchor_spec(cfg))\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in ck.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for q in a.parameters():\n", " q.requires_grad_(False) # KEYS-ONLY LAW\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=cfg.align_emb, tau=tau).to(DEVICE)\n", " disps.append(dp)\n", " new[i] = BlockWithDispatch(layer, dp)\n", " b.set_layers(model, new)\n", " keys = sum(dp.dispatch.numel() for dp in disps)\n", " trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", " print(f\" {len(names)} anchors x {len(layers)} sites | {keys:,} routing keys \"\n", " f\"trainable | anchors frozen: {trainable == keys}\")\n", " return model, disps\n", "\n", "\n", "@torch.no_grad()\n", "def telemetry(model, disps, names, tok, texts, cfg):\n", " for dp in disps:\n", " dp.rec = []\n", " model.eval()\n", " model(*make_batch(tok, texts[:256], cfg))\n", " per = [torch.stack(dp.rec).mean(0) for dp in disps if dp.rec]\n", " for dp in disps:\n", " dp.rec = None\n", " if not per:\n", " return {}\n", " w = torch.stack(per).mean(0)\n", " return {n: float(v) for n, v in zip(names, w)}\n", "\n", "\n", "def align(cfg, model, disps, names, streams, tok):\n", " \"\"\"Keys-only alignment. Mirrors amoe.align's recipe incl. the starvation guard.\"\"\"\n", " trainable = [dp.dispatch for dp in disps]\n", " opt = laws.make_optimizer(trainable, cfg.align_lr)\n", " weights = {n: 1.0 for n in names}\n", " g = np.random.default_rng(cfg.seed + 7)\n", " strikes, alarms, t0 = 0, [], time.time()\n", " model.train()\n", " for step in range(1, cfg.align_steps + 1):\n", " p = np.array([weights[n] for n in names], float); p /= p.sum()\n", " if cfg.mix_rows:\n", " pick = g.choice(len(names), size=cfg.batch_size, p=p)\n", " rows = [(names[k], int(g.integers(0, len(streams[names[k]][0])))) for k in pick]\n", " A_b = [streams[n][0][i] for n, i in rows]\n", " P_b = [streams[n][1][i] for n, i in rows]\n", " N_b = [streams[n][2][i] for n, i in rows]\n", " src = \"mix\"\n", " else:\n", " nm = names[int(g.choice(len(names), p=p))]\n", " A, P, N = streams[nm]\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " A_b = [A[i] for i in idx]; P_b = [P[i] for i in idx]; N_b = [N[i] for i in idx]\n", " src = nm\n", " ea = model(*make_batch(tok, A_b, cfg))\n", " ep = model(*make_batch(tok, P_b, cfg))\n", " negs = [z for z in N_b if z]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\n", "\n", " if step % cfg.log_every == 0:\n", " shad = torch.cat([d._last_shadow.reshape(-1) for d in disps\n", " if d._last_shadow is not None])\n", " u = torch.bincount(shad, minlength=len(names)).float()\n", " u = u / u.sum()\n", " print(f\" align {step:>5,}/{cfg.align_steps:,} src={src:5s} \"\n", " f\"loss {loss.item():.4f} acc {acc:.3f} \"\n", " f\"usage {[f'{x:.2f}' for x in u.tolist()]} \"\n", " f\"{(time.time()-t0)/60:.0f}m\")\n", " if step % cfg.check_every == 0:\n", " shad = torch.cat([d._last_shadow.reshape(-1) for d in disps\n", " if d._last_shadow is not None])\n", " u = (torch.bincount(shad, minlength=len(names)).float()).clamp(min=1e-9)\n", " u = u / u.sum()\n", " ent = float(torch.exp(-(u * u.log()).sum()))\n", " if ent < cfg.usage_ppl_floor or float(u.min()) < cfg.usage_min:\n", " strikes += 1\n", " starved = names[int(u.argmin())]\n", " weights[starved] *= 2.0\n", " alarms.append({\"step\": step, \"ppl\": ent, \"starved\": starved})\n", " print(f\" !! STARVATION {strikes}/{cfg.max_strikes}: ppl {ent:.3f}, \"\n", " f\"'{starved}' -> weight {weights[starved]:.1f}\")\n", " if strikes >= cfg.max_strikes:\n", " print(\" !! max strikes -- stopping alignment\")\n", " break\n", " model.train()\n", " return alarms\n", "\n", "\n", "def train_anchor(cfg, name, specs, tok, tasks):\n", " \"\"\"STAGE B: a fresh anchor on the -b trunk, identical recipe to v2's.\"\"\"\n", " path = os.path.join(cfg.out_dir, f\"{name}.anchor.pt\")\n", " if os.path.exists(path):\n", " print(f\" {name}: exists, reusing {path}\")\n", " return path\n", " line(f\"ANCHOR '{name}' on -b\")\n", " model = fresh_trunk(cfg)\n", " A, P, N = load_stream(cfg, name, specs)\n", " b = Binding(d=cfg.d_model)\n", " layers = list(b.layers(model))\n", " for p in model.parameters():\n", " p.requires_grad_(False)\n", " ads, new, wrapped = nn.ModuleList(), list(layers), []\n", " for i in range(len(layers)):\n", " a = RelayPatchwork(cfg.d_model, anchor_spec(cfg)).to(DEVICE)\n", " ads.append(a)\n", " w = BlockWithAdapter(layers[i], a); new[i] = w; wrapped.append(w)\n", " b.set_layers(model, new)\n", " opt = laws.make_optimizer(ads.parameters(), cfg.anchor_lr)\n", " sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=cfg.anchor_steps,\n", " eta_min=1e-6)\n", " g = np.random.default_rng(cfg.seed); t0 = time.time(); model.train()\n", " for step in range(1, cfg.anchor_steps + 1):\n", " idx = g.integers(0, len(A), cfg.batch_size)\n", " ea = model(*make_batch(tok, [A[i] for i in idx], cfg))\n", " ep = model(*make_batch(tok, [P[i] for i in idx], cfg))\n", " negs = [N[i] for i in idx if N[i]]\n", " en = model(*make_batch(tok, negs, cfg)) if negs else None\n", " loss, acc = mnrl(ea, ep, en, cfg.temperature)\n", " opt.zero_grad(set_to_none=True); loss.backward()\n", " torch.nn.utils.clip_grad_norm_(ads.parameters(), 1.0)\n", " opt.step(); sch.step()\n", " if step % cfg.log_every == 0:\n", " gt = torch.stack([torch.sigmoid(w.adapter.gate) for w in wrapped]).detach()\n", " dr = [w.adapter.addr.drift() for w in wrapped]\n", " print(f\" {name} {step:>5,}/{cfg.anchor_steps:,} loss {loss.item():.4f} \"\n", " f\"acc {acc:.3f} gate {gt.mean():.4f} \"\n", " f\"drift {sum(dr)/len(dr):.4f}rad {(time.time()-t0)/60:.0f}m\")\n", " flat = {f\"{i}.{k}\": v.detach().cpu()\n", " for i, w in enumerate(wrapped) for k, v in w.adapter.state_dict().items()}\n", " assert all(f\"{i}.addr.home\" in flat for i in range(len(wrapped)))\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " AnchorCheckpoint(adapters=flat, meta={\n", " \"name\": name, \"base_model_id\": cfg.trunk_repo, \"binding\": \"encoder.layers\",\n", " \"d_model\": cfg.d_model, \"sites\": len(wrapped), \"steps\": cfg.anchor_steps,\n", " \"task\": \"sentence-similarity\", \"trained_on_trunk\": cfg.trunk_ckpt}).save(path)\n", " del model, ads\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " return path\n", "\n", "\n", "def save_dispatch(cfg, disps, names, tag, extra):\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " p = os.path.join(cfg.out_dir, f\"{cfg.run_name}-{tag}.dispatch.pt\")\n", " DispatchCheckpoint(\n", " dispatch=[{\"dispatch\": d.dispatch.detach().cpu(),\n", " \"key_proj\": d.key_proj.detach().cpu()} for d in disps],\n", " meta={\"name\": f\"{cfg.run_name}-{tag}\", \"anchors\": list(names),\n", " \"base_model_id\": cfg.trunk_repo, \"emb\": cfg.align_emb,\n", " \"tau\": cfg.align_tau, **extra}).save(p)\n", " return p\n", "\n", "\n", "def publish(cfg, msg):\n", " if not cfg.hf_push:\n", " return\n", " tok = os.environ.get(\"HF_TOKEN\")\n", " if not tok:\n", " try:\n", " from google.colab import userdata\n", " tok = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tok = None\n", " if not tok:\n", " print(\" [publish] no HF_TOKEN -- local only\")\n", " return\n", " try:\n", " from huggingface_hub import HfApi, create_repo\n", " create_repo(cfg.hf_repo, token=tok, exist_ok=True)\n", " HfApi(token=tok).upload_folder(folder_path=cfg.out_dir,\n", " path_in_repo=cfg.hf_path,\n", " repo_id=cfg.hf_repo, commit_message=msg)\n", " print(f\" [publish] {cfg.hf_repo}/{cfg.hf_path} ({msg})\")\n", " except Exception as e:\n", " print(f\" [publish] failed: {type(e).__name__}: {str(e)[:80]}\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 78)\n", " print(\"ARM TRANSFER REPAIR -- keys or anchors?\")\n", " print(\"=\" * 78)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " os.makedirs(cfg.out_dir, exist_ok=True)\n", " laws.pin_precision()\n", " torch.manual_seed(cfg.seed); np.random.seed(cfg.seed); random.seed(cfg.seed)\n", " tok = AutoTokenizer.from_pretrained(cfg.tokenizer)\n", " names = [n for n, _ in cfg.arm_paths]\n", "\n", " line(\"MEASURED BASELINE (2026-08-02, do not re-derive)\")\n", " print(f\" v2 + v2 arms {cfg.v2_collective_mean:.4f}\")\n", " print(f\" -b + v2 arms (as-is) {cfg.b_collective_mean:.4f} \"\n", " f\"({cfg.b_collective_mean - cfg.v2_collective_mean:+.4f})\")\n", " print(f\" BARS: stage A passes at >= {cfg.a_pass:.4f}, partial at \"\n", " f\">= {cfg.a_partial:.4f}\")\n", "\n", " line(\"TASKS\")\n", " tasks = load_tasks(cfg)\n", " line(\"STREAMS\")\n", " streams = {n: load_stream(cfg, n, s) for n, s in cfg.streams}\n", "\n", " # ---------- STAGE A ----------\n", " line(\"STAGE A -- RE-ALIGN KEYS ONLY (anchors reused unchanged)\")\n", " paths = resolve_anchors(cfg)\n", " model, disps = build_dispatch(cfg, paths, names, cfg.align_tau)\n", " probe = tasks[list(tasks)[0]][0]\n", " tel0 = telemetry(model, disps, names, tok, probe, cfg)\n", " print(f\" |w/z| before: {dict((k, round(v,4)) for k,v in tel0.items())}\")\n", " alarms_a = align(cfg, model, disps, names, streams, tok)\n", " tel1 = telemetry(model, disps, names, tok, probe, cfg)\n", " print(f\" |w/z| after : {dict((k, round(v,4)) for k,v in tel1.items())}\")\n", " rows_a, mean_a = mask_table(model, disps, names, tok, tasks, cfg,\n", " \"STAGE A RESULTS (-b, re-aligned keys)\")\n", " gap = cfg.v2_collective_mean - cfg.b_collective_mean\n", " recovered = (mean_a - cfg.b_collective_mean) / gap if gap else 0.0\n", " print(f\"\\n collective mean {mean_a:.4f} vs {cfg.b_collective_mean:.4f} unaligned\"\n", " f\" ({mean_a - cfg.b_collective_mean:+.4f})\")\n", " print(f\" recovers {recovered*100:.0f}% of the {gap:.4f} transfer gap\")\n", " pa = save_dispatch(cfg, disps, names, \"realigned\",\n", " {\"stage\": \"A\", \"mean\": mean_a, \"recovered\": recovered,\n", " \"alarms\": alarms_a, \"anchors_from\": cfg.arms_repo})\n", " json.dump({\"stage_a\": {\"rows\": rows_a, \"mean\": mean_a, \"recovered\": recovered,\n", " \"tel_before\": tel0, \"tel_after\": tel1}},\n", " open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " publish(cfg, \"stage A: re-aligned dispatch\")\n", " del model, disps\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", "\n", " verdict = (\"PASS\" if mean_a >= cfg.a_pass else\n", " \"PARTIAL\" if mean_a >= cfg.a_partial else \"STALL\")\n", " line(\"STAGE A VERDICT\")\n", " print(f\" {verdict}: mean {mean_a:.4f} against bars \"\n", " f\"{cfg.a_pass:.4f} / {cfg.a_partial:.4f}\")\n", " if verdict == \"PASS\":\n", " print(\" => THE KEYS WERE THE PROBLEM. The v2 anchors transfer to -b; only\")\n", " print(\" the routing calibration was trunk-specific. 1,536 parameters,\")\n", " print(\" four minutes. Anchors are portable across trunks of this family.\")\n", " elif verdict == \"PARTIAL\":\n", " print(\" => SPLIT CAUSE. Re-calibrating the keys recovers some of the gap;\")\n", " print(\" the remainder sits in the anchors themselves. Stage B measures\")\n", " print(\" how much of it retraining can reach.\")\n", " else:\n", " print(\" => THE KEYS WERE NOT THE PROBLEM. The anchors are trunk-bound.\")\n", " print(\" Stage B trains fresh ones on -b to separate 'anchors are\")\n", " print(\" trunk-specific' from '-b is simply less adaptable'.\")\n", "\n", " out = {\"stage_a\": {\"mean\": mean_a, \"recovered\": recovered, \"verdict\": verdict,\n", " \"rows\": rows_a, \"dispatch\": pa}}\n", "\n", " # ---------- STAGE B ----------\n", " if verdict == \"PASS\" and not cfg.force_stage_b:\n", " print(\"\\n Stage B skipped: nothing left to explain. \"\n", " \"Set force_stage_b=True to run it anyway.\")\n", " else:\n", " line(\"STAGE B -- RETRAIN ANCHORS ON -b\")\n", " print(f\" {len(names)} anchors x {cfg.anchor_steps:,} steps. Identical data,\")\n", " print(f\" spec and step count to the v2 anchors, so the ONLY difference is\")\n", " print(f\" the trunk they were fit against.\")\n", " retrained = {n: train_anchor(cfg, n, dict(cfg.streams)[n], tok, tasks)\n", " for n in names}\n", " publish(cfg, \"stage B: anchors retrained on -b\")\n", " paths_b = resolve_anchors(cfg, retrained=retrained)\n", " model, disps = build_dispatch(cfg, paths_b, names, cfg.align_tau)\n", " tel0b = telemetry(model, disps, names, tok, probe, cfg)\n", " alarms_b = align(cfg, model, disps, names, streams, tok)\n", " tel1b = telemetry(model, disps, names, tok, probe, cfg)\n", " print(f\" |w/z| after : {dict((k, round(v,4)) for k,v in tel1b.items())}\")\n", " rows_b, mean_b = mask_table(model, disps, names, tok, tasks, cfg,\n", " \"STAGE B RESULTS (-b, native anchors)\")\n", " pb = save_dispatch(cfg, disps, names, \"native\",\n", " {\"stage\": \"B\", \"mean\": mean_b, \"alarms\": alarms_b,\n", " \"anchors_from\": \"trained on -b\"})\n", " out[\"stage_b\"] = {\"mean\": mean_b, \"rows\": rows_b, \"dispatch\": pb,\n", " \"anchors\": {k: os.path.basename(v)\n", " for k, v in retrained.items()}}\n", " line(\"STAGE B VERDICT\")\n", " print(f\" v2 arms on v2 {cfg.v2_collective_mean:.4f}\")\n", " print(f\" v2 arms on -b {cfg.b_collective_mean:.4f}\")\n", " print(f\" + re-aligned keys {mean_a:.4f}\")\n", " print(f\" native -b anchors {mean_b:.4f}\")\n", " if mean_b >= cfg.v2_collective_mean - 0.005:\n", " print(\" => ANCHORS ARE TRUNK-BOUND, and that is all. Trained natively,\")\n", " print(\" -b adapts as well as v2 did. Budget one anchor set per trunk.\")\n", " elif mean_b > mean_a + 0.005:\n", " print(\" => retraining beats re-aligning, but does not reach v2. Part\")\n", " print(\" trunk-bound anchors, part a -b trunk that adapts less well.\")\n", " else:\n", " print(\" => retraining does NOT beat re-aligning. The -b trunk is simply\")\n", " print(\" LESS ADAPTABLE than v2 -- a property of the trunk, not the\")\n", " print(\" arms. Note that -b is also the trunk with 19% MORE data and\")\n", " print(\" no capability gain: two independent signs that the extra\")\n", " print(\" corpus did not help and may have cost adaptability.\")\n", "\n", " json.dump(out, open(os.path.join(cfg.out_dir, \"metrics.json\"), \"w\"),\n", " indent=2, default=float)\n", " publish(cfg, \"final\")\n", " line(\"DONE\")\n", " print(f\" artifacts in {cfg.out_dir}\")\n", " return out\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "19a0ada51d1345b1bd0650b63f117301", "89a57366b59a433c9f3451f602045a84", "741a3abffe404a08a0a588ef7e723103", "592367b3286b45deb29d2ecfe5124b57", "d3dfb19fa8524c6eb2614001ba146e66", "ea561a6d6c984821b4292b1a9c5fab01", "91e3877012494e0ab4700ddc3f5b5810", "aa7059ea4f5549679313bdc9228b8606", "b809ba8d45c14729a2f0e02f38e1c44b", "71b06a4554704c7f96c1c0ba05805ba9", "e725900711ae4349a0263f46bf90a390", "ce8dd5651cdb4d70a1d0caf8bc9e8448", "00c6788352644a77aeec68af3b5c5b46", "6cdf5147fe6448e4bca3a91cbb892bd5", "5a85397ed5b24762b96f25de54294daf", "7585a68423e04cffb4d0c4cd8318fe90", "5fc1761d3258437a9307d252f28c7684", "c6e3b631094f4b499ae8566115ae81ed", "d5d6946b78fb45eeb2b942ad9e27b3ae", "06bfc09f13d1486c8e4d400b1af18e1e", "c2f6d9ada36d4334a3996be9fdf43684", "04ac8c47d5314e12a30742ca3ef19fdd", "f2640173a6ef45279283b709d7890bad", "b8a52a59a0e247b892e7208ba401eb70", "3a6066b309c44dec816724dafbd930e6", "51ec50b90efd490ab3defc45afccfd68", "44a60a24fa074cfebb57208a62cbc283", "c37ae8e129054192a12860d3d6352a5b", "209a9494a6224578b38fece6e8bf766e", "e83c8e5f6d2140cfafd6d09758ec9f27", "b646dc173595499f8e0ab8bf6bff3a4e", "36b32b0437c147a58135e051fbd252db", "f481181ab9074b9c9ac6f540e3fa7b35", "318f344ee6b844fc83230df30d28ad6f", "e8ec61bba888460eb3174b23a91655f1", "4b839619138749368ec354f59a9696e4", 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"59a127a0154c49e1ad754d8d5d074bda", "ce0f173a96f34612a42ed6056c62daca", "f845db9153c04a2ebdfd362ca3e8da71", "927a32647a024482b5b0a8e3fb754f1c", "fb3ff25e0ee14788a001e2cf1cdd1afd", "2650f1f2653049679709d755664306a8", "e40f2a7dacb941608186d43e990ee688" ] }, "id": "8iZQaBQ64kzh", "outputId": "0b923dd9-2d30-459c-e94b-6e39092cb2fa" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "==============================================================================\n", "ARM TRANSFER REPAIR -- keys or anchors?\n", "==============================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- MEASURED BASELINE (2026-08-02, do not re-derive) --------------------------\n", " v2 + v2 arms 0.7287\n", " -b + v2 arms (as-is) 0.6863 (-0.0424)\n", " BARS: stage A passes at >= 0.7100, partial at >= 0.6980\n", "-- TASKS ---------------------------------------------------------------------\n", " 8 tasks: ['STS-B', 'SICK-R', 'STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'BIOSSES']\n", "-- STREAMS -------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/5.15k [00:00 SPLIT CAUSE. Re-calibrating the keys recovers some of the gap;\n", " the remainder sits in the anchors themselves. Stage B measures\n", " how much of it retraining can reach.\n", "-- STAGE B -- RETRAIN ANCHORS ON -b ------------------------------------------\n", " 3 anchors x 1,500 steps. Identical data,\n", " spec and step count to the v2 anchors, so the ONLY difference is\n", " the trunk they were fit against.\n", "-- ANCHOR 'equiv' on -b ------------------------------------------------------\n", " equiv 199,835 rows | 56,737 distinct | space/draws 0.284\n", " equiv 100/1,500 loss 2.5849 acc 0.441 gate 0.0517 drift 0.0674rad 0m\n", " equiv 200/1,500 loss 2.3470 acc 0.441 gate 0.0539 drift 0.0778rad 1m\n", " equiv 300/1,500 loss 2.0577 acc 0.465 gate 0.0558 drift 0.0899rad 1m\n", " equiv 400/1,500 loss 2.1364 acc 0.516 gate 0.0575 drift 0.1013rad 2m\n", " equiv 500/1,500 loss 2.0251 acc 0.484 gate 0.0592 drift 0.1126rad 2m\n", " equiv 600/1,500 loss 2.0770 acc 0.500 gate 0.0606 drift 0.1217rad 2m\n", " equiv 700/1,500 loss 1.7445 acc 0.547 gate 0.0620 drift 0.1289rad 3m\n", " equiv 800/1,500 loss 1.8592 acc 0.512 gate 0.0631 drift 0.1346rad 3m\n", " equiv 900/1,500 loss 1.9838 acc 0.496 gate 0.0640 drift 0.1394rad 4m\n", " equiv 1,000/1,500 loss 1.6077 acc 0.594 gate 0.0647 drift 0.1422rad 4m\n", " equiv 1,100/1,500 loss 1.8566 acc 0.516 gate 0.0652 drift 0.1446rad 4m\n", " equiv 1,200/1,500 loss 1.6949 acc 0.543 gate 0.0656 drift 0.1457rad 5m\n", " equiv 1,300/1,500 loss 1.7757 acc 0.527 gate 0.0658 drift 0.1465rad 5m\n", " equiv 1,400/1,500 loss 1.8790 acc 0.512 gate 0.0658 drift 0.1468rad 5m\n", " equiv 1,500/1,500 loss 1.9530 acc 0.500 gate 0.0659 drift 0.1468rad 6m\n", "-- ANCHOR 'simplify' on -b ---------------------------------------------------\n", " simplify 384,994 rows | 372,053 distinct | space/draws 0.966\n", " simplify 100/1,500 loss 0.1219 acc 0.977 gate 0.0521 drift 0.0541rad 0m\n", " simplify 200/1,500 loss 0.0707 acc 0.988 gate 0.0557 drift 0.0640rad 1m\n", " simplify 300/1,500 loss 0.0719 acc 0.988 gate 0.0581 drift 0.0690rad 1m\n", " simplify 400/1,500 loss 0.0379 acc 1.000 gate 0.0600 drift 0.0727rad 2m\n", " simplify 500/1,500 loss 0.0538 acc 0.992 gate 0.0615 drift 0.0742rad 2m\n", " simplify 600/1,500 loss 0.0252 acc 0.996 gate 0.0627 drift 0.0756rad 3m\n", " simplify 700/1,500 loss 0.0402 acc 0.988 gate 0.0638 drift 0.0785rad 3m\n", " simplify 800/1,500 loss 0.0198 acc 0.996 gate 0.0645 drift 0.0794rad 3m\n", " simplify 900/1,500 loss 0.0151 acc 1.000 gate 0.0651 drift 0.0801rad 4m\n", " simplify 1,000/1,500 loss 0.0411 acc 0.996 gate 0.0656 drift 0.0803rad 4m\n", " simplify 1,100/1,500 loss 0.0318 acc 0.992 gate 0.0660 drift 0.0806rad 5m\n", " simplify 1,200/1,500 loss 0.0387 acc 0.996 gate 0.0661 drift 0.0808rad 5m\n", " simplify 1,300/1,500 loss 0.0189 acc 1.000 gate 0.0663 drift 0.0809rad 5m\n", " simplify 1,400/1,500 loss 0.0395 acc 0.984 gate 0.0663 drift 0.0809rad 6m\n", " simplify 1,500/1,500 loss 0.0266 acc 0.996 gate 0.0663 drift 0.0810rad 6m\n", "-- ANCHOR 'paraphrase' on -b -------------------------------------------------\n", " paraphrase 101,364 rows | 59,877 distinct | space/draws 0.591\n", " paraphrase 100/1,500 loss 0.4294 acc 0.863 gate 0.0515 drift 0.0546rad 0m\n", " paraphrase 200/1,500 loss 0.3469 acc 0.887 gate 0.0542 drift 0.0634rad 1m\n", " paraphrase 300/1,500 loss 0.3377 acc 0.887 gate 0.0563 drift 0.0719rad 1m\n", " paraphrase 400/1,500 loss 0.3543 acc 0.867 gate 0.0582 drift 0.0803rad 2m\n", " paraphrase 500/1,500 loss 0.3572 acc 0.887 gate 0.0598 drift 0.0869rad 2m\n", " paraphrase 600/1,500 loss 0.3164 acc 0.887 gate 0.0612 drift 0.0925rad 3m\n", " paraphrase 700/1,500 loss 0.2537 acc 0.922 gate 0.0625 drift 0.0975rad 3m\n", " paraphrase 800/1,500 loss 0.3080 acc 0.879 gate 0.0635 drift 0.1010rad 3m\n", " paraphrase 900/1,500 loss 0.2598 acc 0.938 gate 0.0644 drift 0.1040rad 4m\n", " paraphrase 1,000/1,500 loss 0.2747 acc 0.879 gate 0.0650 drift 0.1064rad 4m\n", " paraphrase 1,100/1,500 loss 0.3166 acc 0.879 gate 0.0654 drift 0.1082rad 5m\n", " paraphrase 1,200/1,500 loss 0.1957 acc 0.945 gate 0.0657 drift 0.1090rad 5m\n", " paraphrase 1,300/1,500 loss 0.2954 acc 0.898 gate 0.0659 drift 0.1095rad 5m\n", " paraphrase 1,400/1,500 loss 0.2466 acc 0.918 gate 0.0659 drift 0.1097rad 6m\n", " paraphrase 1,500/1,500 loss 0.2033 acc 0.922 gate 0.0659 drift 0.1097rad 6m\n", " [publish] AbstractPhil/captionbert-8192-v2-B/amoe/b-collective (stage B: anchors retrained on -b)\n", " 3 anchors x 12 sites | 2,304 routing keys trainable | anchors frozen: True\n", " align 100/800 src=mix loss 1.3335 acc 0.715 usage ['0.43', '0.26', '0.32'] 1m\n", " align 200/800 src=mix loss 0.6129 acc 0.840 usage ['0.46', '0.23', '0.31'] 2m\n", " align 300/800 src=mix loss 0.6929 acc 0.812 usage ['0.47', '0.23', '0.30'] 2m\n", " align 400/800 src=mix loss 0.5824 acc 0.820 usage ['0.42', '0.25', '0.33'] 3m\n", " align 500/800 src=mix loss 0.5189 acc 0.863 usage ['0.43', '0.25', '0.31'] 4m\n", " align 600/800 src=mix loss 0.5977 acc 0.820 usage ['0.42', '0.28', '0.30'] 5m\n", " align 700/800 src=mix loss 0.7267 acc 0.793 usage ['0.41', '0.29', '0.30'] 5m\n", " align 800/800 src=mix loss 0.7844 acc 0.801 usage ['0.39', '0.30', '0.31'] 6m\n", " |w/z| after : {'equiv': 0.4987, 'simplify': 0.1944, 'paraphrase': 0.181}\n", "-- STAGE B RESULTS (-b, native anchors) --------------------------------------\n", " mask STS-B SICK-R STS12 STS13 STS14 STS15 STS16 BIOSSES mean\n", " OFF 0.5752 0.6548 0.5012 0.6037 0.5470 0.7146 0.6782 0.5500 0.6031\n", " equiv-only 0.7219 0.7200 0.6381 0.6890 0.6410 0.7793 0.7143 0.5702 0.6842\n", " simplify-only 0.5995 0.6603 0.5266 0.6325 0.5760 0.7402 0.6896 0.5789 0.6254\n", " paraphrase-only 0.6137 0.6612 0.5177 0.6361 0.5808 0.7325 0.7325 0.5612 0.6295\n", " COLLECTIVE 0.7675 0.7374 0.6706 0.7381 0.6945 0.8109 0.7695 0.6472 0.7294\n", "-- STAGE B VERDICT -----------------------------------------------------------\n", " v2 arms on v2 0.7287\n", " v2 arms on -b 0.6863\n", " + re-aligned keys 0.6987\n", " native -b anchors 0.7294\n", " => ANCHORS ARE TRUNK-BOUND, and that is all. Trained natively,\n", " -b adapts as well as v2 did. Budget one anchor set per trunk.\n", " [publish] AbstractPhil/captionbert-8192-v2-B/amoe/b-collective (final)\n", "-- DONE ----------------------------------------------------------------------\n", " artifacts in /content/b_arms\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================================================\n", "# CAPTIONBERT FULL BENCHMARK -- teachers, MiniLM, both trunks, arms\n", "#\n", "# One harness, one pass, every model measured on the SAME eight tasks with the\n", "# SAME pooling and normalization. The card tables so far mixed sources: the\n", "# teacher numbers came from a 2-task run, the trunk numbers from an 8-task run,\n", "# and MiniLM was quoted for scale from a different pass. That is not a fair\n", "# comparison and it is not defensible in a writeup.\n", "#\n", "# WHAT IS MEASURED\n", "# 5 teachers bert-base, ModernBERT-base, roberta-base, albert-base-v2,\n", "# distilbert -- the exact models the consensus was built from\n", "# reference all-MiniLM-L6-v2 (contrastive, 1B+ curated pairs: a\n", "# DIFFERENT comparison class, labelled as such)\n", "# 2 trunks captionbert-8192-v2 (54 chunks) and -b (66 chunks)\n", "# 2 arm sets each trunk with ITS OWN native arms -- anchors are\n", "# trunk-bound (v2 arms on -b cost 31% of their gain)\n", "#\n", "# 8 TASKS: STS-B, SICK-R, STS12-16, BIOSSES. BIOSSES is 100 rows and is the only\n", "# genuinely out-of-domain gauge; it is reported but never used alone.\n", "#\n", "# EVERY MODEL IS MEAN-POOLED AND L2-NORMALIZED. That is the honest setting for\n", "# an untuned encoder and it is what the teachers were consensus-averaged in.\n", "# It is also why bert-base scores low here: raw mean-pooled BERT is a known-weak\n", "# sentence encoder, which is the entire reason Sentence-BERT exists. Beating it\n", "# is a real efficiency result, not a competitive sentence-embedding result --\n", "# the card should say so and the MiniLM row is there to keep that honest.\n", "#\n", "# Config at the top, functionality in the body, run logic at the base.\n", "# ============================================================================\n", "\n", "import gc\n", "import json\n", "import os\n", "import subprocess\n", "import sys\n", "from dataclasses import dataclass, asdict\n", "from typing import Dict, List, Optional, Tuple\n", "\n", "for _p, _i in [(\"datasets\", \"datasets\"), (\"transformers\", \"transformers\"),\n", " (\"scipy\", \"scipy\"), (\"huggingface_hub\", \"huggingface_hub\"),\n", " (\"amoe-lora @ git+https://github.com/AbstractEyes/amoe-lora\", \"amoe\")]:\n", " try:\n", " __import__(_i)\n", " except ImportError:\n", " subprocess.run([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", _p], check=False)\n", "\n", "import numpy as np\n", "import torch\n", "import torch.nn.functional as F\n", "from scipy.stats import spearmanr\n", "from huggingface_hub import hf_hub_download\n", "from transformers import AutoModel, AutoTokenizer\n", "from datasets import load_dataset\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# BASE CONFIG\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "@dataclass\n", "class BaseConfig:\n", " # ---- the five teachers the consensus was built from ----\n", " teachers: tuple = (\n", " (\"bert-base\", \"google-bert/bert-base-uncased\"),\n", " (\"ModernBERT-base\", \"answerdotai/ModernBERT-base\"),\n", " (\"roberta-base\", \"FacebookAI/roberta-base\"),\n", " (\"albert-base-v2\", \"albert/albert-base-v2\"),\n", " (\"distilbert\", \"distilbert/distilbert-base-uncased\"),\n", " )\n", " # ---- reference point, NOT a teacher ----\n", " references: tuple = (\n", " (\"all-MiniLM-L6-v2\", \"sentence-transformers/all-MiniLM-L6-v2\"),\n", " )\n", " # ---- (label, repo, ckpt, arm_dir|None, dispatch|None) ----\n", " # Arm locations are EXPLICIT. Earlier versions resolved them through\n", " # modeling_captionbert.py, which meant the benchmark broke whenever that\n", " # file was mid-update: BOTH repos currently carry a pre-patch copy that\n", " # searches amoe/collective/ and amoe/moe/, so -b 404s. A benchmark should\n", " # not depend on an artifact it is measuring.\n", " trunks: tuple = (\n", " (\"captionbert-v2\", \"AbstractPhil/captionbert-8192-v2\",\n", " \"checkpoints/best_model.pt\", \"amoe/collective\",\n", " \"amoe/collective/captionbert-v2-collective.dispatch.pt\"),\n", " (\"captionbert-b\", \"AbstractPhil/captionbert-8192-v2-B\",\n", " \"checkpoints/final_model.pt\", \"amoe/b-collective\",\n", " \"amoe/b-collective/captionbert-b-arms-native.dispatch.pt\"),\n", " )\n", " # architecture, so the trunk class is local and needs no remote code\n", " vocab_size: int = 30522\n", " d_model: int = 512\n", " n_heads: int = 12 - 4\n", " n_layers: int = 12\n", " d_ff: int = 2048\n", " output_dim: int = 768\n", " max_len: int = 8192\n", " pooling: str = \"mean\"\n", " # anchor spec -- the certified campaign defaults every anchor was built with\n", " n_slots: int = 16\n", " K: int = 64\n", " D: int = 4\n", " tau: float = 0.1\n", " hidden: int = 178\n", " gate_init: float = -3.0\n", " align_emb: int = 64\n", "\n", " tasks: tuple = (\n", " (\"STS-B\", \"mteb/stsbenchmark-sts\"),\n", " (\"SICK-R\", \"mteb/sickr-sts\"),\n", " (\"STS12\", \"mteb/sts12-sts\"),\n", " (\"STS13\", \"mteb/sts13-sts\"),\n", " (\"STS14\", \"mteb/sts14-sts\"),\n", " (\"STS15\", \"mteb/sts15-sts\"),\n", " (\"STS16\", \"mteb/sts16-sts\"),\n", " (\"BIOSSES\", \"mteb/biosses-sts\"),\n", " )\n", "\n", " batch_size: int = 256\n", " max_tokens: int = 64\n", " geom_n: int = 2000\n", " seed: int = 0\n", "\n", " out_json: str = \"full_benchmark.json\"\n", " out_md: str = \"benchmark_tables.md\"\n", " hf_push: bool = False\n", " hf_repos: tuple = (\"AbstractPhil/captionbert-8192-v2\",\n", " \"AbstractPhil/captionbert-8192-v2-B\")\n", " hf_path: str = \"eval\"\n", "\n", "\n", "CFG = BaseConfig()\n", "\n", "\n", "def free_model(*objs):\n", " \"\"\"Drop refs, collect, empty the cache, and report if VRAM is not coming back.\"\"\"\n", " for o in objs:\n", " try:\n", " if o is not None and hasattr(o, \"to\"):\n", " o.to(\"cpu\")\n", " except Exception:\n", " pass\n", " del objs\n", " gc.collect()\n", " if DEVICE == \"cuda\":\n", " torch.cuda.empty_cache()\n", " torch.cuda.synchronize()\n", " held = torch.cuda.memory_allocated() / 1e9\n", " if held > 2.0:\n", " print(f\" [mem] {held:.1f} GB still allocated after teardown -- \"\n", " f\"something is holding a reference\")\n", "\n", "\n", "def line(t=\"\"):\n", " print(\"-\" * 96 if not t else f\"-- {t} \" + \"-\" * max(0, 92 - len(t)))\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# GAUGES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def effective_rank(x):\n", " xc = (x - x.mean(0, keepdim=True)).double()\n", " s2 = torch.linalg.svdvals(xc) ** 2\n", " return float((s2.sum() ** 2 / (s2 ** 2).sum()).item())\n", "\n", "\n", "@torch.no_grad()\n", "def score(enc, task, cfg):\n", " a, b, g = task\n", " ea, eb = enc(a), enc(b)\n", " cos = F.cosine_similarity(ea, eb, dim=-1).numpy()\n", " E = torch.cat([ea, eb])\n", " n = min(cfg.geom_n, E.shape[0])\n", " S = E[:n] @ E[:n].T\n", " S.fill_diagonal_(0)\n", " return {\"spearman\": float(spearmanr(cos, g).correlation),\n", " \"self_cos\": float(S.sum() / (n * n - n)),\n", " \"erank\": effective_rank(E[:n])}\n", "\n", "\n", "def load_tasks(cfg):\n", " out = {}\n", " for nm, path in cfg.tasks:\n", " try:\n", " d = load_dataset(path, split=\"test\")\n", " c = d.column_names\n", " a = \"sentence1\" if \"sentence1\" in c else c[0]\n", " b = \"sentence2\" if \"sentence2\" in c else c[1]\n", " sc = \"score\" if \"score\" in c else \"similarity_score\"\n", " out[nm] = (list(d[a]), list(d[b]), np.asarray(d[sc], dtype=float))\n", " print(f\" {nm:8s} {len(out[nm][2]):>6,d} pairs\")\n", " except Exception as e:\n", " print(f\" {nm:8s} SKIPPED ({type(e).__name__})\")\n", " return out\n", "\n", "\n", "def hf_encoder(name, cfg):\n", " \"\"\"\n", " Mean-pooled + L2-normalized. The same treatment every teacher gets.\n", "\n", " NOTE the decorator placement. A previous version put @torch.no_grad() on\n", " THIS function, which only covered from_pretrained -- the returned closure\n", " ran outside it, built an autograd graph on every batch, and exhausted a\n", " 96 GB card (it failed to allocate 16 MiB). It also made the embeddings\n", " carry requires_grad, which broke .numpy() downstream. The guard belongs on\n", " the thing that runs per batch.\n", " \"\"\"\n", " tok = AutoTokenizer.from_pretrained(name)\n", " mdl = AutoModel.from_pretrained(name).to(DEVICE).eval()\n", " for q in mdl.parameters():\n", " q.requires_grad_(False)\n", "\n", " n_par = sum(q.numel() for q in mdl.parameters())\n", "\n", " @torch.no_grad()\n", " def enc(texts):\n", " out = []\n", " for i in range(0, len(texts), cfg.batch_size):\n", " t = tok(list(texts[i:i + cfg.batch_size]), max_length=cfg.max_tokens,\n", " padding=True, truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " h = mdl(**t).last_hidden_state\n", " m = t[\"attention_mask\"].unsqueeze(-1).float()\n", " out.append(F.normalize((h * m).sum(1) / m.sum(1).clamp(min=1),\n", " dim=-1).float().cpu())\n", " return torch.cat(out)\n", " return enc, n_par, mdl\n", "\n", "\n", "class CaptionEncoder(torch.nn.Module):\n", " \"\"\"Local, key-compatible with every captionbert-v2-family checkpoint.\"\"\"\n", "\n", " def __init__(self, cfg):\n", " super().__init__()\n", " import torch.nn as nn\n", " d = cfg.d_model\n", " self.pad_token_id, self.pooling = 0, cfg.pooling\n", " self.token_emb = nn.Embedding(cfg.vocab_size, d, padding_idx=0)\n", " self.pos_emb = nn.Embedding(cfg.max_len, d)\n", " self.emb_norm = nn.LayerNorm(d)\n", " self.emb_drop = nn.Dropout(0.1)\n", " layer = nn.TransformerEncoderLayer(\n", " d_model=d, nhead=cfg.n_heads, dim_feedforward=cfg.d_ff, dropout=0.1,\n", " activation=\"gelu\", batch_first=True, norm_first=True)\n", " self.encoder = nn.TransformerEncoder(layer, num_layers=cfg.n_layers,\n", " enable_nested_tensor=False)\n", " self.output_proj = nn.Sequential(\n", " nn.Linear(d, d), nn.GELU(), nn.LayerNorm(d), nn.Linear(d, cfg.output_dim))\n", "\n", " def forward(self, input_ids, attention_mask=None):\n", " L = input_ids.shape[1]\n", " pos = torch.arange(L, device=input_ids.device).unsqueeze(0)\n", " x = self.emb_drop(self.emb_norm(self.token_emb(input_ids) + self.pos_emb(pos)))\n", " kpm = (~attention_mask.bool()) if attention_mask is not None \\\n", " else (input_ids == self.pad_token_id)\n", " for mod in self.encoder.layers:\n", " x = mod(x, src_key_padding_mask=kpm)\n", " if self.encoder.norm is not None:\n", " x = self.encoder.norm(x)\n", " if self.pooling == \"cls\":\n", " pooled = x[:, 0]\n", " else:\n", " m = (attention_mask.unsqueeze(-1).to(x.dtype) if attention_mask is not None\n", " else (~kpm).unsqueeze(-1).to(x.dtype))\n", " pooled = (x * m).sum(1) / m.sum(1).clamp(min=1)\n", " return F.normalize(self.output_proj(pooled), dim=-1)\n", "\n", "\n", "def trunk_encoder(cfg, repo, ckpt, tok):\n", " m = CaptionEncoder(cfg)\n", " sd = torch.load(hf_hub_download(repo, ckpt), weights_only=True, map_location=\"cpu\")\n", " m.load_state_dict(sd, strict=True)\n", " m = m.to(DEVICE).eval()\n", " for q in m.parameters():\n", " q.requires_grad_(False)\n", " n_par = sum(q.numel() for q in m.parameters())\n", "\n", " @torch.no_grad()\n", " def enc(texts):\n", " out = []\n", " for i in range(0, len(texts), cfg.batch_size):\n", " t = tok(list(texts[i:i + cfg.batch_size]), max_length=cfg.max_tokens,\n", " padding=True, truncation=True, return_tensors=\"pt\").to(DEVICE)\n", " out.append(m(t[\"input_ids\"], t[\"attention_mask\"]).float().cpu())\n", " return torch.cat(out)\n", " return enc, n_par, m\n", "\n", "\n", "def attach_arms(cfg, model, repo, arm_dir, dispatch_path):\n", " \"\"\"\n", " Inline attach: anchors and dispatch come from EXPLICIT paths in `repo`.\n", " No modeling_captionbert.py, no AMOE_FALLBACKS, nothing that can go stale.\n", " Returns (dispatch modules, arm names) and leaves every arm enabled.\n", " \"\"\"\n", " import torch.nn as nn\n", " from amoe.core.adapter import AdapterSpec, RelayPatchwork\n", " from amoe.core.dispatch import AnchorDispatch, BlockWithDispatch\n", " from amoe.io.checkpoint import load_anchor, load_dispatch\n", "\n", " dck = load_dispatch(hf_hub_download(repo, dispatch_path))\n", " names = list(dck.meta.get(\"anchors\", []))\n", " tau = float(dck.meta.get(\"tau\", cfg.tau))\n", " cks = [load_anchor(hf_hub_download(repo, f\"{arm_dir}/{n}.anchor.pt\")) for n in names]\n", " spec = AdapterSpec(n_slots=cfg.n_slots, K=cfg.K, D=cfg.D, tau=cfg.tau,\n", " hidden=cfg.hidden, gate_init=cfg.gate_init, zero_init_head=True)\n", " layers = list(model.encoder.layers)\n", " model._orig_layers = layers\n", " new, disps = [], []\n", " for i, layer in enumerate(layers):\n", " stack = nn.ModuleList()\n", " for ck in cks:\n", " a = RelayPatchwork(cfg.d_model, spec)\n", " a.load_state_dict({k[len(f\"{i}.\"):]: v for k, v in ck.adapters.items()\n", " if k.startswith(f\"{i}.\")})\n", " for q in a.parameters():\n", " q.requires_grad_(False)\n", " stack.append(a)\n", " dp = AnchorDispatch(stack.to(DEVICE), cfg.d_model,\n", " emb=int(dck.meta.get(\"emb\", cfg.align_emb)),\n", " tau=tau).to(DEVICE)\n", " with torch.no_grad():\n", " dp.dispatch.copy_(dck.dispatch[i][\"dispatch\"].to(DEVICE))\n", " dp.key_proj.copy_(dck.dispatch[i][\"key_proj\"].to(DEVICE))\n", " for q in dp.parameters():\n", " q.requires_grad_(False)\n", " disps.append(dp)\n", " new.append(BlockWithDispatch(layer, dp))\n", " model.encoder.layers = nn.ModuleList(new)\n", " return disps, names\n", "\n", "\n", "def detach_arms(model):\n", " import torch.nn as nn\n", " if getattr(model, \"_orig_layers\", None) is not None:\n", " model.encoder.layers = nn.ModuleList(model._orig_layers)\n", " model._orig_layers = None\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# TABLES\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def render(rows, tasks, title, params=None):\n", " tk = list(tasks)\n", " line(title)\n", " print(f\" {'model':26s}{'params':>10s}\" + \"\".join(f\"{t:>9s}\" for t in tk)\n", " + f\"{'mean':>9s}\")\n", " for label, r in rows.items():\n", " vals = [r[t][\"spearman\"] for t in tk]\n", " p = params.get(label) if params else None\n", " ps = f\"{p/1e6:>9.1f}M\" if p else f\"{'':>10s}\"\n", " print(f\" {label:26s}{ps}\" + \"\".join(f\"{v:>9.4f}\" for v in vals)\n", " + f\"{np.mean(vals):>9.4f}\")\n", "\n", "\n", "def markdown(rows, tasks, params, note=\"\"):\n", " tk = list(tasks)\n", " out = [\"| model | params | \" + \" | \".join(tk) + \" | mean |\",\n", " \"|---\" * (len(tk) + 3) + \"|\"]\n", " for label, r in rows.items():\n", " vals = [r[t][\"spearman\"] for t in tk]\n", " p = params.get(label)\n", " out.append(f\"| {label} | {f'{p/1e6:.1f}M' if p else '--'} | \"\n", " + \" | \".join(f\"{v:.4f}\" for v in vals)\n", " + f\" | **{np.mean(vals):.4f}** |\")\n", " return \"\\n\".join(out) + (\"\\n\\n\" + note if note else \"\")\n", "\n", "\n", "# ══════════════════════════════════════════════════════════════════\n", "# RUN\n", "# ══════════════════════════════════════════════════════════════════\n", "\n", "def run(cfg: BaseConfig = CFG):\n", " print(\"=\" * 96)\n", " print(\"CAPTIONBERT FULL BENCHMARK -- one harness, every model, eight tasks\")\n", " print(\"=\" * 96)\n", " if DEVICE == \"cuda\":\n", " print(f\"gpu={torch.cuda.get_device_name()} \"\n", " f\"vram={torch.cuda.get_device_properties(0).total_memory/1e9:.0f}GB\")\n", " torch.manual_seed(cfg.seed)\n", " line(\"TASKS\")\n", " tasks = load_tasks(cfg)\n", " if not tasks:\n", " raise RuntimeError(\"no tasks loaded\")\n", " tok = AutoTokenizer.from_pretrained(\"google-bert/bert-base-uncased\")\n", "\n", " rows, params, geom, groups = {}, {}, {}, {\"teachers\": [], \"reference\": [],\n", " \"trunks\": [], \"arms\": []}\n", "\n", " # ---- teachers ----\n", " for label, name in cfg.teachers:\n", " line(f\"TEACHER {label}\")\n", " try:\n", " enc, n_par, mdl = hf_encoder(name, cfg)\n", " rows[label] = {k: score(enc, v, cfg) for k, v in tasks.items()}\n", " params[label] = n_par\n", " groups[\"teachers\"].append(label)\n", " ref = list(tasks)[0]\n", " geom[label] = {k: rows[label][ref][k] for k in (\"self_cos\", \"erank\")}\n", " print(f\" {n_par:,} params | {ref} {rows[label][ref]['spearman']:.4f} \"\n", " f\"| self_cos {rows[label][ref]['self_cos']:+.4f} \"\n", " f\"| erank {rows[label][ref]['erank']:.1f}\")\n", " free_model(mdl, enc)\n", " except Exception as e:\n", " print(f\" FAILED: {type(e).__name__}: {str(e)[:110]}\")\n", " free_model(locals().get(\"mdl\"), locals().get(\"enc\"))\n", "\n", " # ---- reference ----\n", " for label, name in cfg.references:\n", " line(f\"REFERENCE {label} (contrastive, 1B+ pairs -- different class)\")\n", " try:\n", " enc, n_par, mdl = hf_encoder(name, cfg)\n", " rows[label] = {k: score(enc, v, cfg) for k, v in tasks.items()}\n", " params[label] = n_par\n", " groups[\"reference\"].append(label)\n", " ref = list(tasks)[0]\n", " geom[label] = {k: rows[label][ref][k] for k in (\"self_cos\", \"erank\")}\n", " print(f\" {n_par:,} params | {ref} {rows[label][ref]['spearman']:.4f} \"\n", " f\"| self_cos {rows[label][ref]['self_cos']:+.4f} \"\n", " f\"| erank {rows[label][ref]['erank']:.1f}\")\n", " free_model(mdl, enc)\n", " except Exception as e:\n", " print(f\" FAILED: {type(e).__name__}: {str(e)[:110]}\")\n", " free_model(locals().get(\"mdl\"), locals().get(\"enc\"))\n", "\n", " # ---- trunks, bare and with their OWN arms ----\n", " for label, repo, ckpt, arm_dir, dispatch_path in cfg.trunks:\n", " line(f\"TRUNK {label}\")\n", " enc, n_par, m = trunk_encoder(cfg, repo, ckpt, tok)\n", " rows[label] = {k: score(enc, v, cfg) for k, v in tasks.items()}\n", " params[label] = n_par\n", " groups[\"trunks\"].append(label)\n", " ref = list(tasks)[0]\n", " geom[label] = {k: rows[label][ref][k] for k in (\"self_cos\", \"erank\")}\n", " print(f\" {n_par:,} params | {ref} {rows[label][ref]['spearman']:.4f} \"\n", " f\"| self_cos {rows[label][ref]['self_cos']:+.4f} \"\n", " f\"| erank {rows[label][ref]['erank']:.1f}\")\n", "\n", " if arm_dir:\n", " try:\n", " # EXPLICIT paths in THIS trunk's repo. Anchors are trunk-bound:\n", " # v2's arms on -b cost 31% of their gain, so each trunk gets its own.\n", " disps, anames = attach_arms(cfg, m, repo, arm_dir, dispatch_path)\n", " al = f\"{label} + arms\"\n", " rows[al] = {k: score(enc, v, cfg) for k, v in tasks.items()}\n", " params[al] = n_par + sum(p.numel() for d in disps\n", " for a in d.anchors for p in a.parameters())\n", " groups[\"arms\"].append(al)\n", " geom[al] = {k: rows[al][ref][k] for k in (\"self_cos\", \"erank\")}\n", " print(f\" + arms {anames} from {repo}/{arm_dir}: \"\n", " f\"{ref} {rows[al][ref]['spearman']:.4f}\")\n", " detach_arms(m)\n", " except Exception as e:\n", " msg = str(e)[:110]\n", " print(f\" arms FAILED: {type(e).__name__}: {msg}\")\n", " if \"404\" in msg or \"NotFound\" in type(e).__name__:\n", " print(f\" !! 404: check that {repo}/{arm_dir}/ and\")\n", " print(f\" !! {repo}/{dispatch_path} exist.\")\n", " detach_arms(m)\n", " free_model(m, enc)\n", "\n", " # ---- tables ----\n", " order = groups[\"teachers\"] + groups[\"trunks\"] + groups[\"arms\"] + groups[\"reference\"]\n", " ordered = {k: rows[k] for k in order if k in rows}\n", " render(ordered, tasks, \"FULL BENCHMARK -- every model, mean-pooled, L2-normalized\",\n", " params)\n", "\n", " tk = list(tasks)\n", " line(\"READ\")\n", " tmeans = {k: np.mean([rows[k][t][\"spearman\"] for t in tk])\n", " for k in groups[\"teachers\"] if k in rows}\n", " if tmeans:\n", " bt = max(tmeans, key=tmeans.get)\n", " print(f\" best teacher: {bt} {tmeans[bt]:.4f} \"\n", " f\"({params[bt]/1e6:.1f}M)\")\n", " tot = sum(params[k] for k in tmeans)\n", " for k in groups[\"trunks\"]:\n", " if k in rows:\n", " mv = np.mean([rows[k][t][\"spearman\"] for t in tk])\n", " print(f\" {k:26s} {mv:.4f} ({mv-tmeans[bt]:+.4f} vs best teacher) \"\n", " f\"at {params[k]/tot*100:.0f}% of the teachers' combined params\")\n", " for k in groups[\"arms\"]:\n", " if k in rows:\n", " mv = np.mean([rows[k][t][\"spearman\"] for t in tk])\n", " print(f\" {k:26s} {mv:.4f} ({mv-tmeans[bt]:+.4f} vs best teacher)\")\n", " for k in groups[\"reference\"]:\n", " if k in rows:\n", " mv = np.mean([rows[k][t][\"spearman\"] for t in tk])\n", " print(f\" {k:26s} {mv:.4f} <- 1B+ curated pairs, a DIFFERENT class\")\n", "\n", " line(\"GEOMETRY (first task)\")\n", " print(f\" {'model':26s}{'self_cos':>11s}{'erank':>9s}\")\n", " for k in order:\n", " if k in geom:\n", " print(f\" {k:26s}{geom[k]['self_cos']:>+11.4f}{geom[k]['erank']:>9.1f}\")\n", " print()\n", " print(\" self_cos is the isotropy gauge: mean-pooled BERT-family embeddings sit\")\n", " print(\" in a narrow cone. Low is better and it is the mechanism behind the\")\n", " print(\" trunks' advantage -- cosine discriminates poorly inside a cone.\")\n", "\n", " # ---- markdown for the cards ----\n", " note = (\"All models mean-pooled and L2-normalized, no task tuning, one harness. \"\n", " \"`all-MiniLM-L6-v2` was contrastively trained on 1B+ curated pairs and is \"\n", " \"listed for scale, not as a peer.\")\n", " md = [\"## Benchmark\\n\", markdown(ordered, tasks, params, note), \"\",\n", " \"### Geometry\\n\",\n", " \"| model | self_cos | erank |\", \"|---|---|---|\"]\n", " for k in order:\n", " if k in geom:\n", " md.append(f\"| {k} | {geom[k]['self_cos']:+.4f} | {geom[k]['erank']:.1f} |\")\n", " open(cfg.out_md, \"w\").write(\"\\n\".join(md) + \"\\n\")\n", " json.dump({\"rows\": rows, \"params\": params, \"geometry\": geom,\n", " \"groups\": groups, \"config\": asdict(cfg)},\n", " open(cfg.out_json, \"w\"), indent=2, default=float)\n", " print(f\"\\n wrote {cfg.out_json} and {cfg.out_md} (paste-ready card tables)\")\n", "\n", " if cfg.hf_push:\n", " tokn = os.environ.get(\"HF_TOKEN\")\n", " if not tokn:\n", " try:\n", " from google.colab import userdata\n", " tokn = userdata.get(\"HF_TOKEN\")\n", " except Exception:\n", " tokn = None\n", " if tokn:\n", " from huggingface_hub import HfApi\n", " api = HfApi(token=tokn)\n", " for r in cfg.hf_repos:\n", " for f in (cfg.out_json, cfg.out_md):\n", " try:\n", " api.upload_file(path_or_fileobj=f,\n", " path_in_repo=f\"{cfg.hf_path}/{f}\",\n", " repo_id=r, commit_message=\"full benchmark\")\n", " except Exception as e:\n", " print(f\" push {r} failed: {str(e)[:60]}\")\n", " print(f\" pushed to {list(cfg.hf_repos)}\")\n", " return rows\n", "\n", "\n", "if \"get_ipython\" in globals() or __name__ == \"__main__\":\n", " RESULTS = run(CFG)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "125bec82399c4246b7b6b8eac68301c9", "236ebdf378be4f20b7d2b27a1115dbc1", 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"d31c5a34c9eb430488e4b4cc462817b7", "ef7c5144066d4654a728590560c94772", "77ddb885abf84f3c92314d471e98b568", "dbb16fde99454f48b64d2a9b84ea9987", "a26bd077d65e4e81a36ce9e9c7e16f96", "3656e999dfcf4b91aba2804ac4ba79e0", "640dbea970914d909ba0ffd27c866398", "e960651aa62c4a2b82a467d2ffbf4273", "ae7cb694fa9148c1a1a16f960227fc0e", "4b7f3c78fdcc46ea9e25a853d797fbb8", "c159f114648f49f7ae2ec40b371c15a6", "6ff29ef1713a4d3c8a5cce002937d0e5", "b542ade10b2047d3b3c2d97637ed6adb", "40168301fcd4487b9a5dc329587d8c6a", "6b03239b3f2c4e0aa4ce987d3019b9b3", "d8162bf3d68a411db98ce0713e82afc1", "0602c821d0c54035aca187b529d5f209" ] }, "id": "_zPSWPogVfM_", "outputId": "831c01cd-3a2f-4b59-bc24-3305e37ffc10" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "================================================================================================\n", "CAPTIONBERT FULL BENCHMARK -- one harness, every model, eight tasks\n", "================================================================================================\n", "gpu=NVIDIA RTX PRO 6000 Blackwell Server Edition vram=102GB\n", "-- TASKS ---------------------------------------------------------------------------------------\n", " STS-B 1,379 pairs\n", " SICK-R 9,927 pairs\n", " STS12 3,108 pairs\n", " STS13 1,500 pairs\n", " STS14 3,750 pairs\n", " STS15 3,000 pairs\n", " STS16 1,186 pairs\n", " BIOSSES 100 pairs\n", "-- TEACHER bert-base ---------------------------------------------------------------------------\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00