{ "cells": [ { "cell_type": "markdown", "id": "eb6e084e-be73-4f41-ae14-1d41935d1c83", "metadata": {}, "source": [ "\"IOAI\n", "\n", "[IOAI 2025 (Beijing, China), Individual Contest](https://ioai-official.org/china-2025)\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IOAI-official/IOAI-2025/blob/main/Individual-Contest/Concepts/Solution/Concepts_Solution.ipynb)" ] }, { "cell_type": "markdown", "id": "23a00134-fe45-4c31-bfba-9bc1fd2b2fad", "metadata": {}, "source": [ "# Concepts: Reference Solution" ] }, { "cell_type": "code", "execution_count": null, "id": "26a701aa", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "26a701aa", "outputId": "d692d4f1-79a9-4a30-8668-275aba12b7df" }, "outputs": [], "source": [ "# This notebook is the training notebook\n", "import vllm\n", "from vllm import LLM, SamplingParams\n", "from vllm.sampling_params import GuidedDecodingParams\n", "from pydantic import BaseModel" ] }, { "cell_type": "code", "execution_count": null, "id": "a021e386", "metadata": { "id": "a021e386" }, "outputs": [], "source": [ "API_URL = \"\" # Please Use your own API URL here, Qwen is used in Ref Result\n", "# SCORER_API_KEY = \"sk-ioai-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\" # use the api key provided to you\n", "SCORER_API_KEY = \"\" # Please Use your own API key for testing, Qwen is used in Ref Result" ] }, { "cell_type": "code", "execution_count": null, "id": "20ffe741", "metadata": { "id": "20ffe741" }, "outputs": [], "source": [ "import math, random\n", "import httpx\n", "from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type, retry_if_exception\n", "\n", "class GameClient:\n", " def ndcg_at_10(self, predictions, correct_answer):\n", " if correct_answer not in predictions:\n", " return 0.0\n", " try:\n", " rank = predictions[:10].index(correct_answer) + 1\n", " except ValueError:\n", " return 0.0\n", "\n", " return 1 / math.log2(rank + 1)\n", "\n", " def hits_at_10(self, predictions, correct_answer):\n", " return 1.0 if correct_answer in predictions[:10] else 0.0\n", "\n", " def __init__(self):\n", " self._random_options = ['gym', 'dinosaur', 'camel', 'desk', 'chicken', 'suitcase', 'thief', 'penguin', 'bat', 'painter', 'yogurt', 'chocolate', 'football', 'wallet', 'magician', 'shoes', 'bank', 'church', 'chewing gum', 'fashion', 'chainsaw', 'escalator', 'scarf', 'lawyer', 'eagle', 'credit card', 'garden hose', 'glider', 'crosswalk', 'subway', 'fireworks', 'marshmallow', 'cookies', 'curtains', 'dining room', 'cars', 'wedding', 'guitar', 'coffee', 'mouse', 'meat', 'scale', 'train tracks', 'zebra', 'fairy', 'quit', 'museum', 'kangaroo', 'surfboard', 'cheese', 'nightmare', 'jellyfish', 'koala', 'strawberry', 'tiger', 'mailbox', 'kettle', 'potato', 'janitor', 'lighthouse', 'crocodile', 'charger', 'doctor', 'peacock', 'peanut', 'popcorn', 't-shirt', 'fertilizer', 'keyboard', 'umbrella', 'pool', 'watercolor', 'mango', 'xylophone', 'bathroom', 'ice cube', 'giraffe', 'garage', 'cabin', 'plankton', 'pig', 'vulture', 'frame', 'polar bear', 'microscope', 'snake', 'skeleton', 'rocket', 'backpack', 'jacket', 'bedroom', 'castle', 'horse', 'dragonfly', 'hotel', 'cyclist', 'mask', 'restaurant', 'toothpaste', 'angel', 'whistle', 'wrestling', 'eclipse', 'hermit crabs', 'horn', 'boxers', 'volcano', 'fire station', 'toothbrush', 'egg', 'straw', 'rice', 'diamond', 'vitamins', 'tricycle', 'bottle-opener', 'panther', 'ice skates', 'theater', 'gas mask', 'game console', 'path', 'scorpion', 'snowboard', 'crab', 'pie', 'octopus', 'mustache', 'pepper grinder', 'swings', 'palm tree', 'well', 'sewing machine', 'key', 'station', 'mosque', 'chameleon', 'cherry', 'parrot', 'leggings', 'radio', 'brick', 'sunflower', 'hammer', 'carrot', 'radar', 'kite', 'bathtub', 'rhinoceros', 'spoon', 'orchestra', 'gravity', 'flute', 'lipstick', 'school', 'meteorite', 'politician', 'ladder', 'lawnmower', 'computer', 'wheel', 'airport', 'firefighter', 'porch', 'police station', 'queen', 'mayonnaise', 'alumunium foil', 'lion', 'helmet', 'teacher', 'tea', 'fan', 'piano', 'snail', 'farmer', 'harbor', 'nurse', 'sunglasses', 'bee', 'postal worker', 'market', 'plank', 'steering wheel', 'squirrel', 'netting', 'dragon', 'cafeteria', 'millennium', 'spinach', 'fork', 'cabbage', 'ping-pong', 'lock', 'submarine', 'dictionary', 'vaccine', 'soda', 'skirt', 'toaster', 'shorts', 'circus', 'flowerpot', 'lobster', 'rainbow', 'cockroach', 'frog', 'basket ball', 'chilli pepper', 'pajamas', 'crossword', 'light bulb', 'drill', 'beaver', 'daisy', 'river', 'yo-yo', 'harmonica', 'soap', 'igloo', 'sausage', 'deer', 'sailboat', 'fish', 'mosquito', 'can', 'rat', 'frying pan', 'barcode', 'sunscreen', 'ferret', 'whale', 'duck', 'shirt', 'vacuum', 'detective', 'perfume', 'seal', 'raincoat', 'alien', 'bull', 'nest', 'butterfly', 'eraser', 'hedgehog', 'panda', 'refrigerator', 'monocle', 'window', 'kitchen', 'mole', 'speaker', 'waiter', 'salad', 'dolphin', 'storm', 'drums', 'spiderweb', 'bicycle', 'monkey', 'flamingo', 'prison', 'bowling', 'pencil sharpner', 'photo', 'printer', 'robe', 'seahorse', 'doorbell', 'gloves', 'alcohol', 'diving suit', 'shotgun', 'hairbrush', 'cactus', 'ambulance', 'hula hoop', 'snowman', 'mountain', 'unicorn', 'suit', 'cake', 'cow', 'sled', 'boar', 'barbecue', 'trash can', 'slingshot', 'banana', 'dam', 'hat', 'milk', 'shell', 'broom', 'fisherman', 'bucket', 'bell', 'tracktor', 'fly', 'spider', 'carpet', 'coconut tree', 'movie theater', 'socks', 'soldier', 'watering can', 'accountant', 'microphone', 'toothpick', 'wolf', 'trumpet', 'apple', 'library', 'cork', 'zipper', 'pan', 'doghouse', 'dynamite', 'swan', 'grasshopper', 'beach', 'starfish', 'police officer', 'board game', 'magnet', 'cucumber', 'fire extinguisher', 'sundial', 'mechanic', 'lighter', 'shovel', 'shark', 'notebook', 'ostrich', 'bodyguard', 'binoculars', 'parachute', 'drone', 'kiwi', 'ghost', 'baker', 'robot', 'postcard', 'horseshoe', 'karaoke', 'billiards', 'palace', 'hospital', 'compass', 'truck', 'holiday', 'lake', 'cave', 'space station', 'mushroom', 'magnifying glass', 'fox', 'bread', 'rose', 'windmill', 'pirate', 'earring', 'hunter', 'princess', 'calculator', 'clown', 'watch', 'pilot', 'mustard', 'swordfish', 'darts', 'microwave oven', 'plumber', 'sword']\n", "\n", " @retry(\n", " stop=stop_after_attempt(3),\n", " wait=wait_exponential(multiplier=1, min=4, max=10),\n", " retry=retry_if_exception_type((httpx.TimeoutException, httpx.ConnectError, httpx.RequestError)) |\n", " retry_if_exception(lambda e: isinstance(e, httpx.HTTPStatusError) and e.response.is_server_error) # Retry on connection errors or server side errors.\n", " )\n", " def _make_api_call(self, clues, options):\n", " response = httpx.post(f\"{API_URL}/guess\", json={\n", " \"clues\": clues,\n", " \"options\": options\n", " }, headers={\n", " \"Authorization\": f\"Bearer {SCORER_API_KEY}\"\n", " }, timeout=60)\n", "\n", " response.raise_for_status()\n", "\n", " guesser_response = response.json()\n", "\n", " if \"guesses\" not in guesser_response:\n", " raise ValueError(f\"Unable to generate guesses: {guesser_response}\")\n", " if not isinstance(guesser_response[\"guesses\"], list):\n", " raise ValueError(f\"Guesses is not a list: {guesser_response}\")\n", "\n", " return guesser_response\n", "\n", " def simulate_game(self, clues, expected_answer, distractors = []):\n", " expected_answer = expected_answer.lower()\n", " if expected_answer in distractors:\n", " options = []\n", " else:\n", " options = [expected_answer]\n", " if len(distractors) > 0:\n", " options.extend([d.lower() for d in distractors])\n", " options = options[:100]\n", "\n", " # fill in options until the size is 100 with random options\n", " # set seed based on the expected_answers\n", " if len(options) < 100:\n", " random.seed(expected_answer)\n", " options.extend(random.choices(self._random_options, k=100-len(options)))\n", " # then shuffle\n", " random.shuffle(options)\n", "\n", " try:\n", " guesser_response = self._make_api_call(clues, options)\n", " predictions = [p.lower() for p in guesser_response[\"guesses\"]]\n", " return {\n", " \"predictions\": predictions,\n", " \"hit@10\": self.hits_at_10(predictions, expected_answer),\n", " \"NDCG@10\": self.ndcg_at_10(predictions, expected_answer)\n", " }\n", "\n", " except Exception as e:\n", "\n", " if isinstance(e, httpx.HTTPStatusError):\n", " print(f\"HTTP Status Error {e.response.status_code}: {e.response.text}\")\n", " try:\n", " error_detail = e.response.json().get(\"detail\", \"Unknown error\")\n", " print(f\"Error details: {error_detail}\")\n", " except:\n", " print(f\"Could not parse error response {e.response.text}\")\n", "\n", " elif isinstance(e, ValueError):\n", " print(f\"Value error: {e}\")\n", "\n", " elif isinstance(e, httpx.TimeoutException):\n", " print(\"request timed out after retries\")\n", "\n", " elif isinstance(e, httpx.ConnectError):\n", " print(f\"Could not connect to {API_URL} after retries\")\n", "\n", " elif isinstance(e, httpx.RequestError):\n", " print(f\"Request error after retries: {e}\")\n", "\n", " else:\n", " print(f\"Unknown error: {e}\")\n", "\n", " return {\n", " \"predictions\": [],\n", " \"hit@10\": 0.0,\n", " \"NDCG@10\": 0.0\n", " }" ] }, { "cell_type": "code", "execution_count": null, "id": "8730c968", "metadata": { "id": "8730c968" }, "outputs": [], "source": [ "from datasets import load_from_disk\n", "\n", "BASE_PATH = \"/bohr/train-exfl/v2/\"\n", "DESCRIPTIONS = f\"{BASE_PATH}/hint_descriptions\"\n", "\n", "hint_descriptions = load_from_disk(DESCRIPTIONS)['train']\n", "hint_descriptions = {\n", " x['ID']: {'description': x['Description'], 'icons': x['image']}\n", " for x in hint_descriptions\n", "}" ] }, { "cell_type": "code", "execution_count": null, "id": "f1804b3b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f1804b3b", "outputId": "803abf8a-0894-41a3-b33a-29715a0c260e" }, "outputs": [], "source": [ "def tns_desc(description: str):\n", " return description.replace(\"\\n\", \"-\")\n", "\n", "valid_hints = [tns_desc(x['description']) for x in hint_descriptions.values()]\n", "hint_to_id = {\n", " tns_desc(x['description']): xid\n", " for xid, x in hint_descriptions.items()\n", "}\n", "\n", "print(valid_hints)\n", "print(hint_to_id)" ] }, { "cell_type": "code", "execution_count": null, "id": "118f6ada", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "118f6ada", "outputId": "4ba0a0f2-5061-4ab7-f3b9-0fad5130a1a6" }, "outputs": [], "source": [ "TRAINING_SET = f\"{BASE_PATH}/train\"\n", "dev = load_from_disk(TRAINING_SET)['train']\n", "print(dev)\n", "print(dev[0])" ] }, { "cell_type": "markdown", "id": "rlOxgH6RH3Ov", "metadata": { "id": "rlOxgH6RH3Ov" }, "source": [ "## Fine Tuning" ] }, { "cell_type": "code", "execution_count": null, "id": "wH_bCBLtH4i7", "metadata": { "id": "wH_bCBLtH4i7" }, "outputs": [], "source": [ "from openai import AsyncOpenAI\n", "oai_client = AsyncOpenAI(\n", " base_url=\"https://ioai-llm-proxy.up.railway.app/prox/v1\",\n", " api_key=SCORER_API_KEY\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "2bea6620-67dc-4f79-ba1c-2d4479b3e481", "metadata": {}, "outputs": [], "source": [ "from unsloth import FastLanguageModel\n", "import torch\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"/bohr/models-b08n/v1/models/unsloth/Qwen3-0.6B\",\n", " max_seq_length = 4096, # Context length - can be longer, but uses more memory\n", " full_finetuning = False, # We have full finetuning now!\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "4GLl1NqrIfi4", "metadata": { "id": "4GLl1NqrIfi4" }, "outputs": [], "source": [ "from typing import Literal, List, Optional\n", "\n", "def get_prompt(valid_hints, options, answer):\n", " # Format valid_hints properly without escape characters\n", " hints_str = str(valid_hints).replace(\"'\", '\"')\n", "\n", " user_content = (\n", " f\"Your valid list of clues are: {hints_str}. \"\n", " \"Your job is a clue giver. You will help the guesser pick the correct answer from a range of options. \"\n", " \"You must output AT LEAST 1 hint and AT MOST 8 hints for each list of hints. \"\n", " \"Please output a json object with the key 'hints_1', 'hints_2', 'hints_3', and 'hints_4', and the value being a list of hints. \"\n", " \"IMPORTANT: You must ONLY use hint names from the valid hints list provided above. Do not create new hint names. \"\n", " 'Example for the answer \"knight\": '\n", " '{\"hints_1\":[\"Human-Historical-Real\",\"Attack-Conflict - Combat-Weapon\",\"Masculine-Male\"], '\n", " '\"hints_2\":[\"Character-Fictional-Imaginary\",\"Work-Occupation\",\"Defence-Protection-Wall\",\"Arm-Hand-Finger\"], '\n", " '\"hints_3\":[\"Clothing-Accessory-Cotume\",\"Power-Politics\",\"Building-Monument-City\"], '\n", " '\"hints_4\":[\"Life-Heart-Love\",\"Metal\",\"Torso - Back-Stomach-Body\"]} '\n", " f\"The answer word is :{answer}. Please construct sequences of hints that are most relevant to it. Remember to adhere to your instructions.\"\n", " )\n", "\n", " prompt = [\n", " {\"role\": \"system\", \"content\": \"You are an AI giving hints to another AI in a concept guessing game. Generate up to 4 list of concept markers, or hints, to help another AI guess the correct answer.\"},\n", " {\"role\": \"user\", \"content\": user_content}\n", " ]\n", " text = tokenizer.apply_chat_template(\n", " prompt,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", " return text" ] }, { "cell_type": "code", "execution_count": null, "id": "gnc3UgsCI_dB", "metadata": { "id": "gnc3UgsCI_dB" }, "outputs": [], "source": [ "ValidHint = Literal[*valid_hints]\n", "\n", "class Hints(BaseModel):\n", " hints_1: List[ValidHint]\n", " hints_2: List[ValidHint]\n", " hints_3: List[ValidHint]\n", " hints_4: List[ValidHint]\n", "\n", " def to_result(self):\n", " hints = [self.hints_1, self.hints_2, self.hints_3, self.hints_4]\n", " result = []\n", " for hintlist in hints:\n", " cur_hintlist = [hint_to_id[hint] for hint in hintlist[:8]]\n", " result.append(cur_hintlist)\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "id": "UFmr1B45JJbf", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UFmr1B45JJbf", "outputId": "d974a0dd-6656-46eb-b0cb-5d1649acb712" }, "outputs": [], "source": [ "async def gen_dat(label, options):\n", " messages = [{\n", " \"role\": \"user\",\n", " \"content\": get_prompt(valid_hints, options, label)\n", " }]\n", " response = await oai_client.beta.chat.completions.parse(\n", " model=\"openai/gpt-4.1\",\n", " messages=messages,\n", " response_format=Hints\n", " )\n", " messages.append(\n", " {\n", " \"role\": \"assistant\",\n", " \"content\": response.choices[0].message.parsed.model_dump_json()\n", " }\n", " )\n", " return messages\n", "\n", "sample_res = await gen_dat(dev[0]['label'], dev[0]['options'])\n", "print(sample_res[-1]['content'])" ] }, { "cell_type": "code", "execution_count": null, "id": "m9F39BU5L1Dt", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m9F39BU5L1Dt", "outputId": "eedd2ec3-8a79-4ee3-ceab-9143a2970680" }, "outputs": [], "source": [ "import random\n", "# generate corpous\n", "all_options = list(set([opt for x in dev for opt in x['options']]))\n", "print(len(all_options))\n", "\n", "def gen_inout():\n", " options = random.sample(all_options, 100)\n", " label = random.choice(options)\n", " return (label, options)\n", "\n", "initial_corpos = [gen_inout() for _ in range(1000)]\n", "print(initial_corpos[0])\n", "print(await gen_dat(*initial_corpos[0]))" ] }, { "cell_type": "code", "execution_count": null, "id": "WkqyQtWhM50b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WkqyQtWhM50b", "outputId": "26d6239c-e05c-4dad-e093-7bc0e989c269" }, "outputs": [], "source": [ "from asyncio import Semaphore\n", "from tqdm.asyncio import tqdm_asyncio\n", "\n", "sema = Semaphore(90)\n", "\n", "async def gen_with_sema(inp):\n", " async with sema:\n", " return await gen_dat(*inp)\n", "\n", "tasks = [gen_with_sema(inp) for inp in initial_corpos]\n", "results = await tqdm_asyncio.gather(*tasks, total=len(tasks))\n", "len(results)" ] }, { "cell_type": "code", "execution_count": null, "id": "bff93406-d65b-4d3b-b597-39e6daf51f30", "metadata": {}, "outputs": [], "source": [ "SAVE_DIR = \"/personal/concepts_refresult\"" ] }, { "cell_type": "code", "execution_count": null, "id": "Wfa-Ms-yQDlS", "metadata": { "id": "Wfa-Ms-yQDlS" }, "outputs": [], "source": [ "import pickle\n", "import os\n", "os.makedirs(SAVE_DIR, exist_ok=True)\n", "with open(f\"{SAVE_DIR}/ft_data.pickle\", \"wb\") as f:\n", " pickle.dump(results, f)" ] }, { "cell_type": "code", "execution_count": null, "id": "M4sUOZcFQn3F", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M4sUOZcFQn3F", "outputId": "feac3583-8ae5-4f33-ef7c-b2353111dcb4" }, "outputs": [], "source": [ "import pickle\n", "with open(f\"{SAVE_DIR}/ft_data.pickle\", \"rb\") as f:\n", " results = pickle.load(f)\n", "\n", "print(f\"Loaded {len(results)} results.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "_6H0i9YkSBwQ", "metadata": { "id": "_6H0i9YkSBwQ" }, "outputs": [], "source": [ "formatted_results_qwen = [[item['content'] for item in result] for result in results]" ] }, { "cell_type": "code", "execution_count": null, "id": "VRkO-UZ_nYOX", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VRkO-UZ_nYOX", "outputId": "81e71fcf-4ba1-4b4e-9bf1-0688d65d529f" }, "outputs": [], "source": [ "results = formatted_results_qwen\n", "results[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "evuNIf77MrxW", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "evuNIf77MrxW", "outputId": "33393b63-e0dd-4b60-f1f4-b7f8096a5283" }, "outputs": [], "source": [ "!nvidia-smi\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c3SaJ3GzQ2K7", "metadata": { "id": "c3SaJ3GzQ2K7" }, "outputs": [], "source": [ "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r = 32, # Choose any number > 0! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 32, # Best to choose alpha = rank or rank*2\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "6Zlip3C5Rqig", "metadata": { "id": "6Zlip3C5Rqig" }, "outputs": [], "source": [ "processed_conversations = results" ] }, { "cell_type": "code", "execution_count": null, "id": "AP5qpSUKTPW-", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AP5qpSUKTPW-", "outputId": "5968909c-1a59-445b-e49b-be45ce52d21f" }, "outputs": [], "source": [ "processed_conversations[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "MPn_RtLITcq7", "metadata": { "id": "MPn_RtLITcq7" }, "outputs": [], "source": [ "import pandas as pd\n", "from datasets import Dataset\n", "\n", "training_set = pd.Series(processed_conversations)\n", "training_set.name = \"text\"\n", "\n", "training_dataset = Dataset.from_pandas(pd.DataFrame(training_set))" ] }, { "cell_type": "code", "execution_count": null, "id": "rOkg6dCETW93", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "8e7e45bdabda4f8ab2095970cd24cd9a", "f6760a0dc2e044c5a2fcb6201a983673", "f5b48773b85e4357a06efff82604baec", "06e174a90651415f9fcc39649d4a2d51", "1e23430999bd4c28ad97959c6b462c85", "506bba08c20b421dbc7f087c7a2cc0a4", "c6883305d43b42bb8d680ae52ea4975e", "fd1f5bbba9fc451c87ebb02563f9874d", "9e7f928ff7d14b13b0d9b64716db57e3", "a7af3507adcb4345aaeb606cd21a2f76", "c56c1b05c55f492fb73aad09f2034dc4" ] }, "id": "rOkg6dCETW93", "outputId": "cf52613d-e299-4973-aea5-960204a9d087" }, "outputs": [], "source": [ "from trl import SFTTrainer, SFTConfig\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " train_dataset = training_dataset,\n", " eval_dataset = None, # Can set up evaluation!\n", " args = SFTConfig(\n", " dataset_text_field = \"text\",\n", " per_device_train_batch_size = 2,\n", " gradient_accumulation_steps = 4, # Use GA to mimic batch size!\n", " warmup_steps = 5,\n", " num_train_epochs = 1, # Set this for 1 full training run.\n", " max_steps = 100, \n", " learning_rate = 2e-4, # Reduce to 2e-5 for long training runs\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.01,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " logging_steps=1, # 每步都记录日志\n", " logging_dir=\"./logs\", # 日志保存目录\n", " logging_first_step=True, # 强制记录第一步\n", " report_to=\"none\", # 禁用外部报告工具\n", " disable_tqdm=True, # 确保显示进度条\n", " log_level=\"debug\" \n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "oMyQqWVAWify", "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "oMyQqWVAWify", "outputId": "9aa4c2d0-0265-4a47-b3ab-eebfaa933e88" }, "outputs": [], "source": [ "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "3ac06438-1c8c-458b-b8f3-ec4fe2d70035", "metadata": {}, "outputs": [], "source": [ "import torch\n", "def print_gpu_utilization():\n", " allocated = torch.cuda.memory_allocated()/1e9\n", " reserved = torch.cuda.memory_reserved()/1e9\n", " print(f\"显存使用: {allocated:.2f}GB / {reserved:.2f}GB\")\n", "\n", "print_gpu_utilization() # 训练前\n", "trainer_stats = trainer.train()\n", "print_gpu_utilization() # 训练后" ] }, { "cell_type": "code", "execution_count": null, "id": "9AkAF0uEW_3-", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 233 }, "id": "9AkAF0uEW_3-", "outputId": "96841d36-eb29-4a8d-c3a3-5acbce000e33" }, "outputs": [], "source": [ "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory / max_memory * 100, 3)\n", "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(\n", " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", ")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "B8Sv4iRRXBnb", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 176, "referenced_widgets": [ "b54a13bb32464bc6914132f90f6875f6", "28c09124011b418480d6210b29ff311d", "20458da9a647414ba3d1be37d9c9d76c", "a5661bfa468a4dae98922871f9ea6ebc", "b1c8b59859bd4705b68ff78ece157ad6", "3cf556c1336b4c5590446fe138abd138", "d293de96c43b48099ee2ae63ccf0fb3f", "96434961f48f4948a74548dbb646a686", "a4bec96b42814223ba4f4295bc772343", "5f2b0d7dfe034cfcbc3a4d38b5e71d20", "4b12c36b20a1445b84351c0e5cb13fb4" ] }, "id": "B8Sv4iRRXBnb", "outputId": "ce7da12f-4a18-41db-f4c9-8fe14ab074a8" }, "outputs": [], "source": [ " model.save_pretrained_merged(f\"{SAVE_DIR}/model\", tokenizer, save_method = \"merged_16bit\",)" ] }, { "cell_type": "code", "execution_count": null, "id": "b3a14adf-6610-4de3-ab12-b8490d7ac4ad", "metadata": {}, "outputs": [], "source": [ "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": null, "id": "50a07d22", "metadata": { "id": "50a07d22" }, "outputs": [], "source": [ "class ClueGiver:\n", " def __init__(self):\n", " # Configure vLLM to use maximum 0.8 GPU memory\n", " self.llm = LLM(\n", " f\"{SAVE_DIR}/model\",\n", " gpu_memory_utilization=0.8, # Use 80% of available GPU memory\n", " max_model_len=4096, # Optional: adjust based on your model's context window\n", " tensor_parallel_size=1 # Optional: adjust if using multiple GPUs\n", " )\n", " json_schema = Hints.model_json_schema()\n", " self.sampling_params = SamplingParams(\n", " guided_decoding=GuidedDecodingParams(\n", " json=json_schema,\n", " ),\n", " max_tokens=5096,\n", " frequency_penalty=0.5,\n", " presence_penalty=0.8\n", " )\n", "\n", " def construct_clues(self, answers: List[str], options: List[List[str]]):\n", " prompts = []\n", " for answer, options in zip(answers, options):\n", " prompt = get_prompt(valid_hints, options, answer)\n", " prompts.append(prompt)\n", " # batch mode: pass a list of prompts\n", " hints_batch = self.llm.generate(prompts=prompts, sampling_params=self.sampling_params)\n", " results = []\n", " for i, hints in enumerate(hints_batch):\n", " # print(hints.outputs)\n", " # print(f\"len of token ids: {len(hints.outputs[0].token_ids)}\")\n", " txt = hints.outputs[0].text\n", " print(txt)\n", " try:\n", " hints_obj = Hints.model_validate_json(txt)\n", " results.append(hints_obj.to_result())\n", " except Exception as e:\n", " print(f\"Error parsing hints for answer {answers[i]}: {e}\")\n", " results.append([[1,2,3,4]])\n", " return results" ] }, { "cell_type": "code", "execution_count": null, "id": "d2594830-fb11-423d-98dd-2ee15202a3f5", "metadata": {}, "outputs": [], "source": [ "torch.cuda.empty_cache()\n", "import gc\n", "gc.collect()" ] }, { "cell_type": "code", "execution_count": null, "id": "c21319c5", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c21319c5", "outputId": "6eabd74b-42db-4231-ef4c-44cf0817db87" }, "outputs": [], "source": [ "clue_giver = ClueGiver()" ] }, { "cell_type": "code", "execution_count": null, "id": "01a07b30", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 669, "referenced_widgets": [ "1ebc9c7088134530bf402d907fd719df", "399b0dc41c19408193f57da629e43eb4", "19a79174ce6d4e24b744199dd97c046d", "c43fc7de460647f2a4325100d7519439", "fd422b88259b4c6bbbe5212fe6ce7ee3", "142f574b5cc548f0bda426c9c47bda6b", "8015b9470bf649c28c90ad6facf6a2c6", "b9beddbff77a473c864f1ee0ed9c1e22", "16380010167e4d60a9bdac904157f1c6", "e20a5c1c0fd245208f38b33922b066bc", "af36b3affea245069797e7d8612ed5dd", "295833ede6d4437b9e303a5ca4841fa5", "6d175028e6864640ae0375f30a4edc0b", "1a4329c473e441c797b1251cf654c65e", "13e6860e6b4845efb0fcc515a3eaed90", "d1c76a2e33f143748aa742cdf6ee6db9", "07de89de999a43dc9fa72254769c4250", "6fa0605a8f2c44d3b1ce0049baaa3795", "9f8b1214fc1642968cb9ad8d113c6dbb", "78289123eac54371b08225ee5a38efac", "cd72b8ec050e45d4a08eacd34dd31d31", "cd954a826b6944739c797875dd7da322" ] }, "id": "01a07b30", "outputId": "bc8c1a39-248e-4616-f749-ba7f7fde01ce" }, "outputs": [], "source": [ "res_clues = clue_giver.construct_clues([x['label'] for x in dev], [x['options'] for x in dev])" ] }, { "cell_type": "code", "execution_count": null, "id": "bf138945", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bf138945", "outputId": "b241b616-9feb-41ba-9e15-d609b6294400" }, "outputs": [], "source": [ "from tqdm import tqdm\n", "from concurrent.futures import ThreadPoolExecutor, as_completed\n", "\n", "def simulate_one(i_data):\n", " i, data = i_data\n", " clues = res_clues[i]\n", " prediction = game_client.simulate_game(clues, data['label'])\n", " return prediction\n", "\n", "game_client = GameClient()\n", "predictions = []\n", "with ThreadPoolExecutor() as executor:\n", " futures = [executor.submit(simulate_one, (i, data)) for i, data in enumerate(dev)]\n", " for f in tqdm(as_completed(futures), total=len(futures)):\n", " predictions.append(f.result())\n", "\n", "print(\"Final Score: \")\n", "print(sum([p['hit@10'] for p in predictions]) * 0.9 + sum([p['NDCG@10'] for p in predictions]) * 0.1)" ] }, { "cell_type": "markdown", "id": "dda5169b", "metadata": { "id": "dda5169b" }, "source": [ "## Submission\n", "\n", "You do not have to submit your training notebook (you can if you would like to). For resource-efficiency and reliability reasons, we encourage you to upload your trained model weights (if you have one) attached to your submission notebook, instead of submitting your entire training process. For help with submitting model weight files, refer to section 5 in the Bohrium Guide. Your submission notebook only has to include the test inference section below.\n", "\n", "You need to save your answers to testset A and testset B in separate `jsonl` files, `clues_a.jsonl` and `clues_b.jsonl`, as shown below. `clues_a` and `clues_b` should be lists of clues (each clue being a list of lists of integers). You need to zip the files together into `submission.zip`. The file names are important. You must follow the naming conventions otherwise the evaluation script will not be able to find your answers." ] }, { "cell_type": "code", "execution_count": null, "id": "908874dd-f6ea-4cfd-a02f-e5f04f34092a", "metadata": {}, "outputs": [], "source": [ "class ClueGiver:\n", " def __init__(self):\n", " self.llm = LLM(\n", " f\"/bohr/model-li35/v1/\",\n", " gpu_memory_utilization=0.8, # Reduce from default 0.9\n", " max_model_len=32768, # Reduce from 40960\n", " enforce_eager=True, # Disable CUDA graphs\n", " disable_custom_all_reduce=True,\n", " tensor_parallel_size=1\n", " )\n", " json_schema = Hints.model_json_schema()\n", " self.sampling_params = SamplingParams(\n", " guided_decoding=GuidedDecodingParams(\n", " json=json_schema,\n", " ),\n", " max_tokens=5096,\n", " frequency_penalty=0.5,\n", " presence_penalty=0.8\n", " )\n", "\n", " def construct_clues(self, answers: List[str], options: List[List[str]]):\n", " prompts = []\n", " for answer, options in zip(answers, options):\n", " prompt = get_prompt(valid_hints, options, answer)\n", " prompts.append(prompt)\n", " # batch mode: pass a list of prompts\n", " hints_batch = self.llm.generate(prompts=prompts, sampling_params=self.sampling_params)\n", " results = []\n", " for i, hints in enumerate(hints_batch):\n", " # print(hints.outputs)\n", " # print(f\"len of token ids: {len(hints.outputs[0].token_ids)}\")\n", " txt = hints.outputs[0].text\n", " print(txt)\n", " try:\n", " hints_obj = Hints.model_validate_json(txt)\n", " results.append(hints_obj.to_result())\n", " except Exception as e:\n", " print(f\"Error parsing hints for answer {answers[i]}: {e}\")\n", " results.append([[1,2,3,4]])\n", " return results" ] }, { "cell_type": "code", "execution_count": null, "id": "3582fdfe", "metadata": { "id": "3582fdfe" }, "outputs": [], "source": [ "clue_giver = ClueGiver() # Initialize your model. You can load model weights here." ] }, { "cell_type": "code", "execution_count": null, "id": "e35c1193", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209, "referenced_widgets": [ "9dffdd3d18524753a5bd21f5f9c58eb9", "c7611e33116a4e94a457c34067c0b783", "b4ca4c61265b4a0c976922a51368e292", "991f34a4288b45ca8db58270d3481a58", "5464af24f398408ebf67c09b0cbcac78", "6b81591326554166803ec9c893e0e392", "eb0fdf3b2b734313ba2b9c394139c515", "5599ec03cc6f47a49896a8ceec3f98c8", "3f774e02f27b4b32aa813b24747f0519", "8da64034ceb8452f9e6736017d07c850", "8e49817421b6486ba283454ecb064119", "6949f7c1f972463fabddc24cc34ea6f0", "5070cc75efa3490385265bbbdc52bfcd", "e5fd47bd246b4620873bed9b18c8497f", "328e135d43ed45a5bbaffe4161bed028", "85ec8352001a4f1c9404cf0a7fe8f428", "b6262987dd824afea8bcb2754760bd21", "84eb7e9630424cc2af564b0dd0b411a9", "9010858db30640d2982c61514d8eedb6", "d94facef2dbb446a80393e7e1e13f5ac", "49240e1e771048a693918739b031c09a", "0661d4e701164e4195a4930ef9d0a5c0", 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f\"{BASE_PATH}/test\"\n", "\n", "testset_a = load_from_disk(os.path.join(TEST_PATH, \"test_a\"))[\"test\"]\n", "testset_b = load_from_disk(os.path.join(TEST_PATH, \"test_b\"))[\"test\"]\n", "clues_a = clue_giver.construct_clues([x['label'] for x in testset_a], [x['options'] for x in testset_a])\n", "clues_b = clue_giver.construct_clues([x['label'] for x in testset_b], [x['options'] for x in testset_b])" ] }, { "cell_type": "code", "execution_count": null, "id": "4ef60d7a", "metadata": { "id": "4ef60d7a" }, "outputs": [], "source": [ "import zipfile\n", "import json\n", "\n", "PREF = \"refresult_jul28_qwen\"\n", "\n", "def write_clues(clues: List[List[List[int]]], path: str):\n", " with open(path, 'w') as f:\n", " for c in clues:\n", " f.write(json.dumps(c) + '\\n')\n", "\n", "write_clues(clues_a, \"clues_a.jsonl\")\n", "write_clues(clues_b, \"clues_b.jsonl\")\n", "\n", "with zipfile.ZipFile(BASE_PATH+PREF+'submission.zip', 'w') as zipf:\n", " zipf.write('clues_a.jsonl')\n", " 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