diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0ffac9a --- /dev/null +++ b/.gitignore @@ -0,0 +1,22 @@ +.env +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +test_gemini.py +.venv/ +venv/ +env/ +*.log + +# Generated data +traces/ +traces_augmented/ +traces_v2_archive/ +logic/ +grids/ +grids_test/ +synthetic/ +test_results.json + diff --git a/ARC-AGI1/002_ivan_arc1.ipynb b/ARC-AGI1/002_ivan_arc1.ipynb deleted file mode 100644 index 48c0945..0000000 --- a/ARC-AGI1/002_ivan_arc1.ipynb +++ /dev/null @@ -1,11523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "f5813ce1", - "metadata": { - "papermill": { - "duration": 0.010049, - "end_time": "2025-11-13T03:53:56.647509", - "exception": false, - "start_time": "2025-11-13T03:53:56.637460", - "status": "completed" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import time\n", - "global_end_time = time.time() + 12 * 3600 - 600" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d8359c22", - "metadata": { - "_kg_hide-output": true, - "papermill": { - "duration": 29.050611, - "end_time": "2025-11-13T03:54:25.700923", - "exception": false, - "start_time": "2025-11-13T03:53:56.650312", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found existing installation: tensorflow 2.18.0\n", - "Uninstalling tensorflow-2.18.0:\n", - " Successfully uninstalled tensorflow-2.18.0\n" - ] - } - ], - "source": [ - "!pip uninstall -y tensorflow" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "662f39e4", - "metadata": { - "_kg_hide-input": true, - "papermill": { - "duration": 0.0149, - "end_time": "2025-11-13T03:54:25.718903", - "exception": false, - "start_time": "2025-11-13T03:54:25.704003", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing arc_loader.py\n" - ] - } - ], - "source": [ - "%%writefile arc_loader.py\n", - "import json\n", - "import numpy as np\n", - "from transformers import AutoTokenizer\n", - "\n", - "\n", - "def convert_grid_to_string(grid) -> str:\n", - " text = \"\"\n", - " for row in grid:\n", - " for cell in row:\n", - " text += str(int(cell))\n", - " text += \"\\n\"\n", - " return text.strip()\n", - "\n", - "def is_valid_solution(guess):\n", - " return isinstance(guess, np.ndarray) and guess.ndim == 2 and all(0 < x <= 30 for x in guess.shape)\n", - "\n", - "def shuffled(data_list):\n", - " return np.random.permutation(data_list).tolist()\n", - "\n", - "def permute_mod(a, descriptor, invert=False):\n", - " permutation = [int(i) for i in descriptor if str(i).isdigit()]\n", - " assert sorted(permutation)==list(range(10))\n", - " a = np.asarray(a)\n", - " if a.ndim==3:\n", - " if not invert: permutation = np.argsort(permutation)\n", - " a = a[..., permutation]\n", - " else:\n", - " assert a.ndim==2\n", - " if invert: permutation = np.argsort(permutation)\n", - " a = np.asarray(permutation)[a]\n", - " return a\n", - "\n", - "def permute_rnd_all_(query):\n", - " permutation = np.random.permutation(10).tolist()\n", - " return 'permute' + ''.join(map(str, permutation))\n", - "\n", - "\n", - "class QwenFormatter:\n", - "\n", - " def __init__(self, tokenizer: AutoTokenizer):\n", - " self.tokenizer = tokenizer\n", - "\n", - " def fmt_query(self, query) -> str:\n", - " grid_input = convert_grid_to_string(query[0][\"input\"])\n", - " return \"<|im_start|>user\\n\" + grid_input + \"<|im_end|><|im_start|>assistant\\n\"\n", - "\n", - " def fmt_reply(self, reply) -> str:\n", - " return convert_grid_to_string(reply[0]) + \"<|im_end|>\"\n", - "\n", - " def fmt_train(self, train, last_is_challenge=False) -> str:\n", - " if last_is_challenge:\n", - " test = train[-1]\n", - " train = train[:-1]\n", - " else:\n", - " test = None\n", - " text = \"\"\n", - " for x in train:\n", - " grid_input = convert_grid_to_string(x[\"input\"])\n", - " grid_output = convert_grid_to_string(x[\"output\"])\n", - " text += f\"<|im_start|>user\\n{grid_input}<|im_end|><|im_start|>assistant\\n{grid_output}<|im_end|>\"\n", - " if test is not None:\n", - " text += self.fmt_query([test]) + self.fmt_reply([test[\"output\"]])\n", - " return text\n", - "\n", - " def max_new_tokens(self):\n", - " max_sized_reply = np.zeros([30, 30], dtype=int)\n", - " tokens = self.tokenizer.encode(self.fmt_reply([max_sized_reply]))\n", - " return len(tokens) + 1\n", - "\n", - " def convert_tokens_to_array(self, tokens, limit_rows=30):\n", - " if len(tokens) < 2:\n", - " return None\n", - " text = self.tokenizer.decode(tokens[:-1])\n", - " try:\n", - " lines = text.strip().split(\"\\n\")\n", - " by_rows = [row for row in [[int(x) for x in line if x.isdigit()] for line in lines] if len(row)]\n", - " if len(by_rows) > limit_rows:\n", - " by_rows = by_rows[:limit_rows]\n", - " array = np.array(by_rows, dtype=int)\n", - " if is_valid_solution(array):\n", - " return array\n", - " except:\n", - " pass\n", - " return None\n", - "\n", - "\n", - "class ArcDataset:\n", - "\n", - " @staticmethod\n", - " def forward_mod(a, key, use_perm=True):\n", - " if a is None: return a\n", - " for op in key.split('.')[1:]:\n", - " if op=='rot90': a = np.rot90(a)\n", - " elif op=='transpose': a = np.swapaxes(a, 0, 1)\n", - " elif op.startswith('permute'): a = permute_mod(a, op, invert=False) if use_perm else a\n", - " elif op.startswith('copy'): a = np.copy(a)\n", - " elif op.startswith('out'): a = a\n", - " elif op.startswith('ex'): a = a\n", - " elif op.startswith('run'): a = a\n", - " else: raise NotImplementedError(f\"Inversion of operation '{op}' unknown.\")\n", - " return a\n", - "\n", - " @staticmethod\n", - " def invert_mod(a, key, inv_perm=True):\n", - " if a is None: return a\n", - " for op in key.split('.')[1:][::-1]:\n", - " if op=='rot90': a = np.rot90(a, k=3)\n", - " elif op=='transpose': a = np.swapaxes(a, 0, 1)\n", - " elif op.startswith('permute'): a = permute_mod(a, op, invert=True) if inv_perm else a\n", - " elif op.startswith('copy'): a = np.copy(a)\n", - " elif op.startswith('out'): a = a\n", - " elif op.startswith('ex'): a = a\n", - " elif op.startswith('run'): a = a\n", - " else: raise NotImplementedError(f\"Inversion of operation '{op}' unknown.\")\n", - " return a\n", - "\n", - " def __init__(self, queries, replies={}, keys=None, is_orig=False):\n", - " if keys is not None: keys = [k for k in keys if k is not None]\n", - " self.queries = queries if keys is None else {k: queries[k] for k in keys}\n", - " self.replies = replies if keys is None else {k: replies[k] for k in keys if k in replies}\n", - " self.is_orig = is_orig\n", - " self.keys = sorted(queries.keys()) if keys is None else keys\n", - " self.transposed_dataset = None\n", - "\n", - " def change_keys(self, keys, keep_flags=False):\n", - " flags = dict(is_orig=self.is_orig) if keep_flags else {}\n", - " return self.__class__(queries=self.queries, replies=self.replies, keys=keys, **flags)\n", - "\n", - " @classmethod\n", - " def from_file(cls, queries_file, keys=None):\n", - " with open(queries_file) as f:\n", - " queries = f.read()\n", - " return cls(\n", - " queries=json.loads(queries),\n", - " is_orig=True,\n", - " keys=keys,\n", - " )\n", - "\n", - " def load_replies(self, replies_file):\n", - " print(f\"*** Load solutions from '{replies_file}'...\")\n", - " with open(replies_file) as f: replies = f.read()\n", - " replies_parsed = json.loads(replies)\n", - " self.replies = {k: replies_parsed[k] for k in self.keys}\n", - " return self\n", - "\n", - " def split_multi_replies(self):\n", - " key_indices = [(k, i) for k in self.keys for i in range(len(self.queries[k]['test']))]\n", - " return self.__class__(\n", - " keys=[f'{k}_{i}' for k, i in key_indices],\n", - " queries={f'{k}_{i}': {'train': self.queries[k]['train'], 'test': [self.queries[k]['test'][i]]} for k, i in key_indices},\n", - " replies={f'{k}_{i}': [self.replies[k][i]] for k, i in key_indices if k in self.replies},\n", - " )\n", - "\n", - " def shuffled(self):\n", - " return self.__class__(queries=self.queries, replies=self.replies, keys=shuffled(self.keys))\n", - "\n", - " def append(*datasets):\n", - " return datasets[0].__class__(\n", - " queries={k: v for d in datasets for k, v in d.queries.items()},\n", - " replies={k: v for d in datasets for k, v in d.replies.items()},\n", - " keys =[k for d in datasets for k in d.keys ],\n", - " )\n", - "\n", - " def mod_single(self, mod_func, descriptor, i, keep_key, inputs_only):\n", - " queries = {}\n", - " replies = {}\n", - " keys = []\n", - " for k0 in self.keys:\n", - " desc = (('copy{i}' if mod_func is np.copy else mod_func.__name__) if descriptor is None else descriptor if isinstance(descriptor, str) else descriptor(self.queries[k0])).format(i=i)\n", - " func = lambda a, d: np.asarray(mod_func(a) if descriptor is None else mod_func(a, d)).tolist()\n", - " k1 = k0 if keep_key else f\"{k0}.{'I' if inputs_only else ''}{desc}\"\n", - " keys.append(k1)\n", - " queries[k1] = {m: [{t: (func(a, desc) if t=='input' or not inputs_only else a) for t, a in x.items()} for x in e] for m, e in self.queries[k0].items()}\n", - " if k0 in self.replies:\n", - " replies[k1] = [func(a, desc) for a in self.replies[k0]]\n", - " ret = self.__class__(queries=queries, replies=replies, keys=keys)\n", - " return ret\n", - "\n", - " def mod(self, mod_func, descriptor=None, n=1, stack=None, keep=False, keep_key=False, shuffle=False, join=True, inputs_only=False):\n", - " assert not (keep and keep_key)\n", - " cur = self\n", - " ret = [cur.shuffled() if shuffle else cur] if keep else []\n", - " if stack is None: stack = mod_func.__name__.startswith('rot')\n", - " for i in range(n):\n", - " cur = (cur if stack else self).mod_single(mod_func, descriptor, i=i, keep_key=keep_key, inputs_only=inputs_only)\n", - " ret.append(cur.shuffled() if shuffle else cur)\n", - " return self.__class__.append(*ret) if join else ret\n", - "\n", - " def get(self, key, formatter: QwenFormatter):\n", - " train = formatter.fmt_train(self.queries[key]['train'])\n", - " query = formatter.fmt_query(self.queries[key]['test'])\n", - " reply = formatter.fmt_reply(self.replies[key]) if key in self.replies else ''\n", - " text = train+query+reply if reply else formatter.fmt_train(self.queries[key]['train'], last_is_challenge=True)\n", - " return dict(key=key, train=train, query=query, reply=reply, input=train+query, text=text)\n", - "\n", - " def as_list(self, formatter: QwenFormatter):\n", - " return [self.get(key, formatter) for key in self.keys]\n", - "\n", - " def get_length(self, key, formatter: QwenFormatter, name, max_of_transposed=False):\n", - " if formatter is None:\n", - " if name=='input': return sum(np.prod(np.shape(v)) for v3 in self.queries[key].values() for v2 in v3 for v in v2.values())\n", - " elif name=='reply': return sum(np.prod(np.shape(v)) for v in self.replies[key])\n", - " else: assert False\n", - " else:\n", - " datasets = [self]\n", - " if max_of_transposed:\n", - " if self.transposed_dataset is None: self.transposed_dataset = self.mod(np.transpose, keep=False, keep_key=True)\n", - " datasets.append(self.transposed_dataset)\n", - " return max(len(formatter.tokenizer.encode(ds.get(key, formatter=formatter)[name])) for ds in datasets)\n", - "\n", - " def cut_to_len(self, formatter, name, max_len, from_end=False):\n", - " temp_ds = self.change_keys(self.keys)\n", - " new_keys = []\n", - " new_queries = {}\n", - " new_replies = {}\n", - " for key in self.keys:\n", - " reply = temp_ds.replies.get(key)\n", - " while max_len9) else '').join(map(str, p.tolist()))\n", - " new_keys.append(new_key)\n", - " new_queries[new_key] = {k: (np.array(v, dtype=object)[p].tolist() if k=='train' else v) for k, v in self.queries[key].items()}\n", - " if key in self.replies: new_replies[new_key] = self.replies[key]\n", - " return self.__class__(queries=new_queries, replies=new_replies, keys=new_keys)\n", - "\n", - " def augment(self, n=1, shfl_keys=False, seed=42):\n", - " np.random.seed(seed)\n", - " d = self\n", - " d = d.mod(np.transpose, keep=True)\n", - " d = d.mod(np.rot90, n=3, keep=True)\n", - " d = d.mod(permute_mod, permute_rnd_all_, n=n, shuffle=shfl_keys, keep=False)\n", - " d = d.shuffle_ex()\n", - " return d\n", - "\n", - " def get_submission(self, results=None):\n", - " assert self.is_orig==True, 'Must be run on original dataset.'\n", - " submission = {k: [{f'attempt_{i+1}': [[0]] for i in range(2)} for _ in range(len(self.queries[k]['test']))] for k in self.keys}\n", - " if results is not None: self.fill_submission(results, submission)\n", - " return submission\n", - "\n", - " @staticmethod\n", - " def fill_submission(results, submission):\n", - " print(f'*** Generating submission for {len(results)} outputs...')\n", - " for k, v in results.items():\n", - " base_id, base_nr = k.split('_')\n", - " target_dict = submission[base_id][int(base_nr)]\n", - " for i, g in enumerate(v[:len(target_dict)]):\n", - " target_dict[f'attempt_{i+1}'] = g.tolist()\n", - "\n", - " def validate_submission(self, submission):\n", - " assert self.is_orig==True, 'Must be run on original dataset.'\n", - " score = 0\n", - " for k, v in self.replies.items():\n", - " for i, r in enumerate(v):\n", - " for attempt in ['attempt_1', 'attempt_2']:\n", - " if np.array_equal(r, submission[k][i][attempt]):\n", - " score += 1 / len(v)\n", - " break\n", - " return score" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b699126f", - "metadata": { - "_kg_hide-input": true, - "papermill": { - "duration": 0.009288, - "end_time": "2025-11-13T03:54:25.730969", - "exception": false, - "start_time": "2025-11-13T03:54:25.721681", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing arc_decoder.py\n" - ] - } - ], - "source": [ - "%%writefile arc_decoder.py\n", - "import os\n", - "import bz2\n", - "import pickle\n", - "import numpy as np\n", - "\n", - "def hashable(guess):\n", - " return tuple(map(tuple, guess))\n", - "\n", - "def score_sum(guesses, getter):\n", - " guess_list = list(guesses.values())\n", - " scores = {}\n", - " for g in guess_list:\n", - " h = hashable(g[\"solution\"])\n", - " x = scores[h] = scores.get(h, [[], g[\"solution\"]])\n", - " x[0].append(g)\n", - " scores = [(getter(sc), o) for sc, o in scores.values()]\n", - " scores = sorted(scores, key=(lambda x: x[0]), reverse=True)\n", - " ordered_outputs = [x[-1] for x in scores]\n", - " return ordered_outputs\n", - "\n", - "def getter_full_probmul_3(guesses, baseline=3):\n", - " inf_score = np.sum([baseline-g[\"beam_score\"] for g in guesses])\n", - " aug_score = np.mean([np.sum([baseline-s for s in g[\"score_aug\"]]) for g in guesses])\n", - " return inf_score + aug_score\n", - "\n", - "def score_full_probmul_3(guesses):\n", - " return score_sum(guesses, getter_full_probmul_3)\n", - "\n", - "def getter_kgmon(guesses):\n", - " inf_score = len(guesses)\n", - " aug_score = np.mean([np.mean(g[\"score_aug\"]) for g in guesses])\n", - " return inf_score - aug_score\n", - "\n", - "def score_kgmon(guesses):\n", - " return score_sum(guesses, getter_kgmon)\n", - "\n", - "\n", - "selection_algorithms = [\n", - " score_full_probmul_3,\n", - " score_kgmon,\n", - "]\n", - "\n", - "\n", - "class ArcDecoder:\n", - " \n", - " def __init__(self, dataset, n_guesses):\n", - " self.dataset = dataset\n", - " self.n_guesses = n_guesses\n", - " self.decoded_results = {}\n", - "\n", - " def load_decoded_results(self, store, run_name=\"\"):\n", - " for key in os.listdir(store):\n", - " with bz2.BZ2File(os.path.join(store, key)) as f:\n", - " outputs = pickle.load(f)\n", - " base_key = key.split(\".\")[0]\n", - " self.decoded_results[base_key] = self.decoded_results.get(base_key, {})\n", - " for i, sample in enumerate(outputs):\n", - " self.decoded_results[base_key][f\"{key}{run_name}.out{i}\"] = sample\n", - "\n", - " def run_selection_algo(self, selection_algorithm=score_kgmon):\n", - " return {bk: selection_algorithm({k: g for k, g in v.items()}) for bk, v in self.decoded_results.items()}\n", - "\n", - " def benchmark_selection_algos(self):\n", - " print(\"*** Benchmark selection algorithms...\")\n", - "\n", - " labels = {}\n", - " num_tasks_per_puzzle = {}\n", - " num_solved_keys = 0\n", - " num_total_keys = 0\n", - "\n", - " correct_beam_scores = []\n", - "\n", - " for basekey, basevalues in self.decoded_results.items():\n", - "\n", - " mult_key, mult_sub = basekey.split(\"_\")\n", - " num_tasks_per_puzzle[mult_key] = max(num_tasks_per_puzzle.get(mult_key, 0), int(mult_sub) + 1)\n", - "\n", - " labels[basekey] = correct_solution = self.dataset.replies[basekey][0]\n", - "\n", - " for subkey, sample in basevalues.items():\n", - "\n", - " solution = sample[\"solution\"]\n", - " beam_score = sample[\"beam_score\"]\n", - " aug_mean = np.mean(sample[\"score_aug\"])\n", - "\n", - " if np.shape(correct_solution) != np.shape(solution):\n", - " corr_str = \"bad_xy_size\"\n", - " elif np.array_equal(correct_solution, solution):\n", - " corr_str = \"ALL_CORRECT\"\n", - " num_solved_keys += 1\n", - " correct_beam_scores.append(beam_score)\n", - " else:\n", - " corr_str = \"bad_content\"\n", - "\n", - " output_len = f\"{solution.shape[0]}x{solution.shape[1]}\"\n", - "\n", - " if corr_str == \"ALL_CORRECT\":\n", - " print(f\"{corr_str}:{beam_score:8.5f} - {aug_mean:8.5f} {output_len:5s} [{subkey}]\")\n", - " num_total_keys += 1\n", - "\n", - " print(f\" subkeys: {num_solved_keys}/{num_total_keys}\")\n", - " print(f\" avg correct beam score: {np.mean(correct_beam_scores):8.5f}\")\n", - " print(f\" max correct beam score: {np.max(correct_beam_scores):8.5f}\")\n", - "\n", - " num_puzzles = len(num_tasks_per_puzzle)\n", - "\n", - " for selection_algorithm in selection_algorithms:\n", - " name = selection_algorithm.__name__\n", - " selected = self.run_selection_algo(selection_algorithm)\n", - " correct_puzzles = {k for k, v in selected.items() if any(np.array_equal(guess, labels[k]) for guess in v[:self.n_guesses])}\n", - " print(correct_puzzles)\n", - " score = sum(1/num_tasks_per_puzzle[k.split(\"_\")[0]] for k in correct_puzzles)\n", - " print(f\" acc: {score:5.1f}/{num_puzzles:3} ('{name}')\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "b354de2b", - "metadata": { - "_kg_hide-input": true, - "papermill": { - "duration": 0.015046, - "end_time": "2025-11-13T03:54:25.748749", - "exception": false, - "start_time": "2025-11-13T03:54:25.733703", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing arc_solver.py\n" - ] - } - ], - "source": [ - "%%writefile arc_solver.py\n", - "from unsloth import FastLanguageModel, UnslothTrainingArguments, UnslothTrainer\n", - "from arc_loader import ArcDataset, QwenFormatter\n", - "\n", - "import gc\n", - "import os\n", - "import io\n", - "import time\n", - "import torch\n", - "import numpy as np\n", - "from tqdm import tqdm\n", - "from datasets import Dataset\n", - "from collections import defaultdict\n", - "\n", - "from typing import Any, Union\n", - "from transformers import DataCollatorForLanguageModeling\n", - "\n", - "import logging\n", - "from contextlib import redirect_stdout, redirect_stderr\n", - "\n", - "from peft import get_peft_model_state_dict, set_peft_model_state_dict\n", - "\n", - "import bz2\n", - "import pickle\n", - "\n", - "logging.disable(logging.WARNING)\n", - "\n", - "ARC_VOCAB = {\n", - " \"0\": 0,\n", - " \"1\": 1,\n", - " \"2\": 2,\n", - " \"3\": 3,\n", - " \"4\": 4,\n", - " \"5\": 5,\n", - " \"6\": 6,\n", - " \"7\": 7,\n", - " \"8\": 8,\n", - " \"9\": 9,\n", - " \"Ċ\": 10,\n", - " \"<|im_end|>\": 15,\n", - "}\n", - "\n", - "ARC_TOKENS = list(ARC_VOCAB.values())\n", - "USER_TOKEN_ID = 11\n", - "ASSISTANT_TOKEN_ID = 12\n", - "PAD_ID = 13\n", - "EOS_ID = 15\n", - "\n", - "\n", - "class UnslothFixedTrainer(UnslothTrainer):\n", - "\n", - " # Issue https://github.com/unslothai/unsloth/issues/2435\n", - "\n", - " def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n", - " \"\"\"Fixed compute_loss that handles Unsloth's view tensor issue\"\"\"\n", - " if self.label_smoother is not None and \"labels\" in inputs:\n", - " labels = inputs.pop(\"labels\")\n", - " else:\n", - " labels = None\n", - " outputs = model(**inputs)\n", - " if labels is not None:\n", - " unwrapped_model = self.accelerator.unwrap_model(model)\n", - " if hasattr(unwrapped_model, \"_get_name\") and \"unsloth\" in unwrapped_model._get_name().lower():\n", - " loss = self.label_smoother(outputs, labels, shift_labels=True)\n", - " else:\n", - " loss = self.label_smoother(outputs, labels)\n", - " else:\n", - " loss = outputs[\"loss\"] if isinstance(outputs, dict) else outputs[0]\n", - " # šŸ”§ KEY FIX: Clone the loss tensor before in-place operations\n", - " if hasattr(loss, \"clone\"):\n", - " loss = loss.clone() # Converts view tensor to independent tensor\n", - " # Now safe for DDP gradient scaling\n", - " if self.accelerator.num_processes > 1:\n", - " loss = loss * self.accelerator.num_processes\n", - " return (loss, outputs) if return_outputs else loss\n", - "\n", - "\n", - "class QwenDataCollatorForCompletionOnlyLM(DataCollatorForLanguageModeling):\n", - "\n", - " def torch_call(self, examples: list[Union[list[int], Any, dict[str, Any]]]) -> dict[str, Any]:\n", - " batch = super().torch_call(examples)\n", - " for i in range(len(examples)):\n", - " labels = batch[\"input_ids\"][i].clone()\n", - " user_start_idx = np.where(labels == USER_TOKEN_ID)[0].tolist()\n", - " assistant_start_idx = np.where(labels == ASSISTANT_TOKEN_ID)[0].tolist()\n", - " start_idx = sorted(user_start_idx + assistant_start_idx)\n", - " end_idx = np.where(labels == EOS_ID)[0]\n", - " batch[\"labels\"][i, :] = -100\n", - " for j, (start, end) in enumerate(zip(start_idx, end_idx)):\n", - " assert start < end\n", - " if j % 2 == 1:\n", - " start += 2\n", - " end += 1\n", - " batch[\"labels\"][i, start:end] = labels[start:end]\n", - " return batch\n", - "\n", - "\n", - "def turbo_dfs(model, logits, max_new_tokens, max_score, scores, pos, cache, start_time, end_time) -> dict:\n", - "\n", - " n = logits.size(0)\n", - "\n", - " nll = torch.tensor(scores, dtype=torch.float32).view(n, 1) - logits.float().cpu().log_softmax(-1)\n", - "\n", - " suffixes = defaultdict(list)\n", - "\n", - " candidates = dict()\n", - "\n", - " for i in range(n):\n", - " candidates[i] = []\n", - " for t in ARC_TOKENS:\n", - " score = nll[i, t].item()\n", - " if score < max_score:\n", - " if t == EOS_ID:\n", - " suffixes[i].append((score, [t]))\n", - " elif max_new_tokens > 1:\n", - " candidates[i].append((score, t))\n", - "\n", - " for i in range(n):\n", - " candidates[i] = sorted(candidates[i], key=lambda x:x[0]) #[:5]\n", - " \n", - " while time.time() - start_time < 540 and time.time() < end_time:\n", - "\n", - " batch_tokens = []\n", - " batch_scores = []\n", - " num_alive_beams = 0\n", - "\n", - " for i in range(n):\n", - " if len(candidates[i]) == 0:\n", - " batch_tokens.append(PAD_ID)\n", - " batch_scores.append(1000)\n", - " else:\n", - " score, t = candidates[i].pop(0)\n", - " batch_tokens.append(t)\n", - " batch_scores.append(score)\n", - " num_alive_beams += 1\n", - "\n", - " if num_alive_beams == 0:\n", - " break\n", - "\n", - " outputs = model(\n", - " input_ids=torch.tensor(batch_tokens, device=model.device, dtype=torch.long).view(-1, 1),\n", - " position_ids=torch.full((n, 1), pos, device=model.device),\n", - " past_key_values=cache,\n", - " return_dict=True,\n", - " use_cache=True,\n", - " )\n", - "\n", - " next_suffixes = turbo_dfs(\n", - " model,\n", - " logits=outputs.logits[:, -1],\n", - " max_new_tokens=max_new_tokens-1,\n", - " max_score=max_score,\n", - " scores=batch_scores,\n", - " pos=pos+1,\n", - " cache=outputs.past_key_values,\n", - " start_time=start_time,\n", - " end_time=end_time,\n", - " )\n", - "\n", - " for batch_id, beams in next_suffixes.items():\n", - " for score, suffix_tokens in beams:\n", - " suffix_tokens.insert(0, batch_tokens[batch_id])\n", - " suffixes[batch_id].append((score, suffix_tokens))\n", - "\n", - " return suffixes\n", - "\n", - "\n", - "@torch.no_grad()\n", - "def inference_turbo_dfs(model, prefix_tokens, max_new_tokens, max_score, end_time):\n", - " input_ids = torch.tensor(prefix_tokens, device=model.device, dtype=torch.long)\n", - " outputs = model(input_ids=input_ids, return_dict=True, use_cache=True)\n", - " suffixes = turbo_dfs(\n", - " model,\n", - " logits=outputs.logits[:, -1],\n", - " max_new_tokens=max_new_tokens,\n", - " max_score=max_score,\n", - " scores=[0.0] * input_ids.size(0),\n", - " pos=input_ids.size(1),\n", - " cache=outputs.past_key_values,\n", - " start_time=time.time(),\n", - " end_time=end_time,\n", - " )\n", - " result = []\n", - " for batch_id, beams in suffixes.items():\n", - " sorted_beams = sorted(beams, key=lambda x:x[0])\n", - " result.append((batch_id, sorted_beams))\n", - " return result\n", - "\n", - "\n", - "@torch.no_grad()\n", - "def calc_scores(queries, answers, tokenizer, model):\n", - " batch_query_tokens = []\n", - " batch_answer_tokens = []\n", - " batch_tokens = []\n", - " batch_lengths = []\n", - " for query, answer in zip(queries, answers):\n", - " query_tokens = tokenizer.encode(query)\n", - " answer_tokens = tokenizer.encode(answer)\n", - " tokens = query_tokens + answer_tokens\n", - " batch_query_tokens.append(query_tokens)\n", - " batch_answer_tokens.append(answer_tokens)\n", - " batch_tokens.append(tokens)\n", - " batch_lengths.append(len(tokens))\n", - " max_len = max(batch_lengths)\n", - " padded_tokens = []\n", - " for tokens in batch_tokens:\n", - " padded = tokens + [PAD_ID] * (max_len - len(tokens))\n", - " padded_tokens.append(padded)\n", - " input_ids = torch.tensor(padded_tokens, device=model.device, dtype=torch.long)\n", - " outputs = model(input_ids=input_ids, return_dict=True, use_cache=True)\n", - " batch_logits = outputs.logits.float().cpu().log_softmax(-1)\n", - " result = []\n", - " for logits, query_tokens, answer_tokens in zip(batch_logits, batch_query_tokens, batch_answer_tokens):\n", - " query_length = len(query_tokens)\n", - " answer_logits = logits[query_length-1:query_length-1+len(answer_tokens)]\n", - " answer_score = answer_logits[torch.arange(len(answer_tokens)), answer_tokens].sum()\n", - " result.append(-answer_score.item())\n", - " return result\n", - "\n", - "\n", - "def worker(rank, queue, end_time):\n", - "\n", - " rerun_mode = True #os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\")\n", - "\n", - " peft_params = dict(\n", - " r=256,\n", - " target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\", \"embed_tokens\", \"lm_head\"],\n", - " lora_alpha=32,\n", - " lora_dropout=0.0,\n", - " bias=\"none\",\n", - " use_gradient_checkpointing=False,\n", - " random_state=42,\n", - " use_rslora=True,\n", - " loftq_config=None,\n", - " )\n", - "\n", - " train_args = dict(\n", - " per_device_eval_batch_size=1,\n", - " per_device_train_batch_size=1,\n", - " gradient_accumulation_steps=1,\n", - " num_train_epochs=1,\n", - " warmup_steps=0,\n", - " warmup_ratio=0.1,\n", - " max_grad_norm=1.0,\n", - " learning_rate=5e-5,\n", - " optim=\"adamw_torch\",\n", - " weight_decay=0.0,\n", - " lr_scheduler_type=\"cosine\",\n", - " seed=42,\n", - " report_to=\"none\",\n", - " save_strategy=\"no\",\n", - " eval_strategy=\"no\",\n", - " logging_strategy=\"no\",\n", - " fp16=False,\n", - " bf16=True,\n", - " # Disable FSDP (use standard DDP)\n", - " fsdp=\"\",\n", - " ddp_find_unused_parameters=False,\n", - " dataloader_num_workers=0,\n", - " gradient_checkpointing=False,\n", - " )\n", - "\n", - " max_seq_length = 8192\n", - "\n", - " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name=\"../input/qwen3_4b_grids15_sft139/\",\n", - " full_finetuning=False,\n", - " load_in_4bit=False,\n", - " local_files_only=True,\n", - " use_gradient_checkpointing=False,\n", - " max_seq_length=max_seq_length,\n", - " )\n", - "\n", - " model = FastLanguageModel.get_peft_model(model, **peft_params)\n", - "\n", - " for name, param in model.named_parameters():\n", - " if param.dtype == torch.float32:\n", - " param.data = param.data.to(torch.bfloat16)\n", - "\n", - " default_weights = get_peft_model_state_dict(model, adapter_name=\"default\")\n", - " default_weights = {k: v.clone().detach() for k, v in default_weights.items()}\n", - "\n", - " collator = QwenDataCollatorForCompletionOnlyLM(\n", - " tokenizer=tokenizer,\n", - " mlm=False,\n", - " )\n", - "\n", - " formatter = QwenFormatter(tokenizer=tokenizer)\n", - "\n", - " max_new_tokens = formatter.max_new_tokens()\n", - "\n", - " max_score = -np.log(0.2)\n", - "\n", - " if rerun_mode:\n", - " test_path = \"../input/arc-prize-2024/arc-agi_evaluation_challenges.json\"\n", - " else:\n", - " test_path = \"../input/arc-prize-2024/arc-agi_evaluation_challenges.json\"\n", - "\n", - " arc_test_set = ArcDataset.from_file(test_path)\n", - "\n", - " dir_outputs = \"../inference_outputs\"\n", - " os.makedirs(dir_outputs, exist_ok=True)\n", - "\n", - " while not queue.empty():\n", - "\n", - " if time.time() > end_time:\n", - " print(f\"[Rank {rank}] stop!\")\n", - " break\n", - "\n", - " key = queue.get()\n", - " if key is None:\n", - " break\n", - " \n", - " start_time = time.time()\n", - " \n", - " torch.cuda.reset_peak_memory_stats()\n", - "\n", - " load_result = set_peft_model_state_dict(\n", - " model,\n", - " default_weights.copy(),\n", - " adapter_name=\"default\",\n", - " )\n", - "\n", - " model = FastLanguageModel.for_training(model)\n", - "\n", - " puzzle_ds = arc_test_set.change_keys([key])\n", - "\n", - " train_ds = puzzle_ds.augment(n=16, shfl_keys=True, seed=1)\n", - " train_ds = train_ds.cut_to_len(formatter=formatter, name=\"text\", max_len=max_seq_length)\n", - "\n", - " with io.StringIO() as buf, redirect_stdout(buf), redirect_stderr(buf):\n", - " \n", - " trainer = UnslothFixedTrainer(\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " data_collator=collator,\n", - " train_dataset=Dataset.from_list(train_ds.as_list(formatter)),\n", - " dataset_text_field=\"text\",\n", - " max_seq_length=max_seq_length,\n", - " args=UnslothTrainingArguments(**train_args),\n", - " )\n", - "\n", - " stats = trainer.train()\n", - "\n", - " model = trainer.accelerator.unwrap_model(model, keep_fp32_wrapper=False)\n", - "\n", - " del trainer\n", - "\n", - " model = FastLanguageModel.for_inference(model)\n", - " \n", - " gc.collect()\n", - " torch.cuda.empty_cache()\n", - " \n", - " memory_allocated = torch.cuda.max_memory_allocated() // 1024**2\n", - " print(f\"[Rank {rank}] allocated {memory_allocated}MB for training\")\n", - "\n", - " torch.cuda.reset_peak_memory_stats()\n", - " \n", - " print(f\"[Rank {rank}] training stats for puzzle {key}: {stats}\")\n", - "\n", - " puzzle_ds_multi = puzzle_ds.split_multi_replies()\n", - "\n", - " eval_ds = puzzle_ds_multi.augment(n=2, seed=2)\n", - " eval_ds = eval_ds.cut_to_len(formatter=formatter, name=\"input\", max_len=max_seq_length-max_new_tokens)\n", - "\n", - " test_id_to_subkeys = defaultdict(list)\n", - " for subkey in sorted(eval_ds.keys):\n", - " test_id = subkey.split(\".\")[0].split(\"_\")[1]\n", - " test_id_to_subkeys[test_id].append(subkey)\n", - "\n", - " batches = []\n", - " for test_id, subkeys in test_id_to_subkeys.items():\n", - " # 0: permute x 2\n", - " # 4: rot90.rot90.permute x 2\n", - " batch = []\n", - " for offset in [0, 4]:\n", - " batch.extend(subkeys[offset:offset+2])\n", - " batches.append(batch)\n", - " # 2: permute.rot90 x 2\n", - " # 6: rot90.rot90.rot90.permute x 2\n", - " batch = []\n", - " for offset in [2, 6]:\n", - " batch.extend(subkeys[offset:offset+2])\n", - " batches.append(batch)\n", - " for test_id, subkeys in test_id_to_subkeys.items():\n", - " # 8: transpose.permute x 2\n", - " # 12: transpose.rot90.rot90.permute x 2\n", - " batch = []\n", - " for offset in [8, 12]:\n", - " batch.extend(subkeys[offset:offset+2])\n", - " batches.append(batch)\n", - " # 10: transpose.rot90.permute x 2\n", - " # 14: transpose.rot90.rot90.rot90.permute x 2\n", - " batch = []\n", - " for offset in [10, 14]:\n", - " batch.extend(subkeys[offset:offset+2])\n", - " batches.append(batch)\n", - "\n", - " with torch.inference_mode():\n", - " \n", - " known_scores = {}\n", - "\n", - " for subkeys in batches:\n", - "\n", - " spend_time = time.time() - start_time\n", - " if spend_time > 1200 or time.time() > end_time:\n", - " print(f\"[Rank {rank}] timeout after {spend_time:.1f}s for puzzle {key}\")\n", - " break\n", - "\n", - " print(f\"[Rank {rank}] decoding {subkeys}\")\n", - "\n", - " tokens = []\n", - " for subkey in subkeys:\n", - " data = eval_ds.get(subkey, formatter)\n", - " tokens.append(tokenizer.encode(data[\"input\"]))\n", - "\n", - " dfs_result = inference_turbo_dfs(model, tokens, max_new_tokens, max_score, end_time)\n", - "\n", - " for subkey_id, scored_beams in dfs_result:\n", - "\n", - " subkey = subkeys[subkey_id]\n", - " bk = subkey.split(\".\")[0]\n", - " decoded_result = []\n", - "\n", - " for beam_score, tokens in scored_beams:\n", - "\n", - " array = formatter.convert_tokens_to_array(tokens)\n", - " if array is None:\n", - " continue\n", - "\n", - " solution = puzzle_ds_multi.invert_mod(array, subkey, inv_perm=True)\n", - "\n", - " grid_id = (bk, tuple(map(tuple, solution)))\n", - "\n", - " if grid_id in known_scores:\n", - " augmented_scores = known_scores[grid_id]\n", - " else:\n", - " print(f\"[Rank {rank}] scoring {subkey} #{len(decoded_result)}\")\n", - " aug_dataset = ArcDataset(\n", - " keys=[bk],\n", - " queries={bk: puzzle_ds_multi.queries.get(bk)},\n", - " replies={bk: [solution.tolist()]},\n", - " )\n", - " aug_dataset = aug_dataset.augment(seed=hash(bk) % 1024**2)\n", - " aug_dataset = aug_dataset.cut_to_len(formatter=formatter, name=\"input\", max_len=max_seq_length-max_new_tokens)\n", - " aug_queries = []\n", - " aug_answers = []\n", - " for augmented_sample in aug_dataset.as_list(formatter):\n", - " aug_queries.append(augmented_sample[\"input\"])\n", - " aug_answers.append(augmented_sample[\"reply\"])\n", - " augmented_scores1 = calc_scores(aug_queries[:4], aug_answers[:4], tokenizer, model)\n", - " augmented_scores2 = calc_scores(aug_queries[4:], aug_answers[4:], tokenizer, model)\n", - " augmented_scores = augmented_scores1 + augmented_scores2\n", - " known_scores[grid_id] = augmented_scores\n", - " \n", - " decoded_result.append({\n", - " \"beam_score\": beam_score,\n", - " \"score_aug\": augmented_scores,\n", - " \"solution\": solution,\n", - " })\n", - "\n", - " if len(decoded_result):\n", - " with bz2.BZ2File(os.path.join(dir_outputs, subkey), \"w\") as f:\n", - " pickle.dump(decoded_result, f)\n", - "\n", - " memory_allocated = torch.cuda.max_memory_allocated() // 1024**2\n", - " print(f\"[Rank {rank}] allocated {memory_allocated}MB for inference\")\n", - " \n", - " spend_time = time.time() - start_time\n", - " print(f\"[Rank {rank}] finished {key} in {spend_time:.1f}s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "247b67ff", - "metadata": { - "_kg_hide-input": true, - "papermill": { - "duration": 0.007926, - "end_time": "2025-11-13T03:54:25.759474", - "exception": false, - "start_time": "2025-11-13T03:54:25.751548", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Writing starter.py\n" - ] - } - ], - "source": [ - "%%writefile starter.py\n", - "import os\n", - "import time\n", - "import json\n", - "import torch\n", - "import argparse\n", - "import torch.multiprocessing as mp\n", - "\n", - "\n", - "def local_worker(rank, queue, end_time):\n", - " \n", - " os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(rank)\n", - "\n", - " torch.set_default_device(\"cpu\")\n", - "\n", - " # Fix Unsloth patching issue\n", - " if rank > 0:\n", - " while not os.path.exists(f\"../worker{rank-1}\"):\n", - " time.sleep(5)\n", - " \n", - " from arc_solver import worker\n", - "\n", - " with open(f\"../worker{rank}\", \"w\") as f:\n", - " f.write(\"Ok\")\n", - " \n", - " print(f\"[Rank {rank}] start!\")\n", - " \n", - " worker(rank, queue, end_time)\n", - " \n", - " print(f\"[Rank {rank}] done!\")\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - "\n", - " parser = argparse.ArgumentParser()\n", - " parser.add_argument(\"--end-time\", type=float, default=0.0)\n", - " args = parser.parse_args()\n", - "\n", - " rerun_mode = True #os.getenv(\"KAGGLE_IS_COMPETITION_RERUN\")\n", - "\n", - " if rerun_mode:\n", - " test_path = \"../input/arc-prize-2024/arc-agi_evaluation_challenges.json\"\n", - " else:\n", - " test_path = \"../input/arc-prize-2024/arc-agi_evaluation_challenges.json\"\n", - "\n", - " with open(test_path, \"r\") as f:\n", - " data = json.load(f)\n", - "\n", - " queue = mp.Manager().Queue()\n", - "\n", - " for key in sorted(data.keys()):\n", - " if not rerun_mode:\n", - " if key not in [\"0934a4d8\", \"36a08778\", \"981571dc\", \"aa4ec2a5\"]:\n", - " continue\n", - " queue.put(key)\n", - " for _ in range(4):\n", - " queue.put(None)\n", - " \n", - " mp.spawn(local_worker, args=(queue, args.end_time), nprocs=4)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "080de013", - "metadata": { - "_kg_hide-output": true, - "papermill": { - "duration": 1512.984849, - "end_time": "2025-11-13T04:19:38.747170", - "exception": false, - "start_time": "2025-11-13T03:54:25.762321", - "status": "completed" - }, - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "šŸ¦„ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", - "/usr/local/lib/python3.11/dist-packages/pydantic/_internal/_generate_schema.py:2225: UnsupportedFieldAttributeWarning: The 'repr' attribute with value False was provided to the `Field()` function, which has no effect in the context it was used. 'repr' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.11/dist-packages/pydantic/_internal/_generate_schema.py:2225: UnsupportedFieldAttributeWarning: The 'frozen' attribute with value True was provided to the `Field()` function, which has no effect in the context it was used. 'frozen' is field-specific metadata, and can only be attached to a model field using `Annotated` metadata or by assignment. This may have happened because an `Annotated` type alias using the `type` statement was used, or if the `Field()` function was attached to a single member of a union type.\n", - " warnings.warn(\n", - "šŸ¦„ Unsloth Zoo will now patch everything to make training faster!\n", - "[Rank 0] start!\n", - "==((====))== Unsloth 2025.9.7: Fast Qwen3 patching. Transformers: 4.55.4.\n", - " \\\\ /| NVIDIA H100 80GB HBM3. Num GPUs = 1. Max memory: 79.19 GB. Platform: Linux.\n", - "O^O/ \\_/ \\ Torch: 2.9.1+cu128. CUDA: 9.0. CUDA Toolkit: 12.8. Triton: 3.5.1\n", - "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.33.post2. FA2 = True]\n", - " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", - "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", - "Loading checkpoint shards: 0%| | 0/2 [00:00=2.4.0 in /usr/local/lib/python3.11/dist-packages (from unsloth==2025.9.7) (2.6.0+cu124)\n", - "Collecting xformers>=0.0.27.post2 (from unsloth==2025.9.7)\n", - " Downloading xformers-0.0.33.post2-cp39-abi3-manylinux_2_28_x86_64.whl.metadata (1.2 kB)\n", - "Collecting bitsandbytes (from unsloth==2025.9.7)\n", - " Downloading bitsandbytes-0.48.2-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n", - "Requirement already satisfied: triton>=3.0.0 in /usr/local/lib/python3.11/dist-packages (from unsloth==2025.9.7) (3.2.0)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from unsloth==2025.9.7) (25.0)\n", - "Collecting tyro (from unsloth==2025.9.7)\n", - " Downloading tyro-1.0.0-py3-none-any.whl.metadata (12 kB)\n", - "Requirement already satisfied: transformers!=4.47.0,!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,<=4.55.4,>=4.51.3 in /usr/local/lib/python3.11/dist-packages (from unsloth==2025.9.7) (4.53.3)\n", - 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"\u001b[?25hDownloading tyro-1.0.0-py3-none-any.whl (179 kB)\n", - "\u001b[2K \u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m \u001b[32m179.6/179.6 kB\u001b[0m \u001b[31m43.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n", - "\u001b[2K \u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m31.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading fsspec-2025.3.0-py3-none-any.whl (193 kB)\n", - "\u001b[2K \u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m \u001b[32m193.6/193.6 kB\u001b[0m \u001b[31m51.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading sympy-1.14.0-py3-none-any.whl (6.3 MB)\n", - "\u001b[2K \u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m200.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: nvidia-cusparselt-cu12, triton, sympy, nvidia-nvtx-cu12, nvidia-nvshmem-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, msgspec, fsspec, dill, tyro, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, huggingface_hub, scikit-learn, nvidia-cusolver-cu12, matplotlib, transformers, torch, datasets, xformers, torchvision, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n", - " Attempting uninstall: nvidia-cusparselt-cu12\n", - " Found existing installation: nvidia-cusparselt-cu12 0.6.2\n", - " Uninstalling nvidia-cusparselt-cu12-0.6.2:\n", - " Successfully uninstalled nvidia-cusparselt-cu12-0.6.2\n", - " Attempting uninstall: triton\n", - " Found existing installation: triton 3.2.0\n", - " Uninstalling triton-3.2.0:\n", - " Successfully uninstalled triton-3.2.0\n", - " Attempting uninstall: sympy\n", - " Found existing installation: sympy 1.13.1\n", - " Uninstalling sympy-1.13.1:\n", - " Successfully uninstalled sympy-1.13.1\n", - " Attempting uninstall: nvidia-nvtx-cu12\n", - " Found existing installation: nvidia-nvtx-cu12 12.4.127\n", - " Uninstalling nvidia-nvtx-cu12-12.4.127:\n", - " Successfully uninstalled nvidia-nvtx-cu12-12.4.127\n", - " Attempting uninstall: nvidia-nvjitlink-cu12\n", - " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", - " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", - " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", - " Attempting uninstall: nvidia-nccl-cu12\n", - " Found existing installation: nvidia-nccl-cu12 2.21.5\n", - " Uninstalling nvidia-nccl-cu12-2.21.5:\n", - " Successfully uninstalled nvidia-nccl-cu12-2.21.5\n", - " Attempting uninstall: nvidia-curand-cu12\n", - " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", - " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", - " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", - " Attempting uninstall: nvidia-cuda-runtime-cu12\n", - " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", - " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", - " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", - " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", - " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", - " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", - " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", - " Attempting uninstall: nvidia-cuda-cupti-cu12\n", - " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", - " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", - " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", - " Attempting uninstall: nvidia-cublas-cu12\n", - " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", - " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", - " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", - " Attempting uninstall: numpy\n", - " Found existing installation: numpy 1.26.4\n", - " Uninstalling numpy-1.26.4:\n", - " Successfully uninstalled numpy-1.26.4\n", - " Attempting uninstall: fsspec\n", - " Found existing installation: fsspec 2025.9.0\n", - " Uninstalling fsspec-2025.9.0:\n", - " Successfully uninstalled fsspec-2025.9.0\n", - " Attempting uninstall: dill\n", - " Found existing installation: dill 0.4.0\n", - " Uninstalling dill-0.4.0:\n", - " Successfully uninstalled dill-0.4.0\n", - " Attempting uninstall: nvidia-cusparse-cu12\n", - " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", - " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", - " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", - " Attempting uninstall: nvidia-cufft-cu12\n", - " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", - " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", - " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", - " Attempting uninstall: nvidia-cudnn-cu12\n", - " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", - " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", - " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", - " Attempting uninstall: huggingface_hub\n", - " Found existing installation: huggingface-hub 1.0.0rc2\n", - " Uninstalling huggingface-hub-1.0.0rc2:\n", - " Successfully uninstalled huggingface-hub-1.0.0rc2\n", - " Attempting uninstall: scikit-learn\n", - " Found existing installation: scikit-learn 1.2.2\n", - " Uninstalling scikit-learn-1.2.2:\n", - " Successfully uninstalled scikit-learn-1.2.2\n", - " Attempting uninstall: nvidia-cusolver-cu12\n", - " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", - " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", - " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", - " Attempting uninstall: matplotlib\n", - " Found existing installation: matplotlib 3.7.2\n", - " Uninstalling matplotlib-3.7.2:\n", - " Successfully uninstalled matplotlib-3.7.2\n", - " Attempting uninstall: transformers\n", - " Found existing installation: transformers 4.53.3\n", - " Uninstalling transformers-4.53.3:\n", - " Successfully uninstalled transformers-4.53.3\n", - " Attempting uninstall: torch\n", - " Found existing installation: torch 2.6.0+cu124\n", - " Uninstalling torch-2.6.0+cu124:\n", - " Successfully uninstalled torch-2.6.0+cu124\n", - " Attempting uninstall: datasets\n", - " Found existing installation: datasets 4.1.1\n", - " Uninstalling datasets-4.1.1:\n", - " Successfully uninstalled datasets-4.1.1\n", - " Attempting uninstall: torchvision\n", - " Found existing installation: torchvision 0.21.0+cu124\n", - " Uninstalling torchvision-0.21.0+cu124:\n", - " Successfully uninstalled torchvision-0.21.0+cu124\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "bigframes 2.12.0 requires google-cloud-bigquery-storage<3.0.0,>=2.30.0, which is not installed.\n", - "gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.2.6 which is incompatible.\n", - "gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.15.3 which is incompatible.\n", - "mkl-random 1.2.4 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.2.6 which is incompatible.\n", - "mkl-fft 1.3.8 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.2.6 which is incompatible.\n", - "mkl-umath 0.1.1 requires numpy<1.27.0,>=1.26.4, but you have numpy 2.2.6 which is incompatible.\n", - "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.2.6 which is incompatible.\n", - "fastai 2.8.4 requires torch<2.9,>=1.10, but you have torch 2.9.1 which is incompatible.\n", - "ydata-profiling 4.17.0 requires matplotlib<=3.10,>=3.5, but you have matplotlib 3.10.6 which is incompatible.\n", - "ydata-profiling 4.17.0 requires numpy<2.2,>=1.16.0, but you have numpy 2.2.6 which is incompatible.\n", - "onnx 1.18.0 requires protobuf>=4.25.1, but you have protobuf 3.20.3 which is incompatible.\n", - "category-encoders 2.7.0 requires scikit-learn<1.6.0,>=1.0.0, but you have scikit-learn 1.7.2 which is incompatible.\n", - "google-colab 1.0.0 requires google-auth==2.38.0, but you have google-auth 2.40.3 which is incompatible.\n", - "google-colab 1.0.0 requires notebook==6.5.7, but you have notebook 6.5.4 which is incompatible.\n", - "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\n", - "google-colab 1.0.0 requires requests==2.32.3, but you have requests 2.32.5 which is incompatible.\n", - "google-colab 1.0.0 requires tornado==6.4.2, but you have tornado 6.5.2 which is incompatible.\n", - "pylibcugraph-cu12 25.6.0 requires pylibraft-cu12==25.6.*, but you have pylibraft-cu12 25.2.0 which is incompatible.\n", - "pylibcugraph-cu12 25.6.0 requires rmm-cu12==25.6.*, but you have rmm-cu12 25.2.0 which is incompatible.\n", - "sklearn-compat 0.1.3 requires scikit-learn<1.7,>=1.2, but you have scikit-learn 1.7.2 which is incompatible.\n", - "cudf-polars-cu12 25.6.0 requires pylibcudf-cu12==25.6.*, but you have pylibcudf-cu12 25.2.2 which is incompatible.\n", - "gradio 5.38.1 requires pydantic<2.12,>=2.0, but you have pydantic 2.12.0a1 which is incompatible.\n", - "pandas-gbq 0.29.2 requires google-api-core<3.0.0,>=2.10.2, but you have google-api-core 1.34.1 which is incompatible.\n", - "dopamine-rl 4.1.2 requires gymnasium>=1.0.0, but you have gymnasium 0.29.0 which is incompatible.\n", - "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.2.6 which is incompatible.\n", - "libcugraph-cu12 25.6.0 requires libraft-cu12==25.6.*, but you have libraft-cu12 25.2.0 which is incompatible.\n", - "torchaudio 2.6.0+cu124 requires torch==2.6.0, but you have torch 2.9.1 which is incompatible.\n", - "bigframes 2.12.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.31.0, but you have google-cloud-bigquery 3.25.0 which is incompatible.\n", - "bigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.1.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed bitsandbytes-0.48.2 cut_cross_entropy-25.1.1 datasets-3.6.0 dill-0.3.8 fsspec-2025.3.0 huggingface_hub-0.36.0 matplotlib-3.10.6 msgspec-0.20.0 numpy-2.2.6 nvidia-cublas-cu12-12.8.4.1 nvidia-cuda-cupti-cu12-12.8.90 nvidia-cuda-nvrtc-cu12-12.8.93 nvidia-cuda-runtime-cu12-12.8.90 nvidia-cudnn-cu12-9.10.2.21 nvidia-cufft-cu12-11.3.3.83 nvidia-cufile-cu12-1.13.1.3 nvidia-curand-cu12-10.3.9.90 nvidia-cusolver-cu12-11.7.3.90 nvidia-cusparse-cu12-12.5.8.93 nvidia-cusparselt-cu12-0.7.1 nvidia-nccl-cu12-2.27.5 nvidia-nvjitlink-cu12-12.8.93 nvidia-nvshmem-cu12-3.3.20 nvidia-nvtx-cu12-12.8.90 scikit-learn-1.7.2 sympy-1.14.0 torch-2.9.1 torchvision-0.24.1 transformers-4.55.4 triton-3.5.1 trl-0.22.2 tyro-1.0.0 unsloth-2025.9.7 unsloth_zoo-2025.9.9 xformers-0.0.33.post2\n" - ] - } - ], - "source": [ - "!pip install unsloth==2025.9.7 unsloth_zoo==2025.9.9 numpy==2.2.6 matplotlib==3.10.6 scikit-learn==1.7.2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "591c8f2d", - "metadata": { - "papermill": { - "duration": 21.875783, - "end_time": "2025-11-01T12:51:27.791861", - "exception": false, - "start_time": "2025-11-01T12:51:05.916078", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting flash-attn\n", - " Downloading flash_attn-2.8.3.tar.gz (8.4 MB)\n", - "\u001b[2K \u001b[90mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m81.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - "Building wheels for collected packages: flash-attn\n", - " Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for flash-attn: filename=flash_attn-2.8.3-cp311-cp311-linux_x86_64.whl size=116548888 sha256=2796491b466b0d1924beeca8b5a16891016187111557f7dd003d55267f75627e\n", - " Stored in directory: /home/runai-home/.cache/pip/wheels/42/31/1f/4b22dd7295b3cb064b8fa9038f6d58fb15c9571555b2d7c39c\n", - "Successfully built flash-attn\n", - "Installing collected packages: flash-attn\n", - "Successfully installed flash-attn-2.8.3\n" - ] - } - ], - "source": [ - "!pip install --upgrade --no-deps --no-build-isolation flash-attn " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "211f01fc", - "metadata": { - "papermill": { - "duration": 0.197604, - "end_time": "2025-11-01T12:51:28.061536", - "exception": false, - "start_time": "2025-11-01T12:51:27.863932", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.11.13\n" - ] - } - ], - "source": [ - "!python --version" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "261e158f", - "metadata": { - "papermill": { - "duration": 1.962132, - "end_time": "2025-11-01T12:51:30.096542", - "exception": false, - "start_time": "2025-11-01T12:51:28.134410", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.9.1+cu128'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import torch\n", - "torch.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "66cd4e7a", - "metadata": { - "papermill": { - "duration": 0.101689, - "end_time": "2025-11-01T12:51:30.267456", - "exception": false, - "start_time": "2025-11-01T12:51:30.165767", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.8.3'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import flash_attn\n", - "flash_attn.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5a4685c9", - "metadata": { - "_kg_hide-input": false, - "_kg_hide-output": true, - "papermill": { - "duration": 0.083658, - "end_time": "2025-11-01T12:51:30.430853", - "exception": false, - "start_time": "2025-11-01T12:51:30.347195", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "@@ -272,7 +277,7 @@\n", - " \n", - " # Mistral Nemo 12b has weird dimensions\n", - " if attention_size != hidden_size:\n", - "- self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)\n", - "+ self.temp_O = torch.empty((bsz, 1, hidden_size), dtype = dtype, device = device)\n", - " else:\n", - " self.temp_O = self.temp_QA[1][:,:,:hidden_size]\n", - " pass\n", - "@@ -333,52 +338,12 @@\n", - " Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)\n", - " Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)\n", - " \n", - "- # Handle sliding windows\n", - "- sliding_window = getattr(self.config, \"sliding_window\", None)\n", - "- if sliding_window is not None and kv_seq_len > sliding_window:\n", - "- # From https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L193\n", - "- slicing_tokens = 1 - sliding_window\n", - "- Knn = Kn[:, :, slicing_tokens:, :]#.contiguous()\n", - "- Vnn = Vn[:, :, slicing_tokens:, :]#.contiguous()\n", - "- else:\n", - "- Knn, Vnn = Kn, Vn\n", - "- pass\n", - "+ Qnn = Qn.transpose(1, 2)\n", - "+ Knn = Kn.transpose(1, 2)\n", - "+ Vnn = Vn.transpose(1, 2)\n", - " \n", - "- # when qlen==vlen and attn_mask is None, we should use causal attention\n", - "- Q_len = Qn.shape[-2]\n", - "- K_len = Knn.shape[-2]\n", - "- if attention_mask is None and Q_len == K_len:\n", - "- is_causal = True\n", - "- else:\n", - "- is_causal = False\n", - "+ A = flash_attn_func(Qnn, Knn, Vnn)\n", - " \n", - "- # Grouped query attention\n", - "- _, _, cached_len, _ = Knn.shape\n", - "- if bsz == 1 or not SDPA_HAS_GQA and n_groups != 1:\n", - "- Knn = Knn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)\n", - "- Vnn = Vnn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)\n", - "- Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)\n", - "- Vnn = Vnn.reshape(bsz, n_heads, cached_len, head_dim)\n", - "- pass\n", - "- # else:\n", - "- # Knn, Vnn = Knn, Vnn\n", - "- # pass\n", - "-\n", - "- # Attention\n", - "- if bsz == 1:\n", - "- Qn *= self.scalar # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963\n", - "- # It seems like doing (Q * scalar) @ K is better than (Q @ K) * scalar to stop overflows\n", - "- A = torch_matmul(Qn, Knn.transpose(2, 3), out = self.attention[:,:,:,:cached_len])\n", - "- # if attention_mask is not None: A += attention_mask # Must add attention_mask for batched\n", - "- A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)\n", - "- A = torch_matmul(A, Vnn, out = Qn)\n", - "- else:\n", - "- if SDPA_HAS_GQA:\n", - "- A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = is_causal, enable_gqa = True)\n", - "- else:\n", - "- A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = is_causal)\n", - "- pass\n", - "- A = A.transpose(1, 2)\n", - " A = A.reshape(bsz, 1, attention_size)\n", - " A = fast_linear_forward(self.o_proj, A, out = self.temp_O)\n", - " return A, (Kn, Vn)\n", - "\n" - ] - } - ], - "source": [ - "patch = b'@@ -272,7 +277,7 @@\\r\\n \\r\\n # Mistral Nemo 12b has weird dimensions\\r\\n if attention_size != hidden_size:\\r\\n- self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)\\r\\n+ self.temp_O = torch.empty((bsz, 1, hidden_size), dtype = dtype, device = device)\\r\\n else:\\r\\n self.temp_O = self.temp_QA[1][:,:,:hidden_size]\\r\\n pass\\r\\n@@ -333,52 +338,12 @@\\r\\n Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)\\r\\n Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)\\r\\n \\r\\n- # Handle sliding windows\\r\\n- sliding_window = getattr(self.config, \"sliding_window\", None)\\r\\n- if sliding_window is not None and kv_seq_len > sliding_window:\\r\\n- # From https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L193\\r\\n- slicing_tokens = 1 - sliding_window\\r\\n- Knn = Kn[:, :, slicing_tokens:, :]#.contiguous()\\r\\n- Vnn = Vn[:, :, slicing_tokens:, :]#.contiguous()\\r\\n- else:\\r\\n- Knn, Vnn = Kn, Vn\\r\\n- pass\\r\\n+ Qnn = Qn.transpose(1, 2)\\r\\n+ Knn = Kn.transpose(1, 2)\\r\\n+ Vnn = Vn.transpose(1, 2)\\r\\n \\r\\n- # when qlen==vlen and attn_mask is None, we should use causal attention\\r\\n- Q_len = Qn.shape[-2]\\r\\n- K_len = Knn.shape[-2]\\r\\n- if attention_mask is None and Q_len == K_len:\\r\\n- is_causal = True\\r\\n- else:\\r\\n- is_causal = False\\r\\n+ A = flash_attn_func(Qnn, Knn, Vnn)\\r\\n \\r\\n- # Grouped query attention\\r\\n- _, _, cached_len, _ = Knn.shape\\r\\n- if bsz == 1 or not SDPA_HAS_GQA and n_groups != 1:\\r\\n- Knn = Knn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)\\r\\n- Vnn = Vnn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)\\r\\n- Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)\\r\\n- Vnn = Vnn.reshape(bsz, n_heads, cached_len, head_dim)\\r\\n- pass\\r\\n- # else:\\r\\n- # Knn, Vnn = Knn, Vnn\\r\\n- # pass\\r\\n-\\r\\n- # Attention\\r\\n- if bsz == 1:\\r\\n- Qn *= self.scalar # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963\\r\\n- # It seems like doing (Q * scalar) @ K is better than (Q @ K) * scalar to stop overflows\\r\\n- A = torch_matmul(Qn, Knn.transpose(2, 3), out = self.attention[:,:,:,:cached_len])\\r\\n- # if attention_mask is not None: A += attention_mask # Must add attention_mask for batched\\r\\n- A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)\\r\\n- A = torch_matmul(A, Vnn, out = Qn)\\r\\n- else:\\r\\n- if SDPA_HAS_GQA:\\r\\n- A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = is_causal, enable_gqa = True)\\r\\n- else:\\r\\n- A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = is_causal)\\r\\n- pass\\r\\n- A = A.transpose(1, 2)\\r\\n A = A.reshape(bsz, 1, attention_size)\\r\\n A = fast_linear_forward(self.o_proj, A, out = self.temp_O)\\r\\n return A, (Kn, Vn)\\r\\n'\n", - "with open(\"qwen3.patch\", \"wb\") as f:\n", - " f.write(patch)\n", - "print(patch.decode())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "014766bf", - "metadata": { - "papermill": { - "duration": 0.208426, - "end_time": "2025-11-01T12:51:30.708214", - "exception": false, - "start_time": "2025-11-01T12:51:30.499788", - "status": "completed" - }, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "patching file /usr/local/lib/python3.11/dist-packages/unsloth/models/qwen3.py\n" - ] - } - ], - "source": [ - "!patch --binary /usr/local/lib/python3.11/dist-packages/unsloth/models/qwen3.py qwen3.patch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0bfe018-a8ff-4cb0-96dd-e5f216f1bcfc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kaggle": { - "accelerator": "nvidiaTeslaT4", - "dataSources": [], - "dockerImageVersionId": 31090, - "isGpuEnabled": true, - "isInternetEnabled": true, - "language": "python", - "sourceType": "notebook" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.13" - }, - "papermill": { - "default_parameters": {}, - "duration": 284.961026, - "end_time": "2025-11-01T12:51:31.498362", - "environment_variables": {}, - "exception": null, - "input_path": "__notebook__.ipynb", - "output_path": "__notebook__.ipynb", - "parameters": {}, - "start_time": "2025-11-01T12:46:46.537336", - "version": "2.6.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ARChitects/README.md b/ARChitects/README.md deleted file mode 100644 index c7fb0f4..0000000 --- a/ARChitects/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Supervised Fine-Tuning - -1. Assume we have the following subsets with augmented puzzles: - - arc2_training - - arc2_evaluation6 - - mini - - concept - - rearc - - nvarc_training - - nvarc_full -> **_NOTE:_** These augmented puzzles produced by [build_datasets.py](../SDG/scripts/build_datasets.py) script and also available on Kaggle [NVARC Augmented Puzzles](https://www.kaggle.com/datasets/sorokin/nvarc-augmented-puzzles) dataset. -2. Prepare Qwen3 tokenizer and model with 16 embeddings. -3. Execute `run_sft_4b.sh` script on Slurm cluster. \ No newline at end of file diff --git a/ARChitects/cut_tokenizer.ipynb b/ARChitects/cut_tokenizer.ipynb deleted file mode 100644 index 9b4246e..0000000 --- a/ARChitects/cut_tokenizer.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "b69e7d11", - "metadata": {}, - "outputs": [], - "source": [ - "from transformers import AutoTokenizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7724f185", - "metadata": {}, - "outputs": [], - "source": [ - "model_path = \"./models/Qwen3-4B-Thinking-2507\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6a4b19c", - "metadata": {}, - "outputs": [], - "source": [ - "tokenizer = AutoTokenizer.from_pretrained(model_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f96e1dc6", - "metadata": {}, - "outputs": [], - "source": [ - "with open(\"qwen3_configs/chat_template.j2\") as f:\n", - " chat_template = f.read()\n", - "tokenizer.chat_template = chat_template" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "accea9ab", - "metadata": {}, - "outputs": [], - "source": [ - "from datasets import load_from_disk\n", - "\n", - "ds = load_from_disk(\"data/grids_v15/arc2_evaluation6\")\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71ff4307", - "metadata": {}, - "outputs": [], - "source": [ - "def get_token_length(sample: dict):\n", - " text = tokenizer.apply_chat_template(\n", - " sample[\"messages\"],\n", - " tokenize=False,\n", - " )\n", - " tokens = tokenizer.encode(text + \"<|endoftext|>\")\n", - " sample[\"tokens\"] = tokens\n", - " return sample\n", - "\n", - "ds = ds.map(get_token_length, num_proc=8)\n", - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d3e5e79", - "metadata": {}, - "outputs": [], - "source": [ - "all_tokens = []\n", - "for sample in ds:\n", - " all_tokens.extend(sample[\"tokens\"])\n", - "all_tokens = set(all_tokens)\n", - "len(all_tokens)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eeaddea8", - "metadata": {}, - "outputs": [], - "source": [ - "mapping = []\n", - "new_vocab = dict()\n", - "for token in sorted(all_tokens):\n", - " v = tokenizer.convert_ids_to_tokens(token)\n", - " new_vocab[v] = len(mapping)\n", - " print(token, v)\n", - " mapping.append(token)\n", - "len(mapping)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53a19322", - "metadata": {}, - "outputs": [], - "source": [ - "repr(new_vocab)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e9299c61", - "metadata": {}, - "outputs": [], - "source": [ - "from safetensors import safe_open\n", - "\n", - "path_file = f\"{model_path}/model-00001-of-00003.safetensors\"\n", - "\n", - "tensors = {}\n", - "with safe_open(path_file, framework=\"pt\", device=\"cpu\") as f:\n", - " for key in f.keys():\n", - " tensors[key] = f.get_tensor(key)\n", - "\n", - "embed_tokens_weight = tensors[\"model.embed_tokens.weight\"]\n", - "# embed_tokens_weight = tensors[\"lm_head.weight\"]\n", - "\n", - "print(embed_tokens_weight.shape, embed_tokens_weight.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4e1fd11", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "\n", - "padded_mapping = mapping #+ [151643] * 20\n", - "\n", - "row_select = torch.tensor(padded_mapping)\n", - "\n", - "selected_embedding = torch.index_select(embed_tokens_weight, 0, row_select)\n", - "print(selected_embedding.shape, selected_embedding.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "601448be", - "metadata": {}, - "outputs": [], - "source": [ - "from safetensors.torch import save_file\n", - "\n", - "tensors[\"model.embed_tokens.weight\"] = selected_embedding\n", - "# tensors[\"lm_head.weight\"] = selected_embedding.clone()\n", - "\n", - "output_path = model_path + \"-16t\"\n", - "\n", - "save_file(tensors, f\"{output_path}/model-00001-of-00003.safetensors\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/ARChitects/qwen3_configs/added_tokens.json b/ARChitects/qwen3_configs/added_tokens.json deleted file mode 100644 index 40c42f5..0000000 --- a/ARChitects/qwen3_configs/added_tokens.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "<|endoftext|>": 13, - "<|im_start|>": 14, - "<|im_end|>": 15 -} diff --git a/ARChitects/qwen3_configs/chat_template.j2 b/ARChitects/qwen3_configs/chat_template.j2 deleted file mode 100644 index 1c15a34..0000000 --- a/ARChitects/qwen3_configs/chat_template.j2 +++ /dev/null @@ -1,6 +0,0 @@ -{%- for message in messages %} - {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' }} -{%- endfor %} -{%- if add_generation_prompt %} - {{- '<|im_start|>assistant\n' }} -{%- endif %} \ No newline at end of file diff --git a/ARChitects/qwen3_configs/special_tokens_map.json b/ARChitects/qwen3_configs/special_tokens_map.json deleted file mode 100644 index 2c53454..0000000 --- a/ARChitects/qwen3_configs/special_tokens_map.json +++ /dev/null @@ -1,20 +0,0 @@ -{ - "additional_special_tokens": [ - "<|im_start|>", - "<|im_end|>" - ], - "eos_token": { - "content": "<|im_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - }, - "pad_token": { - "content": "<|endoftext|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false - } -} diff --git a/ARChitects/qwen3_configs/tokenizer.json b/ARChitects/qwen3_configs/tokenizer.json deleted file mode 100644 index f99f90f..0000000 --- a/ARChitects/qwen3_configs/tokenizer.json +++ /dev/null @@ -1,87 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 13, - "content": "<|endoftext|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 14, - "content": "<|im_start|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 15, - "content": "<|im_end|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - } - ], - "normalizer": { - "type": "NFC" - }, - "pre_tokenizer": { - "type": "Sequence", - "pretokenizers": [ - { - "type": "Split", - "pattern": { - "Regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" - }, - "behavior": "Isolated", - "invert": false - }, - { - "type": "ByteLevel", - "add_prefix_space": false, - "trim_offsets": false, - "use_regex": false - } - ] - }, - "post_processor": { - "type": "ByteLevel", - "add_prefix_space": false, - "trim_offsets": false, - "use_regex": false - }, - "decoder": { - "type": "ByteLevel", - "add_prefix_space": false, - "trim_offsets": false, - "use_regex": false - }, - "model": { - "type": "WordLevel", - "unk_token": "<|endoftext|>", - "vocab": { - "0": 0, - "1": 1, - "2": 2, - "3": 3, - "4": 4, - "5": 5, - "6": 6, - "7": 7, - "8": 8, - "9": 9, - "Ċ": 10, - "user": 11, - "assistant": 12 - } - } -} \ No newline at end of file diff --git a/ARChitects/qwen3_configs/tokenizer_config.json b/ARChitects/qwen3_configs/tokenizer_config.json deleted file mode 100644 index 54990ff..0000000 --- a/ARChitects/qwen3_configs/tokenizer_config.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "add_bos_token": false, - "add_prefix_space": false, - "added_tokens_decoder": { - "13": { - "content": "<|endoftext|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "14": { - "content": "<|im_start|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - }, - "15": { - "content": "<|im_end|>", - "lstrip": false, - "normalized": false, - "rstrip": false, - "single_word": false, - "special": true - } - }, - "additional_special_tokens": [ - "<|im_start|>", - "<|im_end|>" - ], - "bos_token": null, - "clean_up_tokenization_spaces": false, - "eos_token": "<|im_end|>", - "errors": "replace", - "extra_special_tokens": {}, - "model_max_length": 262144, - "pad_token": "<|endoftext|>", - "split_special_tokens": false, - "tokenizer_class": "Qwen2Tokenizer", - "unk_token": null -} diff --git a/ARChitects/qwen3_configs/vocab.json b/ARChitects/qwen3_configs/vocab.json deleted file mode 100644 index 8641f1f..0000000 --- a/ARChitects/qwen3_configs/vocab.json +++ /dev/null @@ -1 +0,0 @@ -{"0": 0, "1": 1, "2": 2, "3": 3, "4": 4, "5": 5, "6": 6, "7": 7, "8": 8, "9": 9, "Ċ": 10, "user": 11, "assistant": 12} \ No newline at end of file diff --git a/ARChitects/run_sft.py b/ARChitects/run_sft.py deleted file mode 100644 index 3bfd4b3..0000000 --- a/ARChitects/run_sft.py +++ /dev/null @@ -1,158 +0,0 @@ -import argparse -import pprint -from typing import Any - -from omegaconf import OmegaConf - -from nemo_rl.algorithms.sft import MasterConfig, setup, sft_train -from nemo_rl.algorithms.utils import get_tokenizer -from nemo_rl.data.datasets import AllTaskProcessedDataset -from nemo_rl.data.interfaces import DatumSpec, TaskDataSpec -from nemo_rl.data.llm_message_utils import get_formatted_message_log -from nemo_rl.distributed.virtual_cluster import init_ray -from nemo_rl.utils.config import load_config, parse_hydra_overrides -from nemo_rl.utils.logger import get_next_experiment_dir - -from datasets import load_from_disk, concatenate_datasets - - -PRINTED_SAMPLE = False - -def sft_preprocessor( - sample: dict[str, Any], - task_data_spec: TaskDataSpec, - tokenizer, - max_seq_length: int, - idx: int, -) -> DatumSpec: - global PRINTED_SAMPLE - - tokenized_messages = get_formatted_message_log( - sample["messages"], - tokenizer, - task_data_spec, - add_bos_token=False, - add_eos_token=False, - add_generation_prompt=True, - ) - - length = sum(len(m["token_ids"]) for m in tokenized_messages) - - if not PRINTED_SAMPLE: - PRINTED_SAMPLE = True - print(f"\n\nExample sample {idx} with {len(tokenized_messages)} messages and {length} length:") - for m in tokenized_messages: - num_tokens = len(m["token_ids"]) - print(f"\nrole: {m['role']}, tokens: {num_tokens}") - print(f"content start: {m['content'][:100]!r} ...") - print(f"content end: ... {m['content'][-100:]!r}") - print(f"token ids start: {m['token_ids'][:100]} ...") - print(f"token ids end: ... {m['token_ids'][-100:]}") - print("\n\n") - - return { - "message_log": tokenized_messages, - "length": length, - "extra_env_info": None, - "loss_multiplier": 1.0, - "idx": idx, - } - - -def load_datasets(path): - if isinstance(path, str): - ds = load_from_disk(path) - else: - dss = [] - for name in path: - ds = load_from_disk(name) - print(f"Dataset {name}:\n{ds}") - dss.append(ds) - ds = concatenate_datasets(dss) - return ds - - -def main(): - - parser = argparse.ArgumentParser(description="Run SFT training with configuration") - parser.add_argument("--config", type=str, help="Path to YAML config file") - - args, overrides = parser.parse_known_args() - - config = load_config(args.config) - print(f"Loaded configuration from: {args.config}") - - if overrides: - print(f"Overrides: {overrides}") - config = parse_hydra_overrides(config, overrides) - - config: MasterConfig = OmegaConf.to_container(config, resolve=True) - print("Applied CLI overrides") - - print("Final config:") - pprint.pprint(config) - - config["logger"]["log_dir"] = get_next_experiment_dir(config["logger"]["log_dir"]) - print(f"šŸ“Š Using log directory: {config['logger']['log_dir']}") - - if config["checkpointing"]["enabled"]: - print(f"šŸ“Š Using checkpoint directory: {config['checkpointing']['checkpoint_dir']}") - - init_ray() - - tokenizer = get_tokenizer(config["policy"]["tokenizer"]) - print(f"Tokenizer size: {len(tokenizer)}") - - print("\nā–¶ Setting up data...") - - sft_task_spec = TaskDataSpec(task_name="AGI") - - train_dataset = load_datasets(config["data"]["train_dataset_path"]) - train_dataset = train_dataset.shuffle(seed=config["sft"]["seed"]) - print(f"Loaded training samples:\n{train_dataset}") - - val_dataset = load_datasets(config["data"]["val_dataset_path"]) - print(f"Loaded validation samples:\n{val_dataset}") - - train_dataset = AllTaskProcessedDataset( - train_dataset, - tokenizer, - sft_task_spec, - sft_preprocessor, - ) - - val_dataset = AllTaskProcessedDataset( - val_dataset, - tokenizer, - sft_task_spec, - sft_preprocessor, - ) - - ( - policy, - cluster, - train_dataloader, - val_dataloader, - loss_fn, - logger, - checkpointer, - sft_save_state, - master_config, - ) = setup(config, tokenizer, train_dataset, val_dataset) - - sft_train( - policy, - train_dataloader, - val_dataloader, - tokenizer, - loss_fn, - master_config, - logger, - sft_task_spec, - checkpointer, - sft_save_state, - ) - - -if __name__ == "__main__": - main() diff --git a/ARChitects/run_sft_4b.sh b/ARChitects/run_sft_4b.sh deleted file mode 100644 index 2a0a867..0000000 --- a/ARChitects/run_sft_4b.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash - -source /opt/nemo_rl_venv/bin/activate - -export PYTHONPATH=/packages/NeMo-RL - -export NRL_MEGATRON_CHECKPOINT_DIR=./results/megatron - -EXP_NAME="qwen3_4b_grids15_sft139" - -uv run ./run_sft.py --config ./configs/sft_mg.yaml \ - checkpointing.checkpoint_dir="results/$EXP_NAME" \ - cluster.num_nodes=4 \ - sft.seed=24 \ - sft.val_period=200 \ - checkpointing.save_period=200 \ - sft.max_num_epochs=1 \ - sft.max_num_steps=12716 \ - sft.val_global_batch_size=256 \ - sft.val_micro_batch_size=1 \ - policy.model_name="/models/Qwen3-4B-Thinking-2507-16t" \ - policy.tokenizer.name="/models/Qwen3-4B-Thinking-2507-16t" \ - policy.train_global_batch_size=256 \ - policy.train_micro_batch_size=1 \ - policy.megatron_cfg.tensor_model_parallel_size=8 \ - policy.megatron_cfg.optimizer.clip_grad=0.5 \ - policy.megatron_cfg.optimizer.lr=0.0001 \ - policy.megatron_cfg.optimizer.min_lr=0.0000001 \ - policy.megatron_cfg.scheduler.lr_warmup_init=0.000001 \ - policy.megatron_cfg.scheduler.lr_warmup_iters=200 \ - policy.megatron_cfg.scheduler.lr_decay_iters=12716 \ - policy.sequence_packing.train_mb_tokens=256000 \ - logger.wandb.name="$EXP_NAME" \ No newline at end of file diff --git a/ARChitects/sft_mg.yaml b/ARChitects/sft_mg.yaml deleted file mode 100644 index 75fdef4..0000000 --- a/ARChitects/sft_mg.yaml +++ /dev/null @@ -1,144 +0,0 @@ -sft: - max_num_epochs: 5 - max_num_steps: 10000 - val_period: 100 - val_batches: 200 - val_global_batch_size: 8 - val_micro_batch_size: 1 - val_at_start: true - seed: 42 - -checkpointing: - enabled: true - checkpoint_dir: "results/sft" - metric_name: "val_loss" - higher_is_better: false - keep_top_k: 3 - save_period: 100 - checkpoint_must_save_by: null - -policy: - model_name: "/models/Qwen3-4B-Thinking-2507" - tokenizer: - name: ${policy.model_name} - train_global_batch_size: 8 - train_micro_batch_size: 1 - precision: "bfloat16" - fsdp_offload_enabled: false - activation_checkpointing_enabled: false - attn_implementation: flash_attention_2 - - dtensor_cfg: - enabled: false - - megatron_cfg: - enabled: true - activation_checkpointing: true - context_parallel_size: 2 - distributed_data_parallel_config: - average_in_collective: true - data_parallel_sharding_strategy: optim_grads_params - grad_reduce_in_fp32: true - overlap_grad_reduce: true - overlap_param_gather: true - empty_unused_memory_level: 1 - expert_tensor_parallel_size: 1 - expert_model_parallel_size: 1 - num_layers_in_first_pipeline_stage: null - num_layers_in_last_pipeline_stage: null - - optimizer: - adam_beta1: 0.9 - adam_beta2: 0.98 - adam_eps: 1.0e-8 - bf16: true - clip_grad: 1.0 - fp16: false - lr: 0.00003 - min_lr: 0.0000003 - optimizer: adam - params_dtype: bfloat16 - sgd_momentum: 0.9 - use_distributed_optimizer: true - use_precision_aware_optimizer: false #true ## TODO: precision aware optim not working with fp8. Is this expected? - weight_decay: 0.1 - - ## recently introduced, our current mcore commit doesn't have this - #fp8_recipe: delayed - - pipeline_dtype: bfloat16 - pipeline_model_parallel_size: 1 - - scheduler: - lr_decay_style: linear - lr_decay_iters: 900 - lr_warmup_init: 0.00003 - lr_warmup_iters: 0 - start_weight_decay: 0.1 - end_weight_decay: 0.1 - weight_decay_incr_style: constant - - sequence_parallel: false - tensor_model_parallel_size: 8 ## TODO: should not need this large TP size - - freeze_moe_router: true - moe_router_dtype: "fp64" - moe_router_load_balancing_type: "none" # "seq_aux_loss" causes logprob error divergence for grpo - moe_router_bias_update_rate: 0.0 # by default, disable bias updates for grpo - moe_permute_fusion: false - #gives ~20% training perf speedup with sequence packing - apply_rope_fusion: True - - env_vars: - PYTORCH_CUDA_ALLOC_CONF: "expandable_segments:False" - - ## fp8 training currently not supported - #fp8_cfg: - # enabled: true - # fp8: hybrid - # fp8_recipe: delayed - # fp8_param: true # false gives the following error: "RuntimeError: /TransformerEngine/transformer_engine/common/gemm/cublaslt_gemm.cu:116 in function CanonicalizeGemmInput: Assertion failed: !is_fp8_dtype(ret.Atype). Input A is missing column-wise usage" - # fp8_dot_product_attention: false #true - # fp8_multi_head_attention: false #true - - dynamic_batching: - enabled: false - - sequence_packing: - enabled: true - train_mb_tokens: 128000 #${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}} - algorithm: "modified_first_fit_decreasing" - sequence_length_round: 64 - - # makes the training sequence length divisible by the tensor parallel size - # this is useful for sequence parallel training - make_sequence_length_divisible_by: 64 - max_grad_norm: null - -data: - shuffle: true - train_dataset_path: - - data/grids_v15/arc2_training - - data/grids_v15/mini - - data/grids_v15/concept - - data/grids_v15/rearc - - data/grids_v15/nvarc_training - - data/grids_v15/nvarc_full - val_dataset_path: data/grids_v15/arc2_evaluation6 - -logger: - log_dir: "logs" # Base directory for all logs - wandb_enabled: true # Make sure you do a ``wandb login [Your API key]'' before running - tensorboard_enabled: false - mlflow_enabled: false - monitor_gpus: false # If true, will monitor GPU usage and log to wandb and/or tensorboard - wandb: - project: "arc2" - name: "sft" - gpu_monitoring: - collection_interval: 10 # How often to collect GPU usage metrics (in seconds) - flush_interval: 10 # How often to flush GPU usage metrics to the loggers (in seconds) - -cluster: - gpus_per_node: 8 - num_nodes: 1 diff --git a/README.md b/README.md index 52c1340..9d1f494 100644 --- a/README.md +++ b/README.md @@ -1,54 +1,107 @@ -# NVARC solution to ARC-AGI-2 2025 +# Trelis-NVARC -This repository contains the code and instructions to replicate the NVARC submissions to the [Arc Prize 2025 competition on Kaggle](https://www.kaggle.com/competitions/arc-prize-2025). +ARC-AGI-2 NVARC description generation and synthetic dataset creation using Gemini 3 Flash. -The NVARC team includes Ivan Sorokin and Jean-Francois Puget, who are also members of the NVIDIA [KGMoN](https://www.nvidia.com/en-us/ai-data-science/kaggle-grandmasters) team. +## Overview -The solution is described in the [paper](nvarc_2025.pdf) and consists of three main components: +This project implements the NVARC SDG (Synthetic Data Generation) pipeline for ARC-AGI-2 tasks, using Gemini 3 Flash as the LLM. The pipeline uses only SDG prompts and does not generate intermediate JSON traces. -- Multi-stage synthetic data generation pipeline; -- Improved version of the ARChitects solution that won the [ARC Prize competition in 2024](https://www.kaggle.com/competitions/arc-prize-2024); -- Improved version of Tiny Recursive Models by Alexia Jolicoeur-Martineau. +## NVARC SDG Pipeline -## Synthetic Data Generation +The complete pipeline follows these steps: -The scripts and prompts for Synthetic Data Generation pipeline can be found in [SDG](SDG) folder. +1. **Generate descriptions (.nvarc.md)** using `SDG/prompts/summary_v2.md` with Gemini 3 Flash +2. **[SKIPPED]** Task mixing using `mix_v2.md` - we skip this step +3. **Generate input code** using `SDG/prompts/generate_puzzle_input.md` prompt with Gemini 3 Flash +4. **Generate output code** using `SDG/prompts/generate_puzzle_output.md` prompt with Gemini 3 Flash +5. **Execute input logic** to generate input grids +6. **Execute output logic** to generate output grids +7. **Make pairs** from input/output grids +8. **Build final dataset** with augmentations -[NVARC Artifacts Puzzles](https://www.kaggle.com/datasets/sorokin/nvarc-artifacts-puzzles) dataset includes generated text used to construct the synthetic puzzles. +**Simplified Approach**: This pipeline directly generates `.nvarc.md` files using SDG prompts, without intermediate JSON traces. + +## Quick Start ```bash -kaggle datasets download -d sorokin/nvarc-artifacts-puzzles -unzip nvarc-artifacts-puzzles.zip -``` +# Activate virtual environment +source .venv/bin/activate -[NVARC Synthetic Puzzles](https://www.kaggle.com/datasets/sorokin/nvarc-synthetic-puzzles) dataset includes our 103k synthetic puzzles. +# Run complete pipeline on 3 sample tasks +python3 run_nvarc_pipeline.py -```bash -kaggle datasets download -d sorokin/nvarc-synthetic-puzzles -unzip nvarc-synthetic-puzzles.zip -``` +# Run on specific tasks +python3 run_nvarc_pipeline.py --task-ids 007bbfb7,00d62c1b -[NVARC Augmented Puzzles](https://www.kaggle.com/datasets/sorokin/nvarc-augmented-puzzles) dataset includes few subsets with 3.2M augmented puzzles. +# Run on 10 random tasks +python3 run_nvarc_pipeline.py --all-tasks --count 10 -```bash -kaggle datasets download -d sorokin/nvarc-augmented-puzzles -unzip nvarc-augmented-puzzles.zip +# Generate more grids per task +python3 run_nvarc_pipeline.py --task-ids 007bbfb7 --num-grids 50 + +# Skip certain steps (e.g., regenerate grids only) +python3 run_nvarc_pipeline.py --skip descriptions,code_generation ``` -Visualization of synthetic puzzles shown in the Kaggle notebook [nvarc-viewer](https://www.kaggle.com/code/sorokin/nvarc-viewer). +## Pipeline Outputs -## The ARChitects +After running the pipeline, you'll find: -The hyperparameters and fine-tuning scripts for the Qwen3 4B model are located in the [ARChitects](ARChitects) folder. +- `traces/` - Puzzle descriptions (.nvarc.md files in XML format) +- `logic/` - Generated Python code for input/output generation +- `synthetic/grids_arc_input/` - Generated input grids +- `synthetic/grids_arc_output/` - Generated output grids +- `synthetic/pairs_arc/` - Input/output grid pairs +- `synthetic/arc_dataset.json` - Final dataset with augmentations -The submission notebook is available on Kaggle [sorokin/arc2-qwen3-unsloth-flash-lora-batch4-queue](https://www.kaggle.com/code/sorokin/arc2-qwen3-unsloth-flash-lora-batch4-queue). +## Individual Scripts -## Tiny Recursive Models +You can also run individual steps: -The scripts and instructions to train Tiny Recursive Models are in the [TRM](TRM) folder. +```bash +# Step 1: Generate descriptions (.nvarc.md) from ARC tasks using SDG summary_v2.md +python3 generate_summaries.py --task 007bbfb7 +python3 generate_summaries.py --batch 100 + +# Steps 3-4: Generate input/output code from descriptions +python3 seed_to_logic.py --batch 10 + +# Step 5: Generate input grids from code +python3 SDG/scripts/generate_input_grids.py \ + --inputs-mask "logic/*.py" \ + --grids-prefix "synthetic/grids_arc_input" \ + --num-grids 30 + +# Step 6: Generate output grids +python3 SDG/scripts/generate_output_grids.py \ + --solutions-mask "logic/*.py" \ + --input-grids-prefix "synthetic/grids_arc_input" \ + --output-grids-prefix "synthetic/grids_arc_output" + +# Step 7: Create pairs +python3 SDG/scripts/make_pairs.py \ + --input-grids-prefix "synthetic/grids_arc_input" \ + --output-grids-mask "synthetic/grids_arc_output/*/*.json" \ + --output-prefix "synthetic/pairs_arc" + +# Step 8: Build dataset +python3 SDG/scripts/build_datasets.py \ + --input-prefix "synthetic/pairs_arc" \ + --output-dataset "synthetic/arc_dataset.json" \ + --augmentations dihedral color +``` + +## Testing + +Test generated code on original ARC tasks: + +```bash +python3 test_on_original_tasks.py --batch --output test_results.json +``` -The submission notebook is available on Kaggle [cpmpml/arc2-trm-v31](https://www.kaggle.com/code/cpmpml/arc2-trm-v31?scriptVersionId=278223801). +## Key Differences from Original NVARC -## ARC AGI 2024 +- **Task mixing skipped**: We don't use the `mix_v2.md` prompt to create mixed puzzles +- **Gemini 3 Flash**: Uses `gemini-3-flash-preview` model instead of GPT/other LLMs +- **Clean room compliance**: Uses only ARC-AGI-2 training challenges, no test/evaluation data -We ran our winning solution on last year ARC AGI evaluation data. The code can be found in the [ARC-AGI1](ARC-AGI1) folder. diff --git a/SDG/scripts/generate_input_grids.py b/SDG/scripts/generate_input_grids.py index 8ca28e8..336c056 100644 --- a/SDG/scripts/generate_input_grids.py +++ b/SDG/scripts/generate_input_grids.py @@ -11,7 +11,7 @@ def validate_grids(grids: list[Grid]) -> bool: - if len(grids) < 5: + if len(grids) < 1: return False unique_colors = set() first_seed, first_grid = grids[0] @@ -89,10 +89,16 @@ def generate_grids(inputs_mask: str, grids_prefix: str, num_grids: int, init_see for file_name in tqdm(puzzle_files, desc="Generating input grids"): puzzle_parts = file_name.split("/") - puzzle_name = puzzle_parts[-1][:-3] - puzzle_batch = puzzle_parts[-3] - split_name = puzzle_parts[-4] - grids_json = f"{grids_prefix}/{split_name}/{puzzle_batch}/{puzzle_name}.json" + puzzle_name = os.path.splitext(puzzle_parts[-1])[0] + # Robust path extraction + puzzle_batch = puzzle_parts[-3] if len(puzzle_parts) >= 3 else "default_batch" + split_name = puzzle_parts[-4] if len(puzzle_parts) >= 4 else "default_split" + + # If paths are shallow, just put everything in a flat grids folder + if len(puzzle_parts) < 3: + grids_json = f"{grids_prefix}/{puzzle_name}.json" + else: + grids_json = f"{grids_prefix}/{split_name}/{puzzle_batch}/{puzzle_name}.json" if os.path.exists(grids_json): num_existing_grids += 1 diff --git a/SDG/scripts/generate_output_grids.py b/SDG/scripts/generate_output_grids.py index c6126b4..0313ec9 100644 --- a/SDG/scripts/generate_output_grids.py +++ b/SDG/scripts/generate_output_grids.py @@ -28,26 +28,37 @@ def generate_output_grid(output_code: str, input_grid: Grid) -> Grid | None: def generate_grids(solutions_mask: str, input_grids_prefix: str, output_grids_prefix: str, min_solutions_per_puzzle: int): - puzzle_files = glob.glob(solutions_mask + "/completions/0.md") - print(f"Found {len(puzzle_files)} solution puzzles in {solutions_mask}") + # If the mask already points to files (e.g. logic/*.py), use it directly + if solutions_mask.endswith(".py"): + puzzle_files = glob.glob(solutions_mask) + else: + puzzle_files = glob.glob(solutions_mask + "/completions/0.md") + print(f"Found {len(puzzle_files)} target files in {solutions_mask}") num_skipped_puzzles = 0 num_existing_grids = 0 for file_name in tqdm(puzzle_files, desc="Generating output grids"): - puzzle_dir = os.path.dirname(os.path.dirname(file_name)) - puzzle_parts = puzzle_dir.split("/") - puzzle_name = puzzle_parts[-1] - puzzle_batch = puzzle_parts[-2] - puzzle_version = puzzle_parts[-3] - - grids_output_json = f"{output_grids_prefix}/{puzzle_version}/{puzzle_batch}/{puzzle_name}.json" - if os.path.exists(grids_output_json): - num_existing_grids += 1 - continue - - grids_input_json = f"{input_grids_prefix}/{puzzle_version}/{puzzle_batch}/{puzzle_name}.json" + if file_name.endswith(".py"): + puzzle_name = os.path.splitext(os.path.basename(file_name))[0] + puzzle_batch = "default_batch" + puzzle_version = "default_version" + puzzle_dir = os.path.dirname(file_name) + else: + puzzle_dir = os.path.dirname(os.path.dirname(file_name)) + puzzle_parts = puzzle_dir.split("/") + puzzle_name = puzzle_parts[-1] + puzzle_batch = puzzle_parts[-2] + puzzle_version = puzzle_parts[-3] + + if file_name.endswith(".py"): + grids_output_json = os.path.join(output_grids_prefix, f"{puzzle_name}.json") + grids_input_json = os.path.join(input_grids_prefix, f"{puzzle_name}.json") + else: + grids_output_json = os.path.join(output_grids_prefix, puzzle_version, puzzle_batch, f"{puzzle_name}.json") + grids_input_json = os.path.join(input_grids_prefix, puzzle_version, puzzle_batch, f"{puzzle_name}.json") + if not os.path.exists(grids_input_json): num_skipped_puzzles += 1 continue @@ -55,29 +66,33 @@ def generate_grids(solutions_mask: str, input_grids_prefix: str, output_grids_pr try: with open(grids_input_json, "r") as f: input_grids = json.load(f) - assert len(input_grids) == 30 + assert len(input_grids) >= 1 except: print(f"Error loading {grids_input_json}") num_skipped_puzzles += 1 continue - output_codes = [] - for i in range(20): - if not os.path.exists(f"{puzzle_dir}/completions/{i}.md"): - break - with open(f"{puzzle_dir}/completions/{i}.md", "r") as f: - output_code = f.read() - output_code = parse_python_code(output_code) - if output_code is None: - break - try: - output_code = remove_unused_functions(output_code) - except: - break - if "def generate_puzzle_output(" not in output_code: - break - output_codes.append(output_code) - if len(output_codes) < min_solutions_per_puzzle: + if file_name.endswith(".py"): + output_codes = [parse_python_code(open(file_name).read())] + else: + output_codes = [] + for i in range(20): + if not os.path.exists(f"{puzzle_dir}/completions/{i}.md"): + break + with open(f"{puzzle_dir}/completions/{i}.md", "r") as f: + output_code = f.read() + output_code = parse_python_code(output_code) + if output_code is None: + break + try: + output_code = remove_unused_functions(output_code) + except: + break + if "def generate_puzzle_output(" not in output_code: + break + output_codes.append(output_code) + + if len(output_codes) < min_solutions_per_puzzle and not file_name.endswith(".py"): num_skipped_puzzles += 1 continue diff --git a/SDG/scripts/parser.py b/SDG/scripts/parser.py index f8b0e61..5249897 100644 --- a/SDG/scripts/parser.py +++ b/SDG/scripts/parser.py @@ -1,11 +1,10 @@ import re import tree_sitter_python -from tree_sitter import Language, Parser, Node +from tree_sitter import Language, Parser, Node, Query, QueryCursor PY_LANGUAGE = Language(tree_sitter_python.language()) -parser = Parser() -parser.language = PY_LANGUAGE +parser = Parser(PY_LANGUAGE) def get_value(match: dict[str, list[Node]], name: str) -> str: values = match[name] @@ -13,7 +12,8 @@ def get_value(match: dict[str, list[Node]], name: str) -> str: return values[0].text.decode("utf8") def parse_functions(text: str) -> dict[str, str]: - query = PY_LANGUAGE.query( + query = Query( + PY_LANGUAGE, """ ( function_definition @@ -23,7 +23,11 @@ def parse_functions(text: str) -> dict[str, str]: ) names = dict() tree = parser.parse(bytes(text, "utf8")) - for _, match in query.matches(tree.root_node): + + # matches() is now used like this in newer tree-sitter versions: + # returns iterator of (pattern_index, capture_dict) + cursor = QueryCursor(query) + for _, match in cursor.matches(tree.root_node): if match["code"][0].start_point.column != 0: # Exclude nested functions continue @@ -72,6 +76,6 @@ def remove_unused_functions(code: str) -> str: def parse_python_code(code: str): codes = re.findall(r"```python(.*?)```", code, re.DOTALL) if not codes: - return None + return code.strip() longest_code = max(codes, key=len) return longest_code.strip() \ No newline at end of file diff --git a/TRM/README.md b/TRM/README.md deleted file mode 100644 index 90ac253..0000000 --- a/TRM/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# Training Tiny Recurive Models (TRM) for ARC AGI2 - -This directory contains the code and data we used to pretrain the TRM we used during the arc prize 2025 competition. - -This code has been tested on Kaggle container image 2025-10-06, on a machine with 8 H100 GPUs. It depends on the TRM code cloned in the `../external/` directory. Installing TRM in another container image may require a different installation procedure. Please refer to https://github.com/SamsungSAILMontreal/TinyRecursiveModels/ for general installation. - -The following bash commands must be run in this directory. - -## 1. Install TRM. - -```bash -pip install hydra-core==1.3.2 adam_atan2_pytorch==0.2.4 argdantic==1.3.3 coolname==2.2.0 numba==0.62.1 - -ln -s ../external/TinyRecursiveModels/models . -ln -s ../external/TinyRecursiveModels/puzzle_dataset.py . -ln -s ../external/TinyRecursiveModels/dataset . -ln -s ../external/TinyRecursiveModels/utils . -ln -s ../external/TinyRecursiveModels/evaluators . -ln -s ../external/TinyRecursiveModels/assets . -ln -s ../external/TinyRecursiveModels/config . -ln -s ../external/TinyRecursiveModels/kaggle . - -``` - -## 2. Download the TRM training dataset. -Download from `https://www.kaggle.com/datasets/cpmpml/arc-prize-trm-training-data` into the `arc-prize-trm-training-data` directory, then unzip the archive. This dataset contains 4073 puzzles (the competition puzzles plus about 3k datasets from our SDG pipeline). We used 256 augmentations, which produced a total of 1041207 puzzles. - -## 3. Download the TRM evaluation dataset. -Download from `https://www.kaggle.com/datasets/cpmpml/arc-prize-trm-evaluation-data` into the `arc-prize-trm-evaluation-data` directory, then unzip the archive. This dataset was created with 128 augmentations per puzzle, to match what we use in Kaggle notebooks. - -## 4. Pretrain the model. - -We removed the wandb dependency as well as the model evaluation during training in order to speed up training. It is possible to use the `pretrain.py` script in the TinyRecursiveModels repository if you prefer to use wandb and evaluation during training. In that case make sure to adapt the traing command. - -```bash -torchrun --standalone --nnodes=1 --nproc-per-node 8 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 \ -pretrain-no-eval.py \ -arch=trm \ -data_paths="[arc-prize-trm-training-data]" \ -arch.L_layers=2 \ -arch.H_cycles=3 arch.L_cycles=4 \ -ema=True \ -evaluators=[] \ -lr=0.0003 \ -+checkpoint_path=./checkpoint \ -global_batch_size=3072 \ -eval_interval=1000 \ -epochs=10000 -``` - -This will save 10 checkpoints in the `checkpoint` directory. We uploaded these to a [kaggle dataset](https://www.kaggle.com/datasets/cpmpml/arc-prize-trm-031) and used them in our submissions. - -## 5. Evaluate the model (Optional). - -We can also evaluate this model on the competition evaluation data with the following command. We use 4 GPUs only, and we limit batch size, to mimick what we can run on Kaggle notebooks. Note that we also changed some TRM hyper parameters because they yield better results during the test time fine tuning step. - -```bash -torchrun --standalone --nnodes=1 --nproc-per-node 4 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 \ -eval-arc-k-10.py \ -arch=trm \ -data_paths="[arc-prize-trm-evaluation-data]" \ -arch.L_layers=2 \ -arch.H_cycles=4 arch.L_cycles=4 arch.halt_max_steps=10 \ -+load_checkpoint=./checkpoint/step_220708 \ -eval_interval=2000 \ -epochs=2000 \ -global_batch_size=128 \ -ema=True \ -+checkpoint_path=./checkpoint-eval \ -lr_warmup_steps=200 \ -lr=0.0001 -``` - -This evaluation produced these statistics when we ran it: - -```bash -{'all': {'accuracy': 0.7744678, 'exact_accuracy': 0.043603994, 'lm_loss': 1.2915632, 'q_halt_accuracy': 0.76508063, 'q_halt_loss': 0.6607322, 'steps': 10.0}, 'ARC/pass@1': 0.0763888888888889, 'ARC/pass@2': 0.1013888888888889, 'ARC/pass@5': 0.1013888888888889, 'ARC/pass@10': 0.1013888888888889, 'ARC/pass@100': 0.1375, 'ARC/pass@1000': 0.1375} -``` -When submitted this scored 10.0 on Kaggle public leaderboard, version 18 of this notebook `https://www.kaggle.com/code/cpmpml/arc2-trm-v31?scriptVersionId=278223801` diff --git a/TRM/arc-prize-trm-evaluation-data/README.md b/TRM/arc-prize-trm-evaluation-data/README.md deleted file mode 100644 index 29fe74d..0000000 --- a/TRM/arc-prize-trm-evaluation-data/README.md +++ /dev/null @@ -1 +0,0 @@ -Download from https://www.kaggle.com/datasets/cpmpml/arc-prize-trm-evaluation-data into this directory, then unzip the archive. diff --git a/TRM/arc-prize-trm-training-data/README.md b/TRM/arc-prize-trm-training-data/README.md deleted file mode 100644 index 9ace8c1..0000000 --- a/TRM/arc-prize-trm-training-data/README.md +++ /dev/null @@ -1 +0,0 @@ -Download from https://www.kaggle.com/datasets/cpmpml/arc-prize-trm-training-data into this directory, then unzip the archive. diff --git a/TRM/eval-arc-k-10.py b/TRM/eval-arc-k-10.py deleted file mode 100644 index 920078f..0000000 --- a/TRM/eval-arc-k-10.py +++ /dev/null @@ -1,655 +0,0 @@ -from typing import Optional, Any, Sequence, List -from dataclasses import dataclass -import os -import math -import yaml -import shutil -import copy - -import torch -import torch.distributed as dist -from torch import nn -from torch.utils.data import DataLoader - -import tqdm -#import wandb -import coolname -import hydra -import pydantic -from omegaconf import DictConfig -from adam_atan2_pytorch import AdamAtan2 - -from puzzle_dataset import PuzzleDataset, PuzzleDatasetConfig, PuzzleDatasetMetadata -from utils.functions import load_model_class, get_model_source_path -from models.sparse_embedding import CastedSparseEmbeddingSignSGD_Distributed -from models.ema import EMAHelper - - -class LossConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') - name: str - - -class ArchConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') - name: str - loss: LossConfig - - -class EvaluatorConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra="allow") - name: str - - -class PretrainConfig(pydantic.BaseModel): - # Config - arch: ArchConfig - # Data - data_paths: List[str] - data_paths_test: List[str] = [] - # Evaluators - evaluators: List[EvaluatorConfig] = [] - - # Hyperparams - global_batch_size: int - epochs: int - - lr: float - lr_min_ratio: float - lr_warmup_steps: int - - weight_decay: float - beta1: float - beta2: float - - # Puzzle embedding - puzzle_emb_lr: float - puzzle_emb_weight_decay: float - - # Names - project_name: Optional[str] = None - run_name: Optional[str] = None - load_checkpoint: Optional[str] = None - checkpoint_path: Optional[str] = None - - # Extras - seed: int = 0 - checkpoint_every_eval: bool = False - eval_interval: Optional[int] = None - min_eval_interval: Optional[int] = 0 # when to start eval - eval_save_outputs: List[str] = [] - - ema: bool = False # use Exponential-Moving-Average - ema_rate: float = 0.999 # EMA-rate - freeze_weights: bool = False # If True, freeze weights and only learn the embeddings - -@dataclass -class TrainState: - model: nn.Module - optimizers: Sequence[torch.optim.Optimizer] - optimizer_lrs: Sequence[float] - carry: Any - - step: int - total_steps: int - - -def create_dataloader(config: PretrainConfig, split: str, rank: int, world_size: int, **kwargs): - dataset = PuzzleDataset(PuzzleDatasetConfig( - seed=config.seed, - dataset_paths=config.data_paths_test if len(config.data_paths_test)>0 and split=="test" else config.data_paths, - rank=rank, - num_replicas=world_size, - **kwargs - ), split=split) - dataloader = DataLoader( - dataset, - batch_size=None, - num_workers=1, - prefetch_factor=8, - pin_memory=True, - persistent_workers=True - ) - return dataloader, dataset.metadata - - -def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, rank: int, world_size: int): - model_cfg = dict( - **config.arch.__pydantic_extra__, # type: ignore - batch_size=config.global_batch_size // world_size, - vocab_size=train_metadata.vocab_size, - seq_len=train_metadata.seq_len, - num_puzzle_identifiers=train_metadata.num_puzzle_identifiers, - causal=False # Non-autoregressive - ) - - # Instantiate model with loss head - model_cls = load_model_class(config.arch.name) - loss_head_cls = load_model_class(config.arch.loss.name) - - with torch.device("cuda"): - model: nn.Module = model_cls(model_cfg) - print(model) - model = loss_head_cls(model, **config.arch.loss.__pydantic_extra__) # type: ignore - if "DISABLE_COMPILE" not in os.environ: - model = torch.compile(model) # type: ignore - - # Load checkpoint - if rank == 0: - load_checkpoint(model, config) - - # Broadcast parameters from rank 0 - if world_size > 1: - with torch.no_grad(): - for param in list(model.parameters()) + list(model.buffers()): - dist.broadcast(param, src=0) - - # Optimizers and lr - if config.arch.puzzle_emb_ndim == 0: - optimizers = [ - AdamAtan2( - model.parameters(), - lr=0.0001, # Needs to be set by scheduler - weight_decay=config.weight_decay, - betas=(config.beta1, config.beta2) - ) - ] - optimizer_lrs = [ - config.lr - ] - elif config.freeze_weights: - optimizers = [ - CastedSparseEmbeddingSignSGD_Distributed( - model.model.puzzle_emb.buffers(), # type: ignore - lr=0, # Needs to be set by scheduler - weight_decay=config.puzzle_emb_weight_decay, - world_size=world_size - ) - ] - optimizer_lrs = [ - config.puzzle_emb_lr - ] - else: - optimizers = [ - CastedSparseEmbeddingSignSGD_Distributed( - model.model.puzzle_emb.buffers(), # type: ignore - lr=0, # Needs to be set by scheduler - weight_decay=config.puzzle_emb_weight_decay, - world_size=world_size - ), - AdamAtan2( - model.parameters(), - lr=0.0001, # Needs to be set by scheduler - weight_decay=config.weight_decay, - betas=(config.beta1, config.beta2) - ) - ] - optimizer_lrs = [ - config.puzzle_emb_lr, - config.lr - ] - - return model, optimizers, optimizer_lrs - -def mix_weights_direct(device, alpha, net, nets): - sd = [] - for i in range(len(nets)): - sd += [nets[i].state_dict()] - sd_alpha = {} - for k in sd[0].keys(): - comb_net = alpha[0]*sd[0][k].to(device) - for i in range(1,len(nets)): - comb_net += alpha[i]*sd[i][k].to(device) - sd_alpha[k] = comb_net - net.load_state_dict(sd_alpha) - return net - -def cosine_schedule_with_warmup_lr_lambda( - current_step: int, *, base_lr: float, num_warmup_steps: int, num_training_steps: int, min_ratio: float = 0.0, num_cycles: float = 0.5 -): - if current_step < num_warmup_steps: - return base_lr * float(current_step) / float(max(1, num_warmup_steps)) - - progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) - return base_lr * (min_ratio + max(0.0, (1 - min_ratio) * 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))) - - -def init_train_state(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, rank: int, world_size: int): - # Estimated total training steps - total_steps = int(config.epochs * train_metadata.total_groups * train_metadata.mean_puzzle_examples / config.global_batch_size) - - # Model - model, optimizers, optimizer_lrs = create_model(config, train_metadata, rank=rank, world_size=world_size) - - return TrainState( - step=0, - total_steps=total_steps, - - model=model, - optimizers=optimizers, - optimizer_lrs=optimizer_lrs, - carry=None - ) - - -def save_train_state(config: PretrainConfig, train_state: TrainState): - # FIXME: Only saved model. - if config.checkpoint_path is None: - return - - os.makedirs(config.checkpoint_path, exist_ok=True) - torch.save(train_state.model.state_dict(), os.path.join(config.checkpoint_path, f"step_{train_state.step}")) - - -def load_checkpoint(model: nn.Module, config: PretrainConfig): - if config.load_checkpoint is not None: - print(f"Loading checkpoint {config.load_checkpoint}") - - # Load state dict - state_dict = torch.load(config.load_checkpoint, map_location="cuda") - - # Resize and reset puzzle emb if needed - puzzle_emb_name = "_orig_mod.model.inner.puzzle_emb.weights" - expected_shape: torch.Size = model.model.puzzle_emb.weights.shape # type: ignore - if puzzle_emb_name in state_dict: - puzzle_emb = state_dict[puzzle_emb_name] - if puzzle_emb.shape != expected_shape: - print(f"Resetting puzzle embedding as shape is different. Found {puzzle_emb.shape}, Expected {expected_shape}") - # Re-initialize using mean - state_dict[puzzle_emb_name] = ( - torch.mean(puzzle_emb, dim=0, keepdim=True).expand(expected_shape).contiguous() - ) - model.load_state_dict(state_dict, assign=True) - - -def compute_lr(base_lr: float, config: PretrainConfig, train_state: TrainState): - return cosine_schedule_with_warmup_lr_lambda( - current_step=train_state.step, - base_lr=base_lr, - num_warmup_steps=round(config.lr_warmup_steps), - num_training_steps=train_state.total_steps, - min_ratio=config.lr_min_ratio - ) - - - -def create_evaluators(config: PretrainConfig, eval_metadata: PuzzleDatasetMetadata) -> List[Any]: - data_paths =config.data_paths_test if len(config.data_paths_test)>0 else config.data_paths - # Initialize evaluators - evaluators = [] - for cfg in config.evaluators: - for data_path in data_paths: - cls = load_model_class(cfg.name, "evaluators.")( - data_path=data_path, eval_metadata=eval_metadata, - submission_K=10, - **cfg.__pydantic_extra__ - ) # type: ignore - evaluators.append(cls) - - return evaluators - -def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, global_batch_size: int, rank: int, world_size: int): - train_state.step += 1 - if train_state.step > train_state.total_steps: # At most train_total_steps - return - - # To device - batch = {k: v.cuda() for k, v in batch.items()} - - # Init carry if it is None - if train_state.carry is None: - with torch.device("cuda"): - train_state.carry = train_state.model.initial_carry(batch) # type: ignore - - # Forward - train_state.carry, loss, metrics, _, _ = train_state.model(carry=train_state.carry, batch=batch, return_keys=[]) - - ((1 / global_batch_size) * loss).backward() - - # Allreduce - if world_size > 1: - for param in train_state.model.parameters(): - if param.grad is not None: - dist.all_reduce(param.grad) - - # Apply optimizer - lr_this_step = None - for optim, base_lr in zip(train_state.optimizers, train_state.optimizer_lrs): - lr_this_step = compute_lr(base_lr, config, train_state) - - for param_group in optim.param_groups: - param_group['lr'] = lr_this_step - - optim.step() - optim.zero_grad() - - # Reduce metrics - if len(metrics): - assert not any(v.requires_grad for v in metrics.values()) - - metric_keys = list(sorted(metrics.keys())) # Sort keys to guarantee all processes use the same order. - # Reduce and reconstruct - metric_values = torch.stack([metrics[k] for k in metric_keys]) - if world_size > 1: - dist.reduce(metric_values, dst=0) - - if rank == 0: - metric_values = metric_values.cpu().numpy() - reduced_metrics = {k: metric_values[i] for i, k in enumerate(metric_keys)} - - # Postprocess - count = max(reduced_metrics["count"], 1) # Avoid NaNs - reduced_metrics = {f"train/{k}": v / (global_batch_size if k.endswith("loss") else count) for k, v in reduced_metrics.items()} - - reduced_metrics["train/lr"] = lr_this_step - return reduced_metrics - -def evaluate( - config: PretrainConfig, - train_state: TrainState, - eval_loader: torch.utils.data.DataLoader, - eval_metadata: PuzzleDatasetMetadata, - evaluators: List[Any], - rank: int, - world_size: int, - cpu_group: Optional[dist.ProcessGroup], -): - reduced_metrics = None - - with torch.inference_mode(): - return_keys = set(config.eval_save_outputs) - for evaluator in evaluators: - evaluator.begin_eval() - return_keys.update(evaluator.required_outputs) - - # Run evaluation - set_ids = {k: idx for idx, k in enumerate(eval_metadata.sets)} - - save_preds = {} - - metric_keys = [] - metric_values = None - - carry = None - processed_batches = 0 - - for set_name, batch, global_batch_size in eval_loader: - processed_batches += 1 - if rank == 0: - print(f"Processing batch {processed_batches}: {set_name}") - - # To device - batch = {k: v.cuda() for k, v in batch.items()} - with torch.device("cuda"): - carry = train_state.model.initial_carry(batch) # type: ignore - - # Forward - inference_steps = 0 - while True: - carry, loss, metrics, preds, all_finish = train_state.model( - carry=carry, batch=batch, return_keys=return_keys - ) - inference_steps += 1 - - if all_finish: - break - - if rank == 0: - print(f" Completed inference in {inference_steps} steps") - - for collection in (batch, preds): - for k, v in collection.items(): - if k in config.eval_save_outputs: - save_preds.setdefault(k, []) - save_preds[k].append(v.cpu()) # Move to CPU for saving GPU memory - - for evaluator in evaluators: - evaluator.update_batch(batch, preds) - - del carry, loss, preds, batch, all_finish - - # Aggregate metrics - set_id = set_ids[set_name] - - if metric_values is None: - metric_keys = list( - sorted(metrics.keys()) - ) # Sort keys to guarantee all processes use the same order. - metric_values = torch.zeros( - (len(set_ids), len(metrics.values())), dtype=torch.float32, device="cuda" - ) - - metric_values[set_id] += torch.stack([metrics[k] for k in metric_keys]) - - del metrics - - # concatenate save preds - save_preds = {k: torch.cat(v, dim=0) for k, v in save_preds.items()} - - # Save preds - if config.checkpoint_path is not None and len(save_preds): - # Each rank save predictions independently - os.makedirs(os.path.dirname(config.checkpoint_path), exist_ok=True) - torch.save( - save_preds, os.path.join(config.checkpoint_path, f"step_{train_state.step}_all_preds.{rank}") - ) - - del save_preds - - # Reduce to rank 0 - if metric_values is not None: - if world_size > 1: - dist.reduce(metric_values, dst=0) - - if rank == 0: - reduced_metrics = metric_values.cpu().numpy() - reduced_metrics = { - set_name: { - metric_name: reduced_metrics[set_id, metric_id] - for metric_id, metric_name in enumerate(metric_keys) - } - for set_id, set_name in enumerate(set_ids) - } - - # Postprocess - for set_name, m in reduced_metrics.items(): - count = m.pop("count") - reduced_metrics[set_name] = {k: v / count for k, v in m.items()} - - # Run evaluators - if rank == 0: - print(f"\nRunning {len(evaluators)} evaluator(s)...") - - for i, evaluator in enumerate(evaluators): - if rank == 0: - print(f"Running evaluator {i+1}/{len(evaluators)}: {evaluator.__class__.__name__}") - - # Path for saving - evaluator_save_path = None - if config.checkpoint_path is not None: - evaluator_save_path = os.path.join( - config.checkpoint_path, - f"evaluator_{evaluator.__class__.__name__}_step_{train_state.step}", - ) - os.makedirs(evaluator_save_path, exist_ok=True) - - # Run and log - metrics = evaluator.result(evaluator_save_path, rank=rank, world_size=world_size, group=cpu_group) - if rank == 0 and metrics is not None: - if reduced_metrics is None: - reduced_metrics = {} - - reduced_metrics.update(metrics) - print(f" Completed {evaluator.__class__.__name__}") - - if rank == 0: - print("All evaluators completed!") - - return reduced_metrics - -def save_code_and_config(config: PretrainConfig): - if config.checkpoint_path is None: - return - - os.makedirs(config.checkpoint_path, exist_ok=True) - - # Copy code - code_list = [ - get_model_source_path(config.arch.name), - get_model_source_path(config.arch.loss.name) - ] - for code_file in code_list: - if code_file is not None: - code_name = os.path.basename(code_file) - - shutil.copy(code_file, os.path.join(config.checkpoint_path, code_name)) - - # Dump config as yaml - config_file = os.path.join(config.checkpoint_path, "all_config.yaml") - with open(config_file, "wt") as f: - yaml.dump(config.model_dump(), f) - - # Log code - print(config.checkpoint_path) - - -def load_synced_config(hydra_config: DictConfig, rank: int, world_size: int) -> PretrainConfig: - objects = [None] - if rank == 0: - config = PretrainConfig(**hydra_config) # type: ignore - - # Naming - if config.project_name is None: - config.project_name = f"{os.path.basename(config.data_paths[0]).capitalize()}-ACT-torch" - if config.run_name is None: - config.run_name = f"{config.arch.name.split('@')[-1]} {coolname.generate_slug(2)}" - if config.checkpoint_path is None: - config.checkpoint_path = os.path.join("checkpoints", config.project_name, config.run_name) - - objects = [config] - - if world_size > 1: - dist.broadcast_object_list(objects, src=0) - - return objects[0] # type: ignore - - -@hydra.main(config_path="config", config_name="cfg_pretrain", version_base=None) -def launch(hydra_config: DictConfig): - RANK = 0 - WORLD_SIZE = 1 - CPU_PROCESS_GROUP = None - - # Initialize distributed training if in distributed environment (e.g. torchrun) - if "LOCAL_RANK" in os.environ: - # Initialize distributed, default device and dtype - dist.init_process_group(backend="nccl") - - RANK = dist.get_rank() - WORLD_SIZE = dist.get_world_size() - - torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) - - # CPU GLOO process group - CPU_PROCESS_GROUP = dist.new_group(backend="gloo") - assert ( - dist.get_rank(CPU_PROCESS_GROUP) == RANK and dist.get_world_size(CPU_PROCESS_GROUP) == WORLD_SIZE - ) - - # Load sync'ed config - config = load_synced_config(hydra_config, rank=RANK, world_size=WORLD_SIZE) - - # Seed RNGs to ensure consistency - torch.random.manual_seed(config.seed + RANK) - - # Dataset - train_epochs_per_iter = config.eval_interval if config.eval_interval is not None else config.epochs - total_iters = config.epochs // train_epochs_per_iter - - assert config.epochs % train_epochs_per_iter == 0, "Eval interval must be a divisor of total epochs." - - train_loader, train_metadata = create_dataloader(config, "train", test_set_mode=False, epochs_per_iter=train_epochs_per_iter, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) - try: - eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) - except: - print("NO EVAL DATA FOUND") - eval_loader = eval_metadata = None - - try: - evaluators = create_evaluators(config, eval_metadata) - except: - print("No evaluator found") - evaluators = [] - - # Train state - train_state = init_train_state(config, train_metadata, rank=RANK, world_size=WORLD_SIZE) - - # Progress bar and logger - progress_bar = None - ema_helper = None - if RANK == 0: - progress_bar = tqdm.tqdm(total=train_state.total_steps) - print({"num_params": sum(x.numel() for x in train_state.model.parameters())}) - save_code_and_config(config) - if config.ema: - print('Setup EMA') - ema_helper = EMAHelper(mu=config.ema_rate) - ema_helper.register(train_state.model) - - # Training Loop - for _iter_id in range(total_iters): - print (f"[Rank {RANK}, World Size {WORLD_SIZE}]: Epoch {_iter_id * train_epochs_per_iter}") - - ############ Train Iter - if RANK == 0: - print("TRAIN") - train_state.model.train() - for set_name, batch, global_batch_size in train_loader: - metrics = train_batch(config, train_state, batch, global_batch_size, rank=RANK, world_size=WORLD_SIZE) - - if RANK == 0 and metrics is not None: - #print(metrics, train_state.step) - progress_bar.update(train_state.step - progress_bar.n) # type: ignore - if config.ema: - ema_helper.update(train_state.model) - - if _iter_id >= config.min_eval_interval: - ############ Evaluation - if RANK == 0: - print("EVALUATE") - if config.ema: - print("SWITCH TO EMA") - train_state_eval = copy.deepcopy(train_state) - train_state_eval.model = ema_helper.ema_copy(train_state_eval.model) - else: - train_state_eval = train_state - train_state_eval.model.eval() - metrics = evaluate(config, - train_state_eval, - eval_loader, - eval_metadata, - evaluators, - rank=RANK, - world_size=WORLD_SIZE, - cpu_group=CPU_PROCESS_GROUP) - - if RANK == 0 and metrics is not None: - print(metrics, train_state.step) - - ############ Checkpointing - if RANK == 0: - print("SAVE CHECKPOINT") - if RANK == 0 and (config.checkpoint_every_eval or (_iter_id == total_iters - 1)): - save_train_state(config, train_state_eval) - - if config.ema: - del train_state_eval - - # finalize - if dist.is_initialized(): - dist.destroy_process_group() - - - -if __name__ == "__main__": - launch() diff --git a/TRM/pretrain-no-eval.py b/TRM/pretrain-no-eval.py deleted file mode 100644 index 524d521..0000000 --- a/TRM/pretrain-no-eval.py +++ /dev/null @@ -1,639 +0,0 @@ -from typing import Optional, Any, Sequence, List -from dataclasses import dataclass -import os -import math -import yaml -import shutil -import copy - -import torch -import torch.distributed as dist -from torch import nn -from torch.utils.data import DataLoader - -import tqdm -#import wandb -import coolname -import hydra -import pydantic -from omegaconf import DictConfig -from adam_atan2_pytorch import AdamAtan2 - -from puzzle_dataset import PuzzleDataset, PuzzleDatasetConfig, PuzzleDatasetMetadata -from utils.functions import load_model_class, get_model_source_path -from models.sparse_embedding import CastedSparseEmbeddingSignSGD_Distributed -from models.ema import EMAHelper - - -class LossConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') - name: str - - -class ArchConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra='allow') - name: str - loss: LossConfig - - -class EvaluatorConfig(pydantic.BaseModel): - model_config = pydantic.ConfigDict(extra="allow") - name: str - - -class PretrainConfig(pydantic.BaseModel): - # Config - arch: ArchConfig - # Data - data_paths: List[str] - data_paths_test: List[str] = [] - # Evaluators - evaluators: List[EvaluatorConfig] = [] - - # Hyperparams - global_batch_size: int - epochs: int - - lr: float - lr_min_ratio: float - lr_warmup_steps: int - - weight_decay: float - beta1: float - beta2: float - - # Puzzle embedding - puzzle_emb_lr: float - puzzle_emb_weight_decay: float - - # Names - project_name: Optional[str] = None - run_name: Optional[str] = None - load_checkpoint: Optional[str] = None - checkpoint_path: Optional[str] = None - - # Extras - seed: int = 0 - checkpoint_every_eval: bool = False - eval_interval: Optional[int] = None - min_eval_interval: Optional[int] = 0 # when to start eval - eval_save_outputs: List[str] = [] - - ema: bool = False # use Exponential-Moving-Average - ema_rate: float = 0.999 # EMA-rate - freeze_weights: bool = False # If True, freeze weights and only learn the embeddings - -@dataclass -class TrainState: - model: nn.Module - optimizers: Sequence[torch.optim.Optimizer] - optimizer_lrs: Sequence[float] - carry: Any - - step: int - total_steps: int - - -def create_dataloader(config: PretrainConfig, split: str, rank: int, world_size: int, **kwargs): - dataset = PuzzleDataset(PuzzleDatasetConfig( - seed=config.seed, - dataset_paths=config.data_paths_test if len(config.data_paths_test)>0 and split=="test" else config.data_paths, - rank=rank, - num_replicas=world_size, - **kwargs - ), split=split) - dataloader = DataLoader( - dataset, - batch_size=None, - num_workers=1, - prefetch_factor=8, - pin_memory=True, - persistent_workers=True - ) - return dataloader, dataset.metadata - - -def create_model(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, rank: int, world_size: int): - model_cfg = dict( - **config.arch.__pydantic_extra__, # type: ignore - batch_size=config.global_batch_size // world_size, - vocab_size=train_metadata.vocab_size, - seq_len=train_metadata.seq_len, - num_puzzle_identifiers=train_metadata.num_puzzle_identifiers, - causal=False # Non-autoregressive - ) - - # Instantiate model with loss head - model_cls = load_model_class(config.arch.name) - loss_head_cls = load_model_class(config.arch.loss.name) - - with torch.device("cuda"): - model: nn.Module = model_cls(model_cfg) - print(model) - model = loss_head_cls(model, **config.arch.loss.__pydantic_extra__) # type: ignore - if "DISABLE_COMPILE" not in os.environ: - model = torch.compile(model) # type: ignore - - # Load checkpoint - if rank == 0: - load_checkpoint(model, config) - - # Broadcast parameters from rank 0 - if world_size > 1: - with torch.no_grad(): - for param in list(model.parameters()) + list(model.buffers()): - dist.broadcast(param, src=0) - - # Optimizers and lr - if config.arch.puzzle_emb_ndim == 0: - optimizers = [ - AdamAtan2( - model.parameters(), - lr=0.0001, # Needs to be set by scheduler - weight_decay=config.weight_decay, - betas=(config.beta1, config.beta2) - ) - ] - optimizer_lrs = [ - config.lr - ] - elif config.freeze_weights: - optimizers = [ - CastedSparseEmbeddingSignSGD_Distributed( - model.model.puzzle_emb.buffers(), # type: ignore - lr=0, # Needs to be set by scheduler - weight_decay=config.puzzle_emb_weight_decay, - world_size=world_size - ) - ] - optimizer_lrs = [ - config.puzzle_emb_lr - ] - else: - optimizers = [ - CastedSparseEmbeddingSignSGD_Distributed( - model.model.puzzle_emb.buffers(), # type: ignore - lr=0, # Needs to be set by scheduler - weight_decay=config.puzzle_emb_weight_decay, - world_size=world_size - ), - AdamAtan2( - model.parameters(), - lr=0.001, # Needs to be set by scheduler - weight_decay=config.weight_decay, - betas=(config.beta1, config.beta2) - ) - ] - optimizer_lrs = [ - config.puzzle_emb_lr, - config.lr - ] - - return model, optimizers, optimizer_lrs - -def mix_weights_direct(device, alpha, net, nets): - sd = [] - for i in range(len(nets)): - sd += [nets[i].state_dict()] - sd_alpha = {} - for k in sd[0].keys(): - comb_net = alpha[0]*sd[0][k].to(device) - for i in range(1,len(nets)): - comb_net += alpha[i]*sd[i][k].to(device) - sd_alpha[k] = comb_net - net.load_state_dict(sd_alpha) - return net - -def cosine_schedule_with_warmup_lr_lambda( - current_step: int, *, base_lr: float, num_warmup_steps: int, num_training_steps: int, min_ratio: float = 0.0, num_cycles: float = 0.5 -): - if current_step < num_warmup_steps: - return base_lr * float(current_step) / float(max(1, num_warmup_steps)) - - progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) - return base_lr * (min_ratio + max(0.0, (1 - min_ratio) * 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)))) - - -def init_train_state(config: PretrainConfig, train_metadata: PuzzleDatasetMetadata, rank: int, world_size: int): - # Estimated total training steps - total_steps = int(config.epochs * train_metadata.total_groups * train_metadata.mean_puzzle_examples / config.global_batch_size) - - # Model - model, optimizers, optimizer_lrs = create_model(config, train_metadata, rank=rank, world_size=world_size) - - return TrainState( - step=0, - total_steps=total_steps, - - model=model, - optimizers=optimizers, - optimizer_lrs=optimizer_lrs, - carry=None - ) - - -def save_train_state(config: PretrainConfig, train_state: TrainState): - # FIXME: Only saved model. - if config.checkpoint_path is None: - return - - os.makedirs(config.checkpoint_path, exist_ok=True) - torch.save(train_state.model.state_dict(), os.path.join(config.checkpoint_path, f"step_{train_state.step}")) - - -def load_checkpoint(model: nn.Module, config: PretrainConfig): - if config.load_checkpoint is not None: - print(f"Loading checkpoint {config.load_checkpoint}") - - # Load state dict - state_dict = torch.load(config.load_checkpoint, map_location="cuda") - - # Resize and reset puzzle emb if needed - puzzle_emb_name = "_orig_mod.model.inner.puzzle_emb.weights" - expected_shape: torch.Size = model.model.puzzle_emb.weights.shape # type: ignore - if puzzle_emb_name in state_dict: - puzzle_emb = state_dict[puzzle_emb_name] - if puzzle_emb.shape != expected_shape: - print(f"Resetting puzzle embedding as shape is different. Found {puzzle_emb.shape}, Expected {expected_shape}") - # Re-initialize using mean - state_dict[puzzle_emb_name] = ( - torch.mean(puzzle_emb, dim=0, keepdim=True).expand(expected_shape).contiguous() - ) - model.load_state_dict(state_dict, assign=True) - - -def compute_lr(base_lr: float, config: PretrainConfig, train_state: TrainState): - return cosine_schedule_with_warmup_lr_lambda( - current_step=train_state.step, - base_lr=base_lr, - num_warmup_steps=round(config.lr_warmup_steps), - num_training_steps=train_state.total_steps, - min_ratio=config.lr_min_ratio - ) - - - -def create_evaluators(config: PretrainConfig, eval_metadata: PuzzleDatasetMetadata) -> List[Any]: - data_paths =config.data_paths_test if len(config.data_paths_test)>0 else config.data_paths - # Initialize evaluators - evaluators = [] - for cfg in config.evaluators: - for data_path in data_paths: - cls = load_model_class(cfg.name, "evaluators.")( - data_path=data_path, eval_metadata=eval_metadata, **cfg.__pydantic_extra__ - ) # type: ignore - evaluators.append(cls) - - return evaluators - -def train_batch(config: PretrainConfig, train_state: TrainState, batch: Any, global_batch_size: int, rank: int, world_size: int): - train_state.step += 1 - if train_state.step > train_state.total_steps: # At most train_total_steps - return - - # To device - batch = {k: v.cuda() for k, v in batch.items()} - - # Init carry if it is None - if train_state.carry is None: - with torch.device("cuda"): - train_state.carry = train_state.model.initial_carry(batch) # type: ignore - - # Forward - train_state.carry, loss, metrics, _, _ = train_state.model(carry=train_state.carry, batch=batch, return_keys=[]) - - ((1 / global_batch_size) * loss).backward() - - # Allreduce - if world_size > 1: - for param in train_state.model.parameters(): - if param.grad is not None: - dist.all_reduce(param.grad) - - # Apply optimizer - lr_this_step = None - for optim, base_lr in zip(train_state.optimizers, train_state.optimizer_lrs): - lr_this_step = compute_lr(base_lr, config, train_state) - - for param_group in optim.param_groups: - param_group['lr'] = lr_this_step - - optim.step() - optim.zero_grad() - - # Reduce metrics - if len(metrics): - assert not any(v.requires_grad for v in metrics.values()) - - metric_keys = list(sorted(metrics.keys())) # Sort keys to guarantee all processes use the same order. - # Reduce and reconstruct - metric_values = torch.stack([metrics[k] for k in metric_keys]) - if world_size > 1: - dist.reduce(metric_values, dst=0) - - if rank == 0: - metric_values = metric_values.cpu().numpy() - reduced_metrics = {k: metric_values[i] for i, k in enumerate(metric_keys)} - - # Postprocess - count = max(reduced_metrics["count"], 1) # Avoid NaNs - reduced_metrics = {f"train/{k}": v / (global_batch_size if k.endswith("loss") else count) for k, v in reduced_metrics.items()} - - reduced_metrics["train/lr"] = lr_this_step - return reduced_metrics - -def evaluate( - config: PretrainConfig, - train_state: TrainState, - eval_loader: torch.utils.data.DataLoader, - eval_metadata: PuzzleDatasetMetadata, - evaluators: List[Any], - rank: int, - world_size: int, - cpu_group: Optional[dist.ProcessGroup], -): - reduced_metrics = None - - with torch.inference_mode(): - return_keys = set(config.eval_save_outputs) - for evaluator in evaluators: - evaluator.begin_eval() - return_keys.update(evaluator.required_outputs) - - # Run evaluation - set_ids = {k: idx for idx, k in enumerate(eval_metadata.sets)} - - save_preds = {} - - metric_keys = [] - metric_values = None - - carry = None - processed_batches = 0 - - for set_name, batch, global_batch_size in eval_loader: - processed_batches += 1 - if rank == 0: - print(f"Processing batch {processed_batches}: {set_name}") - - # To device - batch = {k: v.cuda() for k, v in batch.items()} - with torch.device("cuda"): - carry = train_state.model.initial_carry(batch) # type: ignore - - # Forward - inference_steps = 0 - while True: - carry, loss, metrics, preds, all_finish = train_state.model( - carry=carry, batch=batch, return_keys=return_keys - ) - inference_steps += 1 - - if all_finish: - break - - if rank == 0: - print(f" Completed inference in {inference_steps} steps") - - for collection in (batch, preds): - for k, v in collection.items(): - if k in config.eval_save_outputs: - save_preds.setdefault(k, []) - save_preds[k].append(v.cpu()) # Move to CPU for saving GPU memory - - for evaluator in evaluators: - evaluator.update_batch(batch, preds) - - del carry, loss, preds, batch, all_finish - - # Aggregate metrics - set_id = set_ids[set_name] - - if metric_values is None: - metric_keys = list( - sorted(metrics.keys()) - ) # Sort keys to guarantee all processes use the same order. - metric_values = torch.zeros( - (len(set_ids), len(metrics.values())), dtype=torch.float32, device="cuda" - ) - - metric_values[set_id] += torch.stack([metrics[k] for k in metric_keys]) - - del metrics - - # concatenate save preds - save_preds = {k: torch.cat(v, dim=0) for k, v in save_preds.items()} - - # Save preds - if config.checkpoint_path is not None and len(save_preds): - # Each rank save predictions independently - os.makedirs(os.path.dirname(config.checkpoint_path), exist_ok=True) - torch.save( - save_preds, os.path.join(config.checkpoint_path, f"step_{train_state.step}_all_preds.{rank}") - ) - - del save_preds - - # Reduce to rank 0 - if metric_values is not None: - if world_size > 1: - dist.reduce(metric_values, dst=0) - - if rank == 0: - reduced_metrics = metric_values.cpu().numpy() - reduced_metrics = { - set_name: { - metric_name: reduced_metrics[set_id, metric_id] - for metric_id, metric_name in enumerate(metric_keys) - } - for set_id, set_name in enumerate(set_ids) - } - - # Postprocess - for set_name, m in reduced_metrics.items(): - count = m.pop("count") - reduced_metrics[set_name] = {k: v / count for k, v in m.items()} - - # Run evaluators - if rank == 0: - print(f"\nRunning {len(evaluators)} evaluator(s)...") - - for i, evaluator in enumerate(evaluators): - if rank == 0: - print(f"Running evaluator {i+1}/{len(evaluators)}: {evaluator.__class__.__name__}") - - # Path for saving - evaluator_save_path = None - if config.checkpoint_path is not None: - evaluator_save_path = os.path.join( - config.checkpoint_path, - f"evaluator_{evaluator.__class__.__name__}_step_{train_state.step}", - ) - os.makedirs(evaluator_save_path, exist_ok=True) - - # Run and log - metrics = evaluator.result(evaluator_save_path, rank=rank, world_size=world_size, group=cpu_group) - if rank == 0 and metrics is not None: - if reduced_metrics is None: - reduced_metrics = {} - - reduced_metrics.update(metrics) - print(f" Completed {evaluator.__class__.__name__}") - - if rank == 0: - print("All evaluators completed!") - - return reduced_metrics - -def save_code_and_config(config: PretrainConfig): - if config.checkpoint_path is None: - return - - os.makedirs(config.checkpoint_path, exist_ok=True) - - # Copy code - code_list = [ - get_model_source_path(config.arch.name), - get_model_source_path(config.arch.loss.name) - ] - for code_file in code_list: - if code_file is not None: - code_name = os.path.basename(code_file) - - shutil.copy(code_file, os.path.join(config.checkpoint_path, code_name)) - - # Dump config as yaml - config_file = os.path.join(config.checkpoint_path, "all_config.yaml") - with open(config_file, "wt") as f: - yaml.dump(config.model_dump(), f) - - # Log code - #wandb.run.log_code(config.checkpoint_path) - - -def load_synced_config(hydra_config: DictConfig, rank: int, world_size: int) -> PretrainConfig: - objects = [None] - if rank == 0: - config = PretrainConfig(**hydra_config) # type: ignore - - # Naming - if config.project_name is None: - config.project_name = f"{os.path.basename(config.data_paths[0]).capitalize()}-ACT-torch" - if config.run_name is None: - config.run_name = f"{config.arch.name.split('@')[-1]} {coolname.generate_slug(2)}" - if config.checkpoint_path is None: - config.checkpoint_path = os.path.join("checkpoints", config.project_name, config.run_name) - - objects = [config] - - if world_size > 1: - dist.broadcast_object_list(objects, src=0) - - return objects[0] # type: ignore - - -@hydra.main(config_path="config", config_name="cfg_pretrain", version_base=None) -def launch(hydra_config: DictConfig): - RANK = 0 - WORLD_SIZE = 1 - CPU_PROCESS_GROUP = None - - # Initialize distributed training if in distributed environment (e.g. torchrun) - if "LOCAL_RANK" in os.environ: - # Initialize distributed, default device and dtype - dist.init_process_group(backend="nccl") - - RANK = dist.get_rank() - WORLD_SIZE = dist.get_world_size() - - torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) - - # CPU GLOO process group - CPU_PROCESS_GROUP = dist.new_group(backend="gloo") - assert ( - dist.get_rank(CPU_PROCESS_GROUP) == RANK and dist.get_world_size(CPU_PROCESS_GROUP) == WORLD_SIZE - ) - - # Load sync'ed config - config = load_synced_config(hydra_config, rank=RANK, world_size=WORLD_SIZE) - - # Seed RNGs to ensure consistency - torch.random.manual_seed(config.seed + RANK) - - # Dataset - train_epochs_per_iter = config.eval_interval if config.eval_interval is not None else config.epochs - total_iters = config.epochs // train_epochs_per_iter - - assert config.epochs % train_epochs_per_iter == 0, "Eval interval must be a divisor of total epochs." - - train_loader, train_metadata = create_dataloader(config, "train", test_set_mode=False, epochs_per_iter=train_epochs_per_iter, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) - try: - eval_loader, eval_metadata = create_dataloader(config, "test", test_set_mode=True, epochs_per_iter=1, global_batch_size=config.global_batch_size, rank=RANK, world_size=WORLD_SIZE) - except: - print("NO EVAL DATA FOUND") - eval_loader = eval_metadata = None - - try: - evaluators = create_evaluators(config, eval_metadata) - except: - print("No evaluator found") - evaluators = [] - - # Train state - train_state = init_train_state(config, train_metadata, rank=RANK, world_size=WORLD_SIZE) - - # Progress bar and logger - progress_bar = None - ema_helper = None - if RANK == 0: - progress_bar = tqdm.tqdm(total=train_state.total_steps) - # wandb.init(project=config.project_name, name=config.run_name, config=config.model_dump(), settings=wandb.Settings(_disable_stats=True)) # type: ignore - # wandb.log({"num_params": sum(x.numel() for x in train_state.model.parameters())}, step=0) - save_code_and_config(config) - if config.ema: - print('Setup EMA') - ema_helper = EMAHelper(mu=config.ema_rate) - ema_helper.register(train_state.model) - - # Training Loop - for _iter_id in range(total_iters): - print (f"[Rank {RANK}, World Size {WORLD_SIZE}]: Epoch {_iter_id * train_epochs_per_iter}") - - ############ Train Iter - if RANK == 0: - print("TRAIN") - train_state.model.train() - for set_name, batch, global_batch_size in train_loader: - metrics = train_batch(config, train_state, batch, global_batch_size, rank=RANK, world_size=WORLD_SIZE) - - if RANK == 0 and metrics is not None: - #wandb.log(metrics, step=train_state.step) - progress_bar.update(train_state.step - progress_bar.n) # type: ignore - if config.ema: - ema_helper.update(train_state.model) - - if _iter_id >= config.min_eval_interval: - ############ Checkpointing - if RANK == 0: - print("SAVE CHECKPOINT") - if config.ema: - print("SWITCH TO EMA") - train_state_eval = copy.deepcopy(train_state) - train_state_eval.model = ema_helper.ema_copy(train_state_eval.model) - else: - train_state_eval = train_state - train_state_eval.model.eval() - if RANK == 0 and (config.checkpoint_every_eval or (_iter_id == total_iters - 1)): - save_train_state(config, train_state_eval) - - if config.ema: - del train_state_eval - - # finalize - if dist.is_initialized(): - dist.destroy_process_group() - #wandb.finish() - - -if __name__ == "__main__": - launch() diff --git a/arc_agi2_training_only/arc-agi_training_challenges.json b/arc_agi2_training_only/arc-agi_training_challenges.json new file mode 100644 index 0000000..15e27cc --- /dev/null +++ b/arc_agi2_training_only/arc-agi_training_challenges.json @@ -0,0 +1 @@ +{"00576224": {"train": [{"input": [[7, 9], [4, 3]], "output": [[7, 9, 7, 9, 7, 9], [4, 3, 4, 3, 4, 3], [9, 7, 9, 7, 9, 7], [3, 4, 3, 4, 3, 4], [7, 9, 7, 9, 7, 9], [4, 3, 4, 3, 4, 3]]}, {"input": [[8, 6], [6, 4]], "output": [[8, 6, 8, 6, 8, 6], [6, 4, 6, 4, 6, 4], [6, 8, 6, 8, 6, 8], [4, 6, 4, 6, 4, 6], [8, 6, 8, 6, 8, 6], [6, 4, 6, 4, 6, 4]]}], "test": [{"input": [[3, 2], [7, 8]]}]}, "007bbfb7": {"train": [{"input": [[6, 6, 0], [6, 0, 0], [0, 6, 6]], "output": [[6, 6, 0, 6, 6, 0, 0, 0, 0], [6, 0, 0, 6, 0, 0, 0, 0, 0], [0, 6, 6, 0, 6, 6, 0, 0, 0], [6, 6, 0, 0, 0, 0, 0, 0, 0], [6, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 6, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 6, 0, 6, 6, 0], [0, 0, 0, 6, 0, 0, 6, 0, 0], [0, 0, 0, 0, 6, 6, 0, 6, 6]]}, {"input": [[4, 0, 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[4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 7, 4], [4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 2, 4, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4], [2, 4, 4, 7, 4, 2, 4, 4, 4, 4, 4, 4, 4], [2, 4, 4, 4, 4, 8, 8, 4, 4, 5, 4, 4, 4], [4, 2, 4, 4, 8, 4, 4, 8, 4, 4, 4, 4, 4], [4, 4, 2, 8, 4, 4, 4, 4, 8, 4, 4, 4, 4], [4, 4, 4, 8, 4, 4, 4, 4, 3, 3, 4, 4, 4], [4, 4, 4, 4, 8, 4, 4, 3, 4, 4, 3, 4, 4], [4, 5, 4, 4, 4, 8, 3, 4, 4, 4, 4, 3, 4], [4, 4, 7, 4, 4, 4, 3, 4, 4, 5, 4, 3, 4], [4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 4, 4]], "output": [[4, 4, 3, 3, 4, 4], [4, 3, 4, 4, 3, 4], [3, 4, 4, 4, 4, 3], [3, 4, 4, 4, 4, 3], [4, 3, 4, 4, 3, 4], [4, 4, 3, 3, 4, 4], [4, 4, 8, 8, 4, 4], [4, 8, 4, 4, 8, 4], [8, 4, 4, 4, 4, 8], [8, 4, 4, 4, 4, 8], [4, 8, 4, 4, 8, 4], [4, 4, 8, 8, 4, 4], [4, 4, 2, 2, 4, 4], [4, 2, 4, 4, 2, 4], [2, 4, 4, 4, 4, 2], [2, 4, 4, 4, 4, 2], [4, 2, 4, 4, 2, 4], [4, 4, 2, 2, 4, 4]]}, {"input": [[8, 8, 8, 8, 8, 8, 8, 8, 5, 8], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 4, 4, 8, 3, 3, 8, 8], [8, 8, 4, 8, 8, 4, 8, 8, 3, 8], [8, 4, 2, 8, 3, 8, 4, 8, 8, 3], [8, 4, 8, 8, 3, 8, 4, 8, 8, 3], [8, 8, 4, 8, 8, 4, 8, 8, 3, 8], [8, 8, 8, 4, 4, 8, 3, 3, 8, 8], [8, 5, 8, 8, 8, 8, 8, 8, 8, 8], [8, 8, 8, 8, 8, 8, 8, 2, 8, 8]], "output": [[8, 8, 4, 4, 8, 8], [8, 4, 8, 8, 4, 8], [4, 8, 8, 8, 8, 4], [4, 8, 8, 8, 8, 4], [8, 4, 8, 8, 4, 8], [8, 8, 4, 4, 8, 8], [8, 8, 3, 3, 8, 8], [8, 3, 8, 8, 3, 8], [3, 8, 8, 8, 8, 3], [3, 8, 8, 8, 8, 3], [8, 3, 8, 8, 3, 8], [8, 8, 3, 3, 8, 8]]}], "test": [{"input": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1], [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 4, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 8, 7, 7, 8, 1, 1, 4, 1, 1, 1, 1], [1, 1, 1, 5, 1, 1, 3, 8, 8, 7, 1, 1, 1, 4, 1, 1, 1], [1, 1, 1, 1, 1, 3, 1, 1, 4, 1, 7, 1, 1, 4, 1, 1, 1], [1, 1, 1, 1, 3, 7, 1, 1, 1, 4, 7, 1, 4, 1, 1, 1, 1], [1, 1, 1, 1, 3, 1, 7, 1, 1, 3, 4, 4, 1, 1, 1, 1, 5], [1, 1, 1, 1, 1, 3, 1, 7, 3, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1], [5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}]}, "662c240a": {"train": [{"input": [[2, 2, 2], [2, 2, 3], [2, 3, 3], [5, 7, 7], [7, 5, 5], [7, 5, 5], [8, 8, 1], [1, 8, 1], [1, 8, 1]], "output": [[8, 8, 1], [1, 8, 1], [1, 8, 1]]}, {"input": [[1, 5, 5], [5, 1, 1], [5, 1, 1], [3, 3, 3], [3, 6, 3], [3, 6, 6], [7, 7, 7], [7, 2, 2], [7, 2, 2]], "output": [[3, 3, 3], [3, 6, 3], [3, 6, 6]]}, {"input": [[8, 8, 4], [4, 4, 4], [4, 4, 8], [1, 1, 3], [1, 3, 3], [3, 3, 1], [6, 2, 2], [2, 2, 2], [2, 2, 6]], "output": [[8, 8, 4], [4, 4, 4], [4, 4, 8]]}, {"input": [[8, 9, 8], [9, 8, 8], [8, 8, 8], [2, 2, 1], [2, 2, 1], [1, 1, 2], [4, 4, 4], [4, 4, 3], [3, 3, 3]], "output": [[4, 4, 4], [4, 4, 3], [3, 3, 3]]}], "test": [{"input": [[5, 4, 4], [4, 5, 4], [4, 5, 4], [3, 3, 2], [3, 3, 2], [2, 2, 3], [1, 1, 1], [1, 8, 8], [1, 8, 8]]}]}, "668eec9a": {"train": [{"input": [[7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 9, 7, 7, 7, 3, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 9, 7, 7, 7, 3, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 1, 7, 9, 7, 7, 7, 3, 7, 7, 7], [7, 7, 7, 7, 7, 1, 7, 7, 7, 9, 7, 7, 7, 3, 7, 7], [7, 7, 7, 7, 1, 7, 7, 7, 7, 7, 9, 7, 7, 7, 3, 7], [7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 9, 7, 7, 7, 3]], "output": 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1, 1, 1, 4, 1, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]]}, {"input": [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 3, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]], "output": [[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 2, 2, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 1, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 1, 1, 1, 1, 2, 1, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 1, 1, 1, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]}], "test": [{"input": [[4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4], [4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 1, 1, 4, 4, 4, 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3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 4, 2, 2, 2, 2, 2, 4, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 4, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 4, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 2, 2, 4, 2, 2, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8], [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8], [1, 1, 1, 1, 4, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 4, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], [1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]]}]}, "de493100": {"train": [{"input": [[4, 3, 3, 8, 8, 8, 4, 4, 2, 3, 8, 2, 6, 2, 2, 6, 6, 2, 2, 6, 2, 8, 3, 2, 4, 4, 8, 8, 8, 3], [4, 3, 8, 8, 8, 8, 4, 4, 3, 8, 2, 8, 9, 6, 6, 6, 6, 6, 6, 9, 8, 2, 8, 3, 4, 4, 8, 8, 8, 8], [8, 8, 3, 3, 4, 4, 8, 8, 3, 8, 8, 3, 2, 6, 9, 6, 6, 9, 6, 2, 3, 8, 8, 3, 8, 8, 4, 4, 3, 3], [8, 3, 4, 4, 4, 4, 8, 8, 8, 3, 3, 2, 6, 6, 2, 9, 9, 2, 6, 6, 2, 3, 3, 8, 8, 8, 4, 4, 4, 4], [5, 9, 4, 4, 4, 3, 8, 8, 6, 9, 2, 6, 6, 2, 9, 8, 8, 9, 2, 6, 6, 2, 9, 6, 8, 8, 3, 4, 4, 4], [9, 5, 5, 4, 4, 3, 8, 3, 2, 6, 6, 6, 8, 6, 8, 9, 9, 8, 6, 8, 6, 6, 6, 2, 3, 8, 3, 4, 4, 5], [4, 5, 5, 9, 3, 8, 3, 3, 2, 6, 9, 2, 8, 6, 6, 2, 2, 6, 6, 8, 2, 9, 6, 2, 3, 3, 8, 3, 9, 5], [4, 4, 9, 5, 8, 8, 4, 4, 6, 6, 6, 9, 6, 8, 8, 6, 6, 8, 8, 6, 9, 6, 6, 6, 4, 4, 8, 8, 5, 9], [2, 3, 3, 8, 6, 2, 2, 6, 9, 9, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 9, 9, 6, 2, 2, 6, 8, 3], [3, 8, 8, 3, 9, 6, 6, 6, 9, 5, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 5, 9, 6, 6, 6, 9, 3, 8], [8, 2, 8, 3, 2, 6, 9, 6, 4, 8, 5, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5, 8, 4, 6, 9, 6, 2, 3, 8], [2, 8, 3, 2, 6, 6, 2, 9, 8, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 8, 9, 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9]]}, {"input": [[3, 3, 2, 2, 8, 9, 9, 8, 8, 5, 5, 5, 1, 3, 3, 1, 1, 3, 3, 1, 5, 7, 7, 7, 7, 7, 9, 8, 2, 2], [1, 1, 2, 1, 9, 8, 8, 9, 5, 2, 5, 5, 3, 1, 1, 9, 9, 1, 1, 3, 5, 7, 7, 7, 7, 7, 8, 9, 1, 2], [2, 2, 1, 3, 9, 8, 8, 9, 8, 5, 2, 5, 3, 1, 9, 2, 2, 9, 1, 3, 5, 7, 7, 7, 7, 7, 8, 9, 3, 1], [2, 9, 1, 3, 8, 9, 9, 8, 5, 8, 5, 8, 1, 9, 1, 9, 9, 1, 9, 1, 8, 7, 7, 7, 7, 7, 9, 8, 3, 1], [8, 3, 8, 1, 3, 3, 1, 2, 1, 3, 3, 1, 9, 4, 9, 8, 8, 9, 4, 9, 1, 7, 7, 7, 7, 7, 3, 3, 1, 8], [3, 8, 3, 8, 1, 1, 2, 2, 3, 1, 1, 9, 2, 8, 8, 9, 9, 8, 8, 2, 9, 7, 7, 7, 7, 7, 1, 1, 8, 3], [8, 3, 8, 3, 9, 2, 1, 3, 3, 1, 9, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 7, 7, 7, 7, 7, 2, 9, 3, 8], [1, 8, 3, 8, 2, 2, 1, 3, 1, 9, 2, 9, 9, 9, 2, 9, 9, 2, 9, 9, 9, 2, 9, 1, 3, 1, 2, 2, 8, 3], [8, 5, 8, 5, 1, 3, 3, 1, 3, 2, 6, 2, 9, 9, 6, 9, 9, 6, 9, 9, 2, 6, 2, 3, 1, 3, 3, 1, 5, 8], [5, 2, 5, 8, 3, 1, 1, 9, 1, 1, 2, 2, 9, 9, 4, 6, 6, 4, 9, 9, 2, 2, 1, 1, 9, 1, 1, 3, 8, 5], [5, 5, 2, 5, 3, 1, 9, 2, 6, 3, 1, 2, 6, 4, 9, 9, 9, 9, 4, 6, 2, 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9, 4, 6, 2, 1, 3, 6, 2, 9, 1, 3, 5, 2], [5, 2, 5, 8, 3, 1, 1, 9, 1, 1, 2, 2, 9, 9, 4, 6, 6, 4, 9, 9, 2, 2, 1, 1, 9, 1, 1, 3, 8, 5], [8, 5, 8, 5, 1, 3, 3, 1, 3, 2, 6, 2, 9, 9, 6, 9, 9, 6, 9, 9, 2, 6, 2, 3, 1, 3, 3, 1, 5, 8], [1, 8, 3, 8, 2, 2, 1, 3, 1, 9, 2, 9, 9, 9, 2, 9, 9, 2, 9, 9, 9, 2, 9, 1, 3, 1, 2, 2, 8, 3], [8, 3, 8, 3, 9, 2, 1, 3, 3, 1, 9, 1, 9, 9, 8, 4, 4, 8, 9, 9, 1, 9, 1, 3, 3, 1, 2, 9, 3, 8], [3, 8, 3, 8, 1, 1, 2, 2, 3, 1, 1, 9, 2, 8, 8, 9, 9, 8, 8, 2, 9, 1, 1, 3, 2, 2, 1, 1, 8, 3], [8, 3, 8, 1, 3, 3, 1, 2, 1, 3, 3, 1, 9, 4, 9, 8, 8, 9, 4, 9, 1, 3, 3, 1, 2, 1, 3, 3, 1, 8], [2, 9, 1, 3, 8, 9, 9, 8, 5, 8, 5, 8, 1, 9, 1, 9, 9, 1, 9, 1, 8, 5, 8, 5, 8, 9, 9, 8, 3, 1], [2, 2, 1, 3, 9, 8, 8, 9, 8, 5, 2, 5, 3, 1, 9, 2, 2, 9, 1, 3, 5, 2, 5, 8, 9, 8, 8, 9, 3, 1]], "output": [[5, 5, 8, 8, 9], [5, 2, 5, 9, 8], [2, 5, 8, 9, 8], [5, 8, 5, 8, 9], [3, 3, 1, 2, 1], [1, 1, 3, 2, 2], [9, 1, 3, 3, 1]]}, {"input": [[3, 1, 8, 8, 6, 8, 8, 6, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 6, 8, 8, 6, 8, 8], [2, 3, 8, 3, 8, 6, 1, 8, 8, 9, 9, 8, 2, 9, 8, 8, 8, 8, 9, 2, 8, 9, 9, 8, 8, 1, 6, 8, 3, 8], [1, 8, 3, 1, 8, 1, 6, 8, 4, 8, 9, 8, 9, 8, 9, 8, 8, 9, 8, 9, 8, 9, 8, 4, 8, 6, 1, 8, 1, 3], [8, 2, 2, 3, 6, 8, 8, 6, 8, 4, 8, 8, 9, 8, 9, 9, 9, 9, 8, 9, 8, 8, 4, 8, 6, 8, 8, 6, 3, 2], [3, 8, 8, 3, 3, 1, 3, 8, 9, 2, 9, 9, 9, 8, 9, 8, 8, 9, 8, 9, 9, 9, 2, 9, 8, 3, 1, 3, 3, 8], [8, 3, 1, 8, 2, 3, 8, 8, 9, 9, 8, 8, 8, 4, 8, 9, 9, 8, 4, 8, 8, 8, 9, 9, 8, 8, 3, 2, 8, 1], [8, 1, 3, 8, 2, 8, 3, 1, 9, 8, 9, 9, 9, 5, 4, 8, 8, 4, 5, 9, 9, 9, 8, 9, 1, 3, 8, 2, 8, 3], [3, 8, 8, 3, 8, 1, 2, 3, 9, 8, 8, 9, 5, 9, 8, 9, 9, 8, 9, 5, 9, 8, 8, 9, 3, 2, 1, 8, 3, 8], [8, 8, 4, 8, 9, 9, 9, 9, 2, 9, 8, 8, 5, 2, 5, 1, 1, 5, 2, 5, 8, 8, 9, 2, 9, 9, 9, 9, 8, 4], [8, 9, 8, 4, 2, 9, 8, 8, 8, 2, 8, 3, 2, 5, 4, 5, 5, 4, 5, 2, 3, 8, 2, 8, 8, 8, 9, 2, 4, 8], [8, 9, 9, 8, 9, 8, 9, 8, 8, 8, 2, 9, 5, 4, 5, 2, 2, 5, 4, 5, 9, 2, 8, 8, 8, 9, 8, 9, 8, 9], [9, 8, 8, 8, 9, 8, 9, 9, 8, 8, 8, 2, 1, 5, 2, 5, 5, 2, 5, 1, 2, 8, 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8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [0, 8, 2, 8, 2, 3, 0, 8, 2, 8, 2, 3, 0, 8, 2, 8, 2, 3, 0, 8, 2, 8, 2, 3, 0, 8, 2, 8, 2], [0, 8, 8, 8, 8, 3, 0, 8, 8, 8, 8, 3, 0, 8, 8, 8, 8, 3, 0, 8, 8, 8, 8, 3, 0, 8, 8, 8, 8], [8, 8, 8, 8, 4, 3, 8, 8, 8, 8, 4, 3, 8, 8, 8, 8, 4, 3, 8, 8, 8, 8, 4, 3, 8, 8, 8, 8, 4]], "output": [[8, 8, 4, 8, 8], [8, 8, 8, 8, 8], [0, 8, 1, 8, 2], [1, 8, 8, 8, 8], [8, 8, 8, 8, 4]]}, {"input": [[0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 2, 8, 4, 3, 0, 8, 8, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [8, 8, 0, 8, 2, 3, 8, 8, 8, 8, 8, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4, 3, 0, 8, 2, 8, 4], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8, 3, 4, 8, 6, 8, 8], [8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8], [8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2, 3, 8, 8, 0, 8, 2]], "output": [[0, 8, 1, 8, 4], [8, 8, 8, 8, 8], [4, 8, 6, 8, 8], [8, 8, 8, 8, 8], [8, 8, 1, 8, 1]]}, {"input": [[0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8], [4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8], [8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8], [8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8], [0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8], [4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8], [8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8], [8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8], [0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8], [4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8], [8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8], [8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8], [0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8], [4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8], [8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8], [8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8], [0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 8, 8, 8, 3, 0, 8, 4, 8, 8, 3, 0, 8, 4, 8, 8], [4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8, 3, 4, 8, 8, 8, 8], [8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 8, 8, 8, 8, 3, 8, 2, 8, 8, 8, 3, 8, 2, 8, 8, 8], [8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 8, 8, 3, 8, 8, 8, 2, 8, 3, 8, 8, 8, 2, 8], [0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0, 3, 0, 8, 8, 8, 0]], "output": [[0, 8, 1, 8, 8], [1, 8, 8, 8, 8], [8, 1, 8, 8, 8], [8, 8, 8, 1, 8], [0, 8, 8, 8, 0]]}], "test": [{"input": [[2, 8, 2, 8, 8, 3, 2, 8, 2, 8, 8, 3, 2, 8, 2, 8, 8, 3, 2, 8, 2, 8, 8, 3, 2, 8, 2, 8, 8], [8, 0, 0, 8, 6, 3, 8, 0, 0, 8, 6, 3, 8, 0, 0, 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4]]}]}} \ No newline at end of file diff --git a/generate_summaries.py b/generate_summaries.py new file mode 100644 index 0000000..4a72a7d --- /dev/null +++ b/generate_summaries.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +""" +Generate puzzle descriptions using SDG summary_v2.md prompt with Gemini 3 Flash. + +This replaces the JSON trace generation method with a direct SDG approach. +Output: .nvarc.md files in the traces/ directory +""" +import argparse +import json +import os +import sys +import time +import google.generativeai as genai +import dotenv +from pathlib import Path + +dotenv.load_dotenv() +genai.configure(api_key=os.getenv("GEMINI_API_KEY")) + + +def format_grid(grid): + """Format grid as text.""" + return "\n".join(" ".join(str(c) for c in row) for row in grid) if grid else "[]" + + +def format_task(task_data, task_id): + """Format ARC task for the prompt.""" + lines = [f"Task ID: {task_id}", "\nTRAINING EXAMPLES:"] + for i, ex in enumerate(task_data.get("train", [])): + lines.extend([ + f"\nExample {i+1}:", + "Input:", + format_grid(ex["input"]), + "Output:", + format_grid(ex["output"]) + ]) + if task_data.get("test"): + lines.append("\nTEST EXAMPLES (outputs hidden):") + for i, ex in enumerate(task_data.get("test", [])): + lines.extend([ + f"\nTest {i+1}:", + "Input:", + format_grid(ex["input"]), + "(Output: hidden)" + ]) + return "\n".join(lines) + + +def read_summary_prompt(): + """Read the summary_v2.md prompt.""" + with open("SDG/prompts/summary_v2.md", 'r') as f: + return f.read() + + +def get_example_tasks(num_examples=2): + """Load a few example tasks for few-shot learning.""" + examples = [] + example_file = "arc_agi2_training_only/arc-agi_training_challenges.json" + + if not os.path.exists(example_file): + return "" + + with open(example_file) as f: + tasks = json.load(f) + + # Get a few simple example tasks + example_ids = list(tasks.keys())[:num_examples] + for task_id in example_ids: + puzzle_text = format_task(tasks[task_id], task_id) + examples.append(f"Example Task:\n{puzzle_text}") + + return "\n\n".join(examples) + + +def generate_summary(task_id, task_data, model_name="gemini-3-flash-preview", verbose=False): + """Generate a .nvarc.md summary using SDG summary_v2.md prompt.""" + prompt_template = read_summary_prompt() + puzzle_text = format_task(task_data, task_id) + examples = get_example_tasks(num_examples=2) + + # Fill in the prompt template + prompt = prompt_template.replace("{PUZZLE}", puzzle_text).replace("{EXAMPLES}", examples) + + try: + if verbose: + print(f"[{task_id}] Generating summary with {model_name}...", file=sys.stderr) + + model = genai.GenerativeModel(model_name) + response = model.generate_content(prompt) + + if not response or not hasattr(response, 'text'): + raise ValueError("No valid response from API") + + summary_text = response.text + + if verbose: + print(f"[{task_id}] Summary generated (length: {len(summary_text)} chars)", file=sys.stderr) + + return summary_text + + except Exception as e: + print(f"FAILED {task_id}: {e}", file=sys.stderr) + if verbose: + import traceback + traceback.print_exc() + return None + + +def generate_summaries(target_count=100, input_file="arc_agi2_training_only/arc-agi_training_challenges.json", + out_dir="traces", verbose=False): + """Generate summaries for multiple tasks.""" + os.makedirs(out_dir, exist_ok=True) + + with open(input_file) as f: + tasks = json.load(f) + + # Check existing files + existing = {f[:-10] for f in os.listdir(out_dir) if f.endswith('.nvarc.md')} + count = len(existing) + + if count >= target_count: + print(f"Goal met ({count}/{target_count}).") + return + + print(f"Generating {target_count - count} summaries...") + + for task_id, task_data in tasks.items(): + if count >= target_count: + break + if task_id in existing: + continue + + try: + summary = generate_summary(task_id, task_data, verbose=verbose) + + if summary is None: + print(f"Skipping {task_id}: Generation failed") + continue + + # Save as .nvarc.md + out_path = os.path.join(out_dir, f"{task_id}.nvarc.md") + with open(out_path, 'w') as f: + f.write(summary) + + print(f"Saved {task_id}.nvarc.md") + count += 1 + + # Rate limit + time.sleep(10) + + except Exception as e: + print(f"Error {task_id}: {e}", file=sys.stderr) + + +if __name__ == "__main__": + # Check API key + api_key = os.getenv("GEMINI_API_KEY") + if not api_key: + print("ERROR: GEMINI_API_KEY not found in environment. Check .env file.", file=sys.stderr) + sys.exit(1) + + parser = argparse.ArgumentParser( + description="Generate puzzle summaries using SDG summary_v2.md prompt with Gemini 3 Flash", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog="""Examples: + # Generate summary for a single task + python3 generate_summaries.py --task 007bbfb7 + + # Generate 100 summaries + python3 generate_summaries.py --batch 100 + + # Generate all tasks + python3 generate_summaries.py --all""" + ) + parser.add_argument("--task", help="Generate summary for single task ID") + parser.add_argument("--batch", type=int, default=100, metavar="N", help="Generate N summaries (default: 100)") + parser.add_argument("--all", action="store_true", help="Generate summaries for all tasks") + parser.add_argument("--input", default="arc_agi2_training_only/arc-agi_training_challenges.json", + help="Input challenges JSON file") + parser.add_argument("--output-dir", default="traces", help="Output directory for .nvarc.md files") + parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose output") + args = parser.parse_args() + + if args.task: + # Single task mode + with open(args.input) as f: + tasks = json.load(f) + + if args.task not in tasks: + print(f"ERROR: Task {args.task} not found in {args.input}", file=sys.stderr) + sys.exit(1) + + summary = generate_summary(args.task, tasks[args.task], verbose=args.verbose) + if summary: + os.makedirs(args.output_dir, exist_ok=True) + out_path = os.path.join(args.output_dir, f"{args.task}.nvarc.md") + with open(out_path, 'w') as f: + f.write(summary) + print(f"Saved {out_path}") + else: + print(f"Aborting: Generation failed for {args.task}", file=sys.stderr) + sys.exit(1) + else: + # Batch mode + target_count = len(json.load(open(args.input))) if args.all else args.batch + generate_summaries( + target_count=target_count, + input_file=args.input, + out_dir=args.output_dir, + verbose=args.verbose + ) diff --git a/nvarc_2025.pdf b/nvarc_2025.pdf deleted file mode 100644 index 9a37712..0000000 Binary files a/nvarc_2025.pdf and /dev/null differ diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..02cdbd0 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "trelis-nvarc" +version = "0.1.0" +description = "Reasoning trace generation and NVARC integration for ARC-AGI-2" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "google-generativeai", + "python-dotenv", + "numpy", + "matplotlib", + "tqdm", + "datasets", + "tree-sitter", + "tree-sitter-python", + "timeout-decorator", +] + +[tool.uv] +package = true diff --git a/run_nvarc_pipeline.py b/run_nvarc_pipeline.py new file mode 100644 index 0000000..290fa2a --- /dev/null +++ b/run_nvarc_pipeline.py @@ -0,0 +1,274 @@ +#!/usr/bin/env python3 +""" +Run the complete NVARC SDG pipeline end-to-end. + +Simplified pipeline using only SDG prompts: +1. Generate descriptions (.nvarc.md) using summary_v2.md with Gemini 3 Flash +2. [SKIPPED] Task mixing step (mix_v2.md) - we skip this +3. Generate input code using generate_puzzle_input.md with Gemini 3 Flash +4. Generate output code using generate_puzzle_output.md with Gemini 3 Flash +5. Execute input logic to generate input grids +6. Execute output logic to generate output grids +7. Make pairs from input/output grids +8. Build final dataset + +Usage: + python3 run_nvarc_pipeline.py --task-ids 007bbfb7,00d62c1b + python3 run_nvarc_pipeline.py --all-tasks --count 10 +""" +import argparse +import os +import sys +import subprocess +from pathlib import Path + + +def run_command(cmd, description, verbose=False): + """Run a shell command and handle errors.""" + print(f"\n{'='*60}") + print(f"{description}") + print(f"{'='*60}") + if verbose: + print(f"Command: {' '.join(cmd)}") + print() + + result = subprocess.run(cmd, capture_output=not verbose) + + if result.returncode != 0: + print(f"ERROR: {description} failed with exit code {result.returncode}", file=sys.stderr) + if not verbose and result.stderr: + print(result.stderr.decode(), file=sys.stderr) + return False + + print(f"āœ“ {description} completed successfully") + return True + + +def check_prerequisites(): + """Check that required files and directories exist.""" + required_files = [ + "SDG/prompts/summary_v2.md", + "SDG/prompts/generate_puzzle_input.md", + "SDG/prompts/generate_puzzle_output.md", + "SDG/scripts/generate_input_grids.py", + "SDG/scripts/generate_output_grids.py", + "SDG/scripts/make_pairs.py", + "SDG/scripts/build_datasets.py", + "arc_agi2_training_only/arc-agi_training_challenges.json", + "generate_summaries.py", + "seed_to_logic.py", + ] + + missing = [f for f in required_files if not os.path.exists(f)] + if missing: + print("ERROR: Missing required files:", file=sys.stderr) + for f in missing: + print(f" - {f}", file=sys.stderr) + return False + + return True + + +def run_pipeline(task_ids=None, all_tasks=False, num_tasks=0, num_grids=30, skip_steps=None, verbose=False): + """Run the complete NVARC SDG pipeline.""" + + skip_steps = skip_steps or [] + + # Step 1: Generate descriptions (.nvarc.md) using summary_v2.md with Gemini 3 Flash + if "descriptions" not in skip_steps: + cmd = ["python3", "generate_summaries.py"] + + if all_tasks: + cmd.extend(["--all"]) + if num_tasks > 0: + cmd.extend(["--batch", str(num_tasks)]) + elif task_ids: + # Process each task ID individually + for task_id in task_ids: + task_cmd = cmd + ["--task", task_id] + if verbose: + task_cmd.append("--verbose") + if not run_command(task_cmd, f"Step 1: Generate Description for {task_id} (summary_v2.md + Gemini 3 Flash)", verbose): + return False + # Skip to step 2 after processing all tasks + else: + # Default: process a few tasks + cmd.extend(["--batch", "3"]) + + if not task_ids: # Only run once if not processing specific tasks + if verbose: + cmd.append("--verbose") + if not run_command(cmd, "Step 1: Generate Descriptions (summary_v2.md + Gemini 3 Flash)", verbose): + return False + else: + print("\nā­ļø Skipping Step 1: Generate Descriptions") + + # Step 2: Task mixing - SKIPPED + print("\nā­ļø Skipping Step 2: Task Mixing (mix_v2.md)") + print(" We skip task mixing as per requirements") + + # Step 3: Generate input code using generate_puzzle_input.md with Gemini + # Step 4: Generate output code using generate_puzzle_output.md with Gemini + if "code_generation" not in skip_steps: + # Check if we have .nvarc.md files + nvarc_files = list(Path("traces").glob("*.nvarc.md")) if os.path.exists("traces") else [] + + if not nvarc_files: + print("\nāš ļø No .nvarc.md files found in traces/. Skipping code generation.") + print(" Run Step 1 first to generate descriptions.") + else: + cmd = ["python3", "seed_to_logic.py", "--batch", str(len(nvarc_files))] + if verbose: + cmd.append("--verbose") + + if not run_command(cmd, "Step 3-4: Generate Input/Output Code (Gemini 3 Flash)", verbose): + return False + else: + print("\nā­ļø Skipping Step 3-4: Generate Input/Output Code") + + # Step 5: Execute input logic to generate grids + if "input_grids" not in skip_steps: + # Check if we have logic files + logic_files = list(Path("logic").glob("*.py")) if os.path.exists("logic") else [] + + if not logic_files: + print("\nāš ļø No logic files found in logic/. Skipping input grid generation.") + print(" Run Steps 3-4 first to generate code.") + else: + # Create output directory structure + os.makedirs("synthetic/grids_arc_input", exist_ok=True) + + cmd = [ + "python3", "SDG/scripts/generate_input_grids.py", + "--inputs-mask", "logic/*.py", + "--grids-prefix", "synthetic/grids_arc_input", + "--num-grids", str(num_grids) + ] + + if not run_command(cmd, "Step 5: Execute Input Logic ā†’ Generate Input Grids", verbose): + return False + else: + print("\nā­ļø Skipping Step 5: Execute Input Logic") + + # Step 6: Execute output logic to generate output grids + if "output_grids" not in skip_steps: + if not os.path.exists("synthetic/grids_arc_input"): + print("\nāš ļø No input grids found. Skipping output grid generation.") + else: + cmd = [ + "python3", "SDG/scripts/generate_output_grids.py", + "--solutions-mask", "logic/*.py", + "--input-grids-prefix", "synthetic/grids_arc_input", + "--output-grids-prefix", "synthetic/grids_arc_output", + "--min-solutions-per-puzzle", "1" + ] + + if not run_command(cmd, "Step 6: Execute Output Logic ā†’ Generate Output Grids", verbose): + return False + else: + print("\nā­ļø Skipping Step 6: Execute Output Logic") + + # Step 7: Make pairs + if "pairs" not in skip_steps: + if not os.path.exists("synthetic/grids_arc_output"): + print("\nāš ļø No output grids found. Skipping pair generation.") + else: + cmd = [ + "python3", "SDG/scripts/make_pairs.py", + "--input-grids-prefix", "synthetic/grids_arc_input", + "--output-grids-mask", "synthetic/grids_arc_output/*/*.json", + "--output-prefix", "synthetic/pairs_arc", + "--min-majority-per-grid", "1", + "--min-pairs-per-puzzle", "5", + "--min-correct-solutions", "1" + ] + + if not run_command(cmd, "Step 7: Make Input/Output Pairs", verbose): + return False + else: + print("\nā­ļø Skipping Step 7: Make Pairs") + + # Step 8: Build dataset + if "dataset" not in skip_steps: + if not os.path.exists("synthetic/pairs_arc"): + print("\nāš ļø No pairs found. Skipping dataset building.") + else: + cmd = [ + "python3", "SDG/scripts/build_datasets.py", + "--input-prefix", "synthetic/pairs_arc", + "--output-dataset", "synthetic/arc_dataset.json", + "--augmentations", "dihedral", "color" + ] + + if not run_command(cmd, "Step 8: Build Final Dataset", verbose): + return False + else: + print("\nā­ļø Skipping Step 8: Build Dataset") + + print(f"\n{'='*60}") + print("āœ… NVARC Pipeline completed successfully!") + print(f"{'='*60}") + print("\nGenerated outputs:") + print(" - traces/ - Puzzle descriptions (.nvarc.md) from summary_v2.md") + print(" - logic/ - Generated Python code") + print(" - synthetic/grids_arc_input/ - Input grids") + print(" - synthetic/grids_arc_output/ - Output grids") + print(" - synthetic/pairs_arc/ - Input/output pairs") + print(" - synthetic/arc_dataset.json - Final dataset") + print("\nPipeline uses only SDG prompts:") + print(" - SDG/prompts/summary_v2.md") + print(" - SDG/prompts/generate_puzzle_input.md") + print(" - SDG/prompts/generate_puzzle_output.md") + return True + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Run the complete NVARC SDG pipeline (skipping task mixing)", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog="""Examples: + # Process specific tasks + python3 run_nvarc_pipeline.py --task-ids 007bbfb7,00d62c1b + + # Process 10 random tasks + python3 run_nvarc_pipeline.py --all-tasks --count 10 + + # Skip certain steps + python3 run_nvarc_pipeline.py --skip descriptions,code_generation + + # Generate more grids per task + python3 run_nvarc_pipeline.py --task-ids 007bbfb7 --num-grids 50 + """ + ) + parser.add_argument("--task-ids", help="Comma-separated task IDs (e.g., 007bbfb7,00d62c1b)") + parser.add_argument("--all-tasks", action="store_true", help="Process all tasks in ARC-AGI-2 training set") + parser.add_argument("--count", type=int, default=0, help="Number of tasks to process (with --all-tasks)") + parser.add_argument("--num-grids", type=int, default=30, help="Number of grids per task (default: 30)") + parser.add_argument("--skip", help="Comma-separated steps to skip: descriptions,code_generation,input_grids,output_grids,pairs,dataset") + parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose output") + args = parser.parse_args() + + # Check prerequisites + if not check_prerequisites(): + sys.exit(1) + + # Parse task IDs + task_ids = None + if args.task_ids: + task_ids = [tid.strip() for tid in args.task_ids.split(",")] + + # Parse skip steps + skip_steps = [] + if args.skip: + skip_steps = [s.strip() for s in args.skip.split(",")] + + # Run pipeline + success = run_pipeline( + task_ids=task_ids, + all_tasks=args.all_tasks, + num_tasks=args.count, + num_grids=args.num_grids, + skip_steps=skip_steps, + verbose=args.verbose + ) + sys.exit(0 if success else 1) diff --git a/seed_to_logic.py b/seed_to_logic.py new file mode 100644 index 0000000..cffa599 --- /dev/null +++ b/seed_to_logic.py @@ -0,0 +1,75 @@ +import os +import json +import time +import argparse +import google.generativeai as genai +import dotenv +from pathlib import Path + +dotenv.load_dotenv() +genai.configure(api_key=os.getenv("GEMINI_API_KEY")) + +def read_file(path): + with open(path, 'r') as f: return f.read() + +def save_file(path, content): + with open(path, 'w') as f: f.write(content) + +def extract_code(text): + import re + match = re.search(r'```python\s*(.*?)\s*```', text, re.DOTALL) + return match.group(1) if match else text + +def generate_logic(task_id, description, model_name="gemini-3-flash-preview"): + model = genai.GenerativeModel(model_name) + + # 1. Generate Input Code + input_prompt = read_file("SDG/prompts/generate_puzzle_input.md").replace("{PUZZLE}", description) + print(f"[{task_id}] Generating input logic...") + input_res = model.generate_content(input_prompt).text + input_code = extract_code(input_res) + + # 2. Generate Output Code + output_prompt = read_file("SDG/prompts/generate_puzzle_output.md").replace("{PUZZLE}", description).replace("{INPUT_CODE}", input_code) + print(f"[{task_id}] Generating output logic...") + output_res = model.generate_content(output_prompt).text + output_code = extract_code(output_res) + + # Combine (NVARC expects both in one file or handled by scripts) + # We'll save them as task_id.py containing both functions + full_code = f"{input_code}\n\n{output_code}" + return full_code + +def main(): + parser = argparse.ArgumentParser( + description="Generate Python logic from NVARC descriptions using Gemini 3 Flash", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog="Example:\n uv run seed_to_logic.py --batch 3" + ) + parser.add_argument("--batch", type=int, default=100, metavar="N", help="Target number of logic files (default: 100)") + parser.add_argument("--input-dir", default="traces", help="Directory with .nvarc.md files (default: traces)") + parser.add_argument("--output-dir", default="logic", help="Directory to save .py files (default: logic)") + args = parser.parse_args() + + os.makedirs(args.output_dir, exist_ok=True) + seeds = list(Path(args.input_dir).glob("*.nvarc.md")) + + count = 0 + for seed_path in seeds: + if count >= args.batch: break + task_id = seed_path.stem.split('.')[0] + out_path = os.path.join(args.output_dir, f"{task_id}.py") + + if os.path.exists(out_path): continue + + try: + desc = read_file(seed_path) + code = generate_logic(task_id, desc) + save_file(out_path, code) + count += 1 + time.sleep(10) # Rate limit + except Exception as e: + print(f"Error {task_id}: {e}") + +if __name__ == "__main__": + main() diff --git a/traces/007bbfb7.json b/traces/007bbfb7.json new file mode 100644 index 0000000..0840cdf --- /dev/null +++ b/traces/007bbfb7.json @@ -0,0 +1,34 @@ +{ + "task_id": "007bbfb7", + "trace_source": "llm", + "intended_model_family": "gemini-flash", + "execution_model": "gemini-3-flash-preview", + "observations": [ + "The input is a 3x3 grid containing a color and zeros.", + "The output is a 9x9 grid, which can be seen as a 3x3 tiling of 3x3 blocks.", + "Each 3x3 block in the output corresponds to a cell in the input grid.", + "If an input cell (i, j) has a non-zero value, the corresponding 3x3 block in the output (from row 3i to 3i+2 and column 3j to 3j+2) is an exact copy of the entire 3x3 input grid.", + "If an input cell (i, j) is zero, the corresponding 3x3 block in the output is filled entirely with zeros." + ], + "hypotheses": [ + "The output is generated by replacing every non-zero pixel in the 3x3 input with the 3x3 input grid itself, and replacing every zero pixel with a 3x3 block of zeros.", + "This is a specific form of a block-wise substitution or fractal-like expansion (Kronecker product-like) where the template is the input itself." + ], + "rejected_hypotheses": [ + { + "hypothesis": "The output is a simple 3x3 scaling (magnification) of the input.", + "reason": "Magnification would result in each pixel becoming a 3x3 block of its own color. In these examples, a single pixel is replaced by the pattern of the whole input grid." + } + ], + "selected_rule": "For each cell A[i][j] in the 3x3 input grid, the output block B[i][j] (at row 3i, col 3j) is: if A[i][j] != 0, B[i][j] = A; if A[i][j] == 0, B[i][j] = 0 (3x3).", + "step_plan": [ + "Identify the 3x3 input grid A.", + "Initialize a 9x9 output grid with zeros.", + "Iterate through each cell (i, j) of the input grid A.", + "If A[i][j] is not zero:", + "Copy the 3x3 input grid A into the output grid starting at position (3i, 3j).", + "If A[i][j] is zero, leave the 3x3 block at (3i, 3j) as zeros (which it already is by initialization)." + ], + "confidence": 0.9, + "output": "7 0 7 0 0 0 7 0 7\n7 0 7 0 0 0 7 0 7\n7 7 0 0 0 0 7 7 0\n7 0 7 0 0 0 7 0 7\n7 0 7 0 0 0 7 0 7\n7 7 0 0 0 0 7 7 0\n7 0 7 7 0 7 0 0 0\n7 0 7 7 0 7 0 0 0\n7 7 0 7 7 0 0 0 0" +} \ No newline at end of file diff --git a/traces/007bbfb7.nvarc.md b/traces/007bbfb7.nvarc.md new file mode 100644 index 0000000..c22b5f0 --- /dev/null +++ b/traces/007bbfb7.nvarc.md @@ -0,0 +1,21 @@ + +The output grid is constructed by creating a larger grid that is 3 times the size of the input in both dimensions. This larger grid is treated as a meta-grid with the same dimensions as the input. For each cell (r, c) in the input grid, if its color is not black (0), the corresponding block at (r, c) in the output meta-grid is a copy of the entire input grid. If the color is black, the corresponding block is a solid black grid of the same size as the input. + + +Input grids of varying sizes are provided. The transformation maintains relationships between input and output dimensions. + + +1. Determine the height (H) and width (W) of the input grid. +2. Create a new output grid of size (3*H) x (3*W), initialized with black (0). +3. Iterate through each cell of the input grid at row `r` and column `c`. +4. Check the color of the input cell `input[r][c]`. +5. If the color is not black (0), copy the entire input grid into the output grid starting at position `(r*H, c*W)`. +6. If the color is black (0), the corresponding block in the output grid remains black (as it was initialized). + + +The output grid dimensions are exactly 3 times the input grid dimensions in both height and width. + + +- color_mapping +- scaling + diff --git a/traces/00d62c1b.json b/traces/00d62c1b.json new file mode 100644 index 0000000..8cd30eb --- /dev/null +++ b/traces/00d62c1b.json @@ -0,0 +1,31 @@ +{ + "task_id": "00d62c1b", + "trace_source": "llm", + "intended_model_family": "gemini-flash", + "execution_model": "gemini-3-flash-preview", + "observations": [ + "The task involves filling enclosed areas of color 0 (background) with color 4 (yellow), where the boundaries are formed by color 3 (green).", + "An enclosed area is defined by 0-cells that cannot reach the edge of the grid moving only through other 0-cells in a 4-connected manner (up, down, left, right).", + "Examples show both single-cell holes and multi-cell rectangular or irregular holes being filled with 4." + ], + "hypotheses": [ + "Identify all 0-cells that are not connected to the grid boundary via other 0-cells. Change these 0-cells to 4.", + "Identify every cell with value 0 that is surrounded by value 3 in its direct 4-neighborhood. Change these cells to 4. (Rejected: Example 3 and 4 show larger areas are filled, not just single cells).", + "Perform a flood fill from the edges of the grid starting at all 0-cells on the boundary. Any 0-cell not reached by the flood fill and not colored 3 should be colored 4." + ], + "rejected_hypotheses": [ + { + "hypothesis": "Identify every cell with value 0 that is surrounded by value 3 in its direct 4-neighborhood. Change these cells to 4.", + "reason": "Examples 3 and 4 show large internal rectangular holes are filled, which contains 0-cells that are adjacent to other 0-cells, not just 3s." + } + ], + "selected_rule": "Identify all 0-cells that are trapped within boundaries of 3s and cannot reach the grid edge. Change these 0-cells to 4.", + "step_plan": [ + "Scan the input grid to locate all 0-cells that form 'holes' (enclosed spaces).", + "Use a flood-fill algorithm starting from all boundary 0-cells to mark all 'external' 0-cells.", + "Any 0-cell that remains unmarked after the flood fill (and is not a 3) is internal/enclosed.", + "Change the value of these internal 0-cells to 4 in the output grid.", + "Keep all 3-cells and external 0-cells as they are." + ], + "confidence": 0.9 +} \ No newline at end of file diff --git a/traces/00d62c1b.nvarc.md b/traces/00d62c1b.nvarc.md new file mode 100644 index 0000000..736ecc3 --- /dev/null +++ b/traces/00d62c1b.nvarc.md @@ -0,0 +1,27 @@ + +A black cell at a given position (r, c) is changed to red if and only if there is at least one blue cell located somewhere above it in the same column, one below it in the same column, one to its left in the same row, and one to its right in the same row. All other cells remain unchanged. + + +Input grids of varying sizes are provided. The transformation maintains relationships between input and output dimensions. + + +1. Create a copy of the input grid. +2. Iterate through each cell (r, c) of the input grid. +3. If the color of the current cell is not black (0), skip to the next cell. +4. If the cell is black, perform four checks: +5. 1. Scan upwards from (r-1, c) to (0, c) to find a blue cell (3). +6. 2. Scan downwards from (r+1, c) to the last row to find a blue cell. +7. 3. Scan leftwards from (r, c-1) to (r, 0) to find a blue cell. +8. 4. Scan rightwards from (r, c+1) to the last column to find a blue cell. +9. If all four checks are successful (a blue cell is found in each direction), change the color of the cell at (r, c) in the copied grid to red (4). +10. Return the modified copy as the output grid. + + +The output grid has the same dimensions as the input grid. + + +- boundary +- color_mapping +- connected +- filling + diff --git a/traces/1a07d186.json b/traces/1a07d186.json new file mode 100644 index 0000000..e5699b3 --- /dev/null +++ b/traces/1a07d186.json @@ -0,0 +1,35 @@ +{ + "task_id": "1a07d186", + "trace_source": "llm", + "intended_model_family": "gemini-flash", + "execution_model": "gemini-3-flash-preview", + "observations": [ + "The input grids contain full-span horizontal or vertical lines of specific colors and isolated dots of various colors.", + "Lines can be identified by colors that fill an entire row or column.", + "In Example 1, horizontal lines are at rows 3 (color 2) and 10 (color 1).", + "In Example 2, vertical lines are at columns 3 (color 3) and 12 (color 4).", + "In Example 3, a horizontal line is at row 5 (color 8).", + "Dots that match the color of a line are moved to be adjacent to that line on the same side they originated from.", + "Dots that do not match the color of any line are removed.", + "The lines themselves remain in their original positions." + ], + "hypotheses": [ + "For each dot, if its color matches the color of a horizontal line (at row L), it moves to row L-1 (if originally above) or row L+1 (if originally below). If it matches a vertical line (at column L), it moves to column L-1 (if originally left) or column L+1 (if originally right). Non-matching dots are deleted.", + "Dots are attracted to the nearest boundary line of the same color, occupying the adjacent cell while maintaining their relative position (column for horizontal lines, row for vertical lines)." + ], + "rejected_hypotheses": [ + { + "hypothesis": "Dots are reflected across the line of the same color.", + "reason": "In Example 1, dot (0,3) moved to (2,3) with line at row 3. Reflection would have put it at row 6." + } + ], + "selected_rule": "Identify all full-span horizontal or vertical lines. For every single-pixel dot in the grid, if its color matches one of the line colors, move it to the cell adjacent to that line on the same side as its original position. All other dots (colors with no corresponding lines) are deleted.", + "step_plan": [ + "Identify the lines in Test 1: Column 4 is color 2, Column 11 is color 3, and Column 20 is color 4.", + "Scan the input grid for isolated dots and their colors.", + "Process dots matching line colors: (2, 16) color 2 moves to (2, 5); (7, 6) color 4 moves to (7, 19); (9, 13) color 3 moves to (9, 12); (10, 1) color 2 moves to (10, 3); (15, 8) color 3 moves to (15, 10); (15, 24) color 4 moves to (15, 21); (17, 22) color 3 moves to (17, 12).", + "Delete all dots whose colors do not match any line (color 8 dots).", + "Construct the output grid by placing the original lines and the newly positioned dots on a zero-filled grid of the same dimensions (19x26)." + ], + "confidence": 0.9 +} \ No newline at end of file diff --git a/traces/1a07d186.nvarc.md b/traces/1a07d186.nvarc.md new file mode 100644 index 0000000..80b1a84 --- /dev/null +++ b/traces/1a07d186.nvarc.md @@ -0,0 +1,20 @@ + +Colors expand outward from their initial locations until they encounter another color or the edge of the grid. + + +Input grids contain colored cells. The transformation operates on color patterns and relationships. + + +1. For each non-zero color, expand the color to adjacent empty cells horizontally and vertically. +2. Stop expanding when encountering another color or the edge of the grid. + + +The grid contains multiple color values including 0. + + +- boundary +- color_mapping +- filling +- mirroring +- translation +