{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kaggle":{"accelerator":"nvidiaL4","dataSources":[{"sourceId":91496,"databundleVersionId":11802066,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Его величество, \"домашка №1\"","metadata":{"id":"JBA5IPyb4BFH"}},{"cell_type":"markdown","source":"В этой домашней работе вам предоставится уникальная возможность обучить Byte-level BPE токенизатор и небольшую LM.  \n\nДомашняя работа состоит из нескольких последовательных блоков: реализация и обучение токенизатора, реализация Transformer модели и обучение модели на датасете с русскими анекдотами!\n\nОбученные токенизатор и модель можно и нужно выложить на [🤗 HuggingFace](https://huggingface.co/). Зарегистрируйтесь там, подпишитесь на [deep vk](https://huggingface.co/deepvk) и создайте себе API токен.\n\nСледуйте ячейкам тетрадки и заполняйте пропущенные ячейки. В конце тетрадки вы найдете задачи со звездочкой, чтобы получить максимальный балл!","metadata":{"id":"cOnY6pwvpghE"}},{"cell_type":"code","source":"# Установим необходимые дополнительные библиотеки\n!pip install google-cloud-bigquery-storage\n%pip install --quiet datasets livelossplot","metadata":{"id":"0byNYx5dzB4b","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:01.013415Z","iopub.execute_input":"2025-10-27T19:08:01.013645Z","iopub.status.idle":"2025-10-27T19:08:29.116592Z","shell.execute_reply.started":"2025-10-27T19:08:01.013629Z","shell.execute_reply":"2025-10-27T19:08:29.115929Z"}},"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7929b9afd4d0>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/google-cloud-bigquery-storage/\u001b[0m\u001b[33m\n\u001b[0mCollecting google-cloud-bigquery-storage\n  Downloading google_cloud_bigquery_storage-2.33.1-py3-none-any.whl.metadata (10 kB)\nRequirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1 in /usr/local/lib/python3.11/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (1.34.1)\nRequirement already satisfied: google-auth!=2.24.0,!=2.25.0,<3.0.0,>=2.14.1 in /usr/local/lib/python3.11/dist-packages (from google-cloud-bigquery-storage) (2.40.3)\nRequirement already satisfied: proto-plus<2.0.0,>=1.22.3 in /usr/local/lib/python3.11/dist-packages (from google-cloud-bigquery-storage) (1.26.1)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<7.0.0,>=3.20.2 in /usr/local/lib/python3.11/dist-packages (from google-cloud-bigquery-storage) (3.20.3)\nRequirement already satisfied: googleapis-common-protos<2.0dev,>=1.56.2 in /usr/local/lib/python3.11/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (1.70.0)\nRequirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.11/dist-packages (from google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (2.32.5)\nRequirement already satisfied: grpcio<2.0dev,>=1.33.2 in /usr/local/lib/python3.11/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (1.75.1)\nRequirement already satisfied: grpcio-status<2.0dev,>=1.33.2 in /usr/local/lib/python3.11/dist-packages (from google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (1.49.0rc1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=2.14.1->google-cloud-bigquery-storage) (5.5.2)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.11/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=2.14.1->google-cloud-bigquery-storage) (0.4.2)\nRequirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.11/dist-packages (from google-auth!=2.24.0,!=2.25.0,<3.0.0,>=2.14.1->google-cloud-bigquery-storage) (4.9.1)\nRequirement already satisfied: typing-extensions~=4.12 in /usr/local/lib/python3.11/dist-packages (from grpcio<2.0dev,>=1.33.2->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (4.15.0)\nRequirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth!=2.24.0,!=2.25.0,<3.0.0,>=2.14.1->google-cloud-bigquery-storage) (0.6.1)\nRequirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (3.4.3)\nRequirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (3.10)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (2.5.0)\nRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.10.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,!=2.7.*,!=2.8.*,!=2.9.*,<3.0.0,>=1.34.1->google-cloud-bigquery-storage) (2025.8.3)\nDownloading google_cloud_bigquery_storage-2.33.1-py3-none-any.whl (293 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.6/293.6 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: google-cloud-bigquery-storage\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.\nbigframes 2.12.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.31.0, but you have google-cloud-bigquery 3.25.0 which is incompatible.\nbigframes 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 google-cloud-bigquery-storage-2.33.1\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.7/47.7 MB\u001b[0m \u001b[31m52.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h\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.\npylibcudf-cu12 25.2.2 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\ncudf-cu12 25.2.2 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 22.0.0 which is incompatible.\nbigframes 2.12.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.31.0, but you have google-cloud-bigquery 3.25.0 which is incompatible.\nbigframes 2.12.0 requires rich<14,>=12.4.4, but you have rich 14.1.0 which is incompatible.\ncudf-polars-cu12 25.6.0 requires pylibcudf-cu12==25.6.*, but you have pylibcudf-cu12 25.2.2 which is incompatible.\npandas-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.\u001b[0m\u001b[31m\n\u001b[0mNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"# Необходимые импорты\n\nimport inspect\nimport json\nimport os\nfrom collections import Counter\nfrom dataclasses import dataclass\nfrom functools import lru_cache, partial\nfrom pathlib import Path\n\nimport regex as re\nimport torch\nimport torch.nn as nn\nfrom datasets import load_dataset\nfrom huggingface_hub import HfApi, PyTorchModelHubMixin, interpreter_login, snapshot_download\nfrom huggingface_hub.utils import SoftTemporaryDirectory\nfrom livelossplot import PlotLosses\nfrom torch import Tensor\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom tqdm.auto import tqdm, trange","metadata":{"id":"UILR1tu3z9oI","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:29.117726Z","iopub.execute_input":"2025-10-27T19:08:29.118156Z","iopub.status.idle":"2025-10-27T19:08:32.481077Z","shell.execute_reply.started":"2025-10-27T19:08:29.118133Z","shell.execute_reply":"2025-10-27T19:08:32.480480Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"# Этой функцией будут помечены все места, которые необходимо дозаполнить\n# Это могут быть как целые функции, так и отдельные части внутри них\n# Всегда можно воспользоваться интроспекцией и найти места использования этой функции :)\n\n\ndef todo():\n    stack = inspect.stack()\n    caller_frame = stack[1]\n    function_name = caller_frame.function\n    line_number = caller_frame.lineno\n    raise NotImplementedError(f\"TODO at {function_name}, line {line_number}\")","metadata":{"id":"4p7OLSivnPW0","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:32.481627Z","iopub.execute_input":"2025-10-27T19:08:32.482127Z","iopub.status.idle":"2025-10-27T19:08:32.485527Z","shell.execute_reply.started":"2025-10-27T19:08:32.482107Z","shell.execute_reply":"2025-10-27T19:08:32.485054Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"interpreter_login()","metadata":{"id":"bCkJJp2JK99x","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:32.486600Z","iopub.execute_input":"2025-10-27T19:08:32.486829Z","iopub.status.idle":"2025-10-27T19:08:40.405785Z","shell.execute_reply.started":"2025-10-27T19:08:32.486814Z","shell.execute_reply":"2025-10-27T19:08:40.405250Z"}},"outputs":[{"name":"stdout","text":"\n    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"Enter your token (input will not be visible):  ········\nAdd token as git credential? (Y/n)  n\n"}],"execution_count":5},{"cell_type":"code","source":"# Подготовим репозиторий для будущей модели и токенизатора\nusername = HfApi().whoami()[\"name\"]\nREPO_NAME = f\"{username}/llm-course-hw1\"  # Или как вам хочется\n\nprint(f\"Homework repository: '{REPO_NAME}'\")\n\n# И другие полезные вещи\nSEED = 0xC0FFEE","metadata":{"id":"GVWKkwaryDTq","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:40.406437Z","iopub.execute_input":"2025-10-27T19:08:40.406621Z","iopub.status.idle":"2025-10-27T19:08:40.494525Z","shell.execute_reply.started":"2025-10-27T19:08:40.406605Z","shell.execute_reply":"2025-10-27T19:08:40.494055Z"}},"outputs":[{"name":"stdout","text":"Homework repository: 'Amir337/llm-course-hw1'\n","output_type":"stream"}],"execution_count":6},{"cell_type":"markdown","source":"# Датасет\n\nПервым делом загрузим данные: [🤗 IgorVolochay/russian_jokes](https://huggingface.co/datasets/IgorVolochay/russian_jokes)\n\nИ немного посмотрим на них 👀","metadata":{"id":"GxN5JUbZ3ToV"}},{"cell_type":"code","source":"dataset = load_dataset(\"json\", data_files=\"hf://datasets/IgorVolochay/russian_jokes/dataset.json\")\nprint(\"\\n===\\n\".join(dataset[\"train\"][\"jokes\"][:3]))","metadata":{"id":"rT78JNcqpRXW","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:40.495082Z","iopub.execute_input":"2025-10-27T19:08:40.495252Z","iopub.status.idle":"2025-10-27T19:08:44.265264Z","shell.execute_reply.started":"2025-10-27T19:08:40.495238Z","shell.execute_reply":"2025-10-27T19:08:44.264657Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"dataset.json:   0%|          | 0.00/44.2M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c851c12d60d4fad9122ad20390e6a87"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Generating train split: 0 examples [00:00, ? examples/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc69b3707fe54b5b8f0d2c7b368e524b"}},"metadata":{}},{"name":"stdout","text":"- Зять, а ты знаешь, где найти того мужчину, который спас меня, когда я тонула?- Да, он уже приходил ко мне извиняться!\n \n===\nПосле проведения акции \"К животным по-человечески\" животные посовещались\n и решили провести акцию \"К человеку по-скотски\".\n \n===\nШтирлиц пришел домой и сразу завалился на боковую. Средняя от досады заплакала.\n \n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# Подготовим холдауты\ndataset = dataset[\"train\"].train_test_split(test_size=0.1, seed=SEED)\ndataset","metadata":{"id":"z7G7o6OdK99z","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:44.265879Z","iopub.execute_input":"2025-10-27T19:08:44.266091Z","iopub.status.idle":"2025-10-27T19:08:44.479612Z","shell.execute_reply.started":"2025-10-27T19:08:44.266073Z","shell.execute_reply":"2025-10-27T19:08:44.479091Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"DatasetDict({\n    train: Dataset({\n        features: ['jokes'],\n        num_rows: 135497\n    })\n    test: Dataset({\n        features: ['jokes'],\n        num_rows: 15056\n    })\n})"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"# Токенизатор [3 балла]\n\nВ качестве токенизатора будем использоват Byte-level BPE.\n\nДля этого:\n1. Реализуем его обучения, нам необходимо построить словарь заданного размера и набор слияний по этому словарю\n2. Обучим токенизатор на датасете\n3. Реализуем инференс токенизатора: кодирование текста и декодирование токенов","metadata":{"id":"aZzvdEVO3-kM"}},{"cell_type":"code","source":"# Всякие полезности\n\nWHITESPACE_SPLITTER = re.compile(r\"\"\"'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+\"\"\")\n\n\ndef bytes_to_unicode() -> dict[int, str]:\n    \"\"\"The original dictionary consists of 256 bytes and their corresponding Unicode characters.\n    For example, chr(33) is '!'. However, not all bytes have a visually appealing representation,\n    so such characters are skipped and replaced with the first available ones, i.e. shifted by 256.\n    \"\"\"\n    initial_bytes = (\n        list(range(ord(\"!\"), ord(\"~\") + 1)) + list(range(ord(\"¡\"), ord(\"¬\") + 1)) + list(range(ord(\"®\"), ord(\"ÿ\") + 1))\n    )\n    initial_chars = [chr(it) for it in initial_bytes]\n    n = 0\n    for byte in range(2**8):\n        if byte not in initial_bytes:\n            initial_bytes.append(byte)\n            initial_chars.append(chr(2**8 + n))\n            n += 1\n    return dict(sorted(zip(initial_bytes, initial_chars)))","metadata":{"id":"15U6H1iLU3kI","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:44.480360Z","iopub.execute_input":"2025-10-27T19:08:44.480549Z","iopub.status.idle":"2025-10-27T19:08:44.486245Z","shell.execute_reply.started":"2025-10-27T19:08:44.480534Z","shell.execute_reply":"2025-10-27T19:08:44.485733Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"def merge(merge_pair: tuple[str, str], pair_frequences: Counter[tuple[str, str]], words_by_tokens: Counter[tuple[str]]):\n    \"\"\"Merges a given pair of tokens and update corresponding stats\n\n    Args:\n        merge_pair: The pair of tokens to be merged.\n        pair_frequences: A counter tracking the frequency of token pairs in the dataset.\n        words_by_tokens: A counter mapping tokenized words to their frequencies.\n\n    Returns:\n        Updated pair frequences and word tokenization w.r.t. to new token.\n    \"\"\"\n    a, b = merge_pair\n    n_tok = a + b\n    n_words = Counter()\n    for toks, freq in words_by_tokens.items():\n        i = 0\n        n = len(toks)\n        merged = []\n        while i < n:\n            if i < n - 1 and toks[i] == a and toks[i + 1] == b:\n                merged.append(n_tok)\n                i += 2\n            else:\n                merged.append(toks[i])\n                i += 1\n        n_words[tuple(merged)] += freq\n    n_freqs = Counter()\n    for toks, freq in n_words.items():\n        if len(toks) < 2:\n            continue\n        for i in range(len(toks) - 1):\n            n_freqs[(toks[i], toks[i + 1])] += freq\n\n    return n_freqs, n_words\n\ndef train(data: list[str], vocab_size: int = 1024, special_tokens: list[str] = None):\n    \"\"\"Train BPE tokenizer on passed data\n\n    Args:\n        data: List of train documents\n        vocab_size: Size of target vocabulary\n        special_tokens: List of special tokens to add into vocabulary\n    Returns:\n        vocabulary: mapping from string token to id\n        merges: list of merges, each one is tuple of string tokens\n    \"\"\"\n    if vocab_size < 256:\n        raise ValueError(\"Vocab size can't be less than 256\")\n    if special_tokens is None:\n        special_tokens = []\n\n    # 1. Initialize vocabulary (using inverse one during training)\n    id2token = bytes_to_unicode()\n    merges = []\n\n    # 2. Load data\n    words_by_tokens = Counter()\n    for sample in tqdm(data, desc=\"Loading data\"):\n        # 2.1 Split into words\n        words = WHITESPACE_SPLITTER.findall(sample.strip())\n        for word in words:\n            # 2.2 Tokenize with base vocabulary\n            bt = word.encode(\"utf-8\")\n            toks = tuple(id2token[b] for b in bt)\n            if toks:\n                words_by_tokens[toks] += 1\n\n    # 3. Calculate statistic of token's pairs\n    pair_frequences = Counter()\n    for toks, freq in words_by_tokens.items():\n        if len(toks) < 2:\n            continue\n        for i in range(len(toks) - 1):\n            pair_frequences[(toks[i], toks[i + 1])] += freq\n\n    # 4. Build vocabulary\n    pbar = trange(vocab_size, desc=\"Building vocabulary\", initial=len(id2token) + len(special_tokens))\n    while len(id2token) < vocab_size - len(special_tokens):\n        if len(pair_frequences) == 0:\n            print(\"Not enough data to fulfil vocabulary\")\n            break\n\n        # 4.1 Find the most frequent pair and create new token\n        top_pair = pair_frequences.most_common(1)[0][0]\n        new_token = top_pair[0] + top_pair[1]\n        del pair_frequences[top_pair]\n\n        # 4.2 Add to vocabulary\n        if new_token in id2token.values():\n            continue\n        id2token[len(id2token)] = new_token\n        merges.append(top_pair)\n\n        # 4.3 Update stats and merge the top pair in all tokens\n        pair_frequences, words_by_tokens = merge(top_pair, pair_frequences, words_by_tokens)\n\n        pbar.update()\n    pbar.close()\n\n    # 5. Add special tokens\n    for special_token in special_tokens:\n        id2token[len(id2token)] = special_token\n\n    return {v: k for k, v in id2token.items()}, merges","metadata":{"id":"yk919hENEFwL","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:44.486741Z","iopub.execute_input":"2025-10-27T19:08:44.486900Z","iopub.status.idle":"2025-10-27T19:08:44.503225Z","shell.execute_reply.started":"2025-10-27T19:08:44.486888Z","shell.execute_reply":"2025-10-27T19:08:44.502718Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"# Обучаем токенизатор на тренировочных текстах\n# Для нашей задачи хватит и небольшого словаря, но можете пробовать и большего размера обучить!\n\n\nvocab, merges = train(dataset[\"train\"][\"jokes\"], vocab_size=1024, special_tokens=[\"[EOS]\"])","metadata":{"id":"iLwur-KgK990","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:08:44.505231Z","iopub.execute_input":"2025-10-27T19:08:44.505456Z","iopub.status.idle":"2025-10-27T19:18:34.430137Z","shell.execute_reply.started":"2025-10-27T19:08:44.505442Z","shell.execute_reply":"2025-10-27T19:18:34.429442Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Loading data:   0%|          | 0/135497 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"530cfcb5e7b649839aa625c0eb35c254"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Building vocabulary:  25%|##5       | 257/1024 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"224b8fabb3e94d2b840a6994b56faa34"}},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"# Посмотрим на случайные токены\n\nrandom_tokens = [512, 614, 768, 888, 1022]\nunicode_to_bytes = {v: k for k, v in bytes_to_unicode().items()}\nfor token_id in random_tokens:\n    token = [k for k, v in vocab.items() if v == token_id][0]\n    raw_bytes = bytes([unicode_to_bytes[it] for it in token])\n    print(f\"Token #{token_id}: '{raw_bytes.decode('utf-8', errors='replace')}'\")","metadata":{"id":"kcsapJUAK990","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:34.430732Z","iopub.execute_input":"2025-10-27T19:18:34.430919Z","iopub.status.idle":"2025-10-27T19:18:34.436442Z","shell.execute_reply.started":"2025-10-27T19:18:34.430897Z","shell.execute_reply":"2025-10-27T19:18:34.435977Z"}},"outputs":[{"name":"stdout","text":"Token #512: 'ус'\nToken #614: ' Э'\nToken #768: 'уз'\nToken #888: ' после'\nToken #1022: 'ались'\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"class ByteLevelBPETokenizer:\n\n    def __init__(self, vocab: dict[str, int], merges: list[tuple[str, str]], eos_token: str = \"[EOS]\"):\n        \"\"\"Byte-Level BPE Tokenizer\n\n        Args:\n            vocab: mapping from string token to id\n            merges: list of merges in prioritized order\n            eos_token: string representation of EOS token\n        \"\"\"\n        super().__init__()\n        if eos_token not in vocab:\n            raise ValueError(\"There is no EOS token in vocab\")\n        self.byte_encoder = bytes_to_unicode()\n        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}\n        self.token2id = vocab\n        self.id2token = {v: k for k, v in self.token2id.items()}\n        self.eos_token = eos_token\n        self.eos_token_id = self.token2id[eos_token]\n\n        # The closer the pair is to the beginning, the higher the rank\n        self.merges = merges\n        self.bpe_ranks = {pair: i for i, pair in enumerate(merges)}\n\n    @lru_cache\n    def bpe(self, word: tuple[str]) -> tuple[str]:\n        \"\"\"Process word into tokenized representation.\n        Word is a tuple of base tokens, i.e. bytes.\n\n        Under the hood:\n        1. Tracks the set of token pairs, bi-grams\n        2. While possible, replaces the highest-ranking pair with its union\n\n        Args:\n            word: list of base string tokens\n        Return:\n            list of BPE tokens\n        \"\"\"\n        if len(word) < 2:\n            return word\n        pairs = set((word[i], word[i + 1]) for i in range(len(word) - 1))\n\n        while pairs:\n            best_pair = None\n            best_rank = float(\"inf\")\n            for p in pairs:\n                r = self.bpe_ranks.get(p)\n                if r is not None and r < best_rank:\n                    best_rank = r\n                    best_pair = p\n            if best_pair is None:\n                break\n            a, b = best_pair\n            ab = a + b\n            i = 0\n            new_word = []\n            n = len(word)\n            while i < n:\n                if i < n - 1 and word[i] == a and word[i + 1] == b:\n                    new_word.append(ab)\n                    i += 2\n                else:\n                    new_word.append(word[i])\n                    i += 1\n            word = tuple(new_word)\n\n            if len(word) < 2:\n                break\n            pairs = set((word[i], word[i + 1]) for i in range(len(word) - 1))\n\n        return word\n\n    def encode(self, text: str, add_eos_token: bool = True) -> list[int]:\n        \"\"\"Convert string to list of token ids.\n\n        Args:\n            text: input string, may contain multiple words\n            add_eos_token: whether to add eos token id at the end\n        Return:\n            list of ints, ids of tokenized text\n        \"\"\"\n        words = WHITESPACE_SPLITTER.findall(text)\n        ids: list[int] = []\n        for w in words:\n            bt = w.encode(\"utf-8\")\n            base_tokens = tuple(self.byte_encoder[b] for b in bt)\n            bpe_tokens = self.bpe(base_tokens)\n            for t in bpe_tokens:\n                ids.append(self.token2id[t])\n        if add_eos_token:\n            ids.append(self.eos_token_id)\n        return ids\n\n    def decode(self, idx: list[int]) -> str:\n        \"\"\"Convert list of tokens' ids to text, opposite to encode method\n\n        Args:\n            idx: list of tokens' ids\n        Return:\n            string, decoded text\n        \"\"\"\n        out_parts: list[str] = []\n        byte_buf: list[int] = []\n\n        for tid in idx:\n            if tid == self.eos_token_id:\n                continue\n            tok = self.id2token[tid]\n            all_base = True\n            for ch in tok:\n                if ch not in self.byte_decoder:\n                    all_base = False\n                    break\n\n            if all_base:\n                for ch in tok:\n                    byte_buf.append(self.byte_decoder[ch])\n            else:\n                if byte_buf:\n                    out_parts.append(bytes(byte_buf).decode(\"utf-8\", errors=\"replace\"))\n                    byte_buf = []\n                out_parts.append(tok)\n\n        if byte_buf:\n            out_parts.append(bytes(byte_buf).decode(\"utf-8\", errors=\"replace\"))\n\n        return \"\".join(out_parts)\n\n\n    def push_to_hub(self, repo_id, *, private=None, token=None):\n        api = HfApi()\n        repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id\n\n        # Push the files to the repo in a single commit\n        with SoftTemporaryDirectory() as tmp:\n            save_directory = Path(tmp) / repo_id\n            save_directory.mkdir(parents=True)\n            with open(save_directory / \"vocabulary.json\", \"w\") as f_out:\n                print(json.dumps(self.token2id, indent=2), file=f_out)\n            with open(save_directory / \"merges.json\", \"w\") as f_out:\n                print(json.dumps({\"merges\": self.merges}), file=f_out)\n\n            return api.upload_folder(repo_id=repo_id, folder_path=save_directory, token=token)\n\n    @classmethod\n    def from_pretrained(cls, pretrained_model_name_or_path, *, token=None, **model_kwargs):\n        if not os.path.isdir(pretrained_model_name_or_path):\n            storage_folder = snapshot_download(repo_id=pretrained_model_name_or_path, token=token)\n        else:\n            storage_folder = pretrained_model_name_or_path\n        storage_folder = Path(storage_folder)\n        with open(storage_folder / \"vocabulary.json\", \"r\") as f_in:\n            vocab = json.load(f_in)\n        with open(storage_folder / \"merges.json\", \"r\") as f_in:\n            merges = [tuple(it) for it in json.load(f_in)[\"merges\"]]\n        return cls(vocab, merges, **model_kwargs)","metadata":{"id":"ugUz7cma1Czs","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:34.437006Z","iopub.execute_input":"2025-10-27T19:18:34.437161Z","iopub.status.idle":"2025-10-27T19:18:34.473991Z","shell.execute_reply.started":"2025-10-27T19:18:34.437149Z","shell.execute_reply":"2025-10-27T19:18:34.473380Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"# Инициализируем токенизатор\n\n\ntokenizer = ByteLevelBPETokenizer(vocab, merges)","metadata":{"id":"xRTQzO3wK991","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:34.474472Z","iopub.execute_input":"2025-10-27T19:18:34.474629Z","iopub.status.idle":"2025-10-27T19:18:34.489048Z","shell.execute_reply.started":"2025-10-27T19:18:34.474617Z","shell.execute_reply":"2025-10-27T19:18:34.488566Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"# Загружаем токенизатор на хаб\n\ntokenizer.push_to_hub(REPO_NAME)","metadata":{"id":"-9m_65vBK991","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:34.489547Z","iopub.execute_input":"2025-10-27T19:18:34.489703Z","iopub.status.idle":"2025-10-27T19:18:34.954774Z","shell.execute_reply.started":"2025-10-27T19:18:34.489691Z","shell.execute_reply":"2025-10-27T19:18:34.954278Z"}},"outputs":[{"name":"stderr","text":"No files have been modified since last commit. Skipping to prevent empty commit.\n","output_type":"stream"},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"CommitInfo(commit_url='https://huggingface.co/Amir337/llm-course-hw1/commit/ae0e4748cb7ed19a83d61d7887f08f88e564e65e', commit_message='Upload folder using huggingface_hub', commit_description='', oid='ae0e4748cb7ed19a83d61d7887f08f88e564e65e', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Amir337/llm-course-hw1', endpoint='https://huggingface.co', repo_type='model', repo_id='Amir337/llm-course-hw1'), pr_revision=None, pr_num=None)"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"# Скачиваем токенизатор с хаба\n\ntokenizer = ByteLevelBPETokenizer.from_pretrained(REPO_NAME)","metadata":{"id":"S9VohtKrK991","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:34.955336Z","iopub.execute_input":"2025-10-27T19:18:34.955552Z","iopub.status.idle":"2025-10-27T19:18:36.337859Z","shell.execute_reply.started":"2025-10-27T19:18:34.955538Z","shell.execute_reply":"2025-10-27T19:18:36.337349Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"697c0958293c444d8cbcad165c68b9d9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/2.07M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f93c050359b40f68acf4aef25374cfd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"README.md:   0%|          | 0.00/352 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e020eb52004e48019423c6937caefa49"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":".gitattributes: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68156b207c9c4949a0bed1325388f069"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/160 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"183f6554cb6e466ba74aa1291263133b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"merges.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"597228cb693847b6bda077ce1620e03b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocabulary.json: 0.00B [00:00, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"592cc32d507748c8ac4226a982e8fe0d"}},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"# Смотрим на работу токенизатора\n\ntext = \"Студенты ВШЭ знают, что экономика — это когда на лекции ты теряешь время, а на экзамене — надежду.\"\nids = tokenizer.encode(text)\nprint(ids)\nreverse_text = [tokenizer.decode([it]) for it in ids]\nprint(\"|\".join(reverse_text))\nprint(tokenizer.decode(ids))","metadata":{"id":"MFvf_Q8knPW4","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:36.338517Z","iopub.execute_input":"2025-10-27T19:18:36.338700Z","iopub.status.idle":"2025-10-27T19:18:36.343655Z","shell.execute_reply.started":"2025-10-27T19:18:36.338686Z","shell.execute_reply":"2025-10-27T19:18:36.343178Z"}},"outputs":[{"name":"stdout","text":"[468, 260, 400, 528, 272, 362, 208, 168, 208, 173, 591, 464, 44, 348, 369, 278, 335, 301, 1002, 32, 557, 420, 570, 314, 345, 383, 327, 467, 437, 289, 299, 269, 465, 806, 44, 349, 314, 369, 278, 346, 329, 286, 259, 32, 557, 606, 522, 699, 46, 1023]\nС|т|уд|ент|ы| В|�|�|�|�| зн|ают|,| что| э|к|он|ом|ика| |—| это| когда| на| л|ек|ц|ии| ты| т|ер|я|ешь| время|,| а| на| э|к|з|ам|ен|е| |—| над|еж|ду|.|\nСтуденты ВШЭ знают, что экономика — это когда на лекции ты теряешь время, а на экзамене — надежду.\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"# Посчитаем немного статистики по токенизации, определимся с размером контекста у модели\n\nlens = []\nfor text in tqdm(dataset[\"test\"][\"jokes\"]):\n    ids = tokenizer.encode(text)\n    lens.append(len(ids))\n\nprint(f\"Average token len per sample: {sum(lens) / len(lens):.2f}\")\nprint(f\"Minimum and maximum lens are: {min(lens)} and {max(lens)}\")","metadata":{"id":"_QgpwFYiK991","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:18:36.344226Z","iopub.execute_input":"2025-10-27T19:18:36.344441Z","iopub.status.idle":"2025-10-27T19:18:49.796231Z","shell.execute_reply.started":"2025-10-27T19:18:36.344428Z","shell.execute_reply":"2025-10-27T19:18:49.795690Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/15056 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"322f76c68b994ee69eca3387e1c29703"}},"metadata":{}},{"name":"stdout","text":"Average token len per sample: 73.49\nMinimum and maximum lens are: 5 and 3418\n","output_type":"stream"}],"execution_count":18},{"cell_type":"markdown","source":"Должно получиться в среднем по 70 токенов на последовательность.\nКонтекста в 128 токенов будет вполне достаточно.","metadata":{"id":"OwN3mfmznPW5"}},{"cell_type":"markdown","source":"# Модель [5 баллов]\n\nВ качестве модели реализуем трансформер, в котором\n1. В качестве позиционных эмбеддингов используется ALiBi\n2. Механизм внимания использует GQA\n3. В Feed-Forward блоке SwiGLU","metadata":{"id":"Ub1FljGXC7I-"}},{"cell_type":"code","source":"# Для удобства заведем конфиг для модели\n\n\n@dataclass\nclass TransformerConfig:\n    n_layer: int\n    n_head: int\n    n_kv_head: int\n    hidden_dim: int\n    intermediate_dim: int\n    dropout: float = 0.1\n    vocab_size: int = 1024\n    max_seq_len: int = 128\n    use_rope: bool = False\n    rope_theta: float = 10000.0\n    use_mla: bool = False\n    latent_dim: int = 128\n\n\nmodel_configs = {\n    \"nano\": TransformerConfig(n_layer=3, n_head=4, n_kv_head=2, hidden_dim=96, intermediate_dim=256),\n    \"mini\": TransformerConfig(n_layer=6, n_head=6, n_kv_head=3, hidden_dim=384, intermediate_dim=1024),\n    \"small\": TransformerConfig(n_layer=12, n_head=12, n_kv_head=6, hidden_dim=768, intermediate_dim=2048),\n}","metadata":{"id":"ipZrCvkmK992","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:54:52.887562Z","iopub.execute_input":"2025-10-27T20:54:52.888122Z","iopub.status.idle":"2025-10-27T20:54:52.893683Z","shell.execute_reply.started":"2025-10-27T20:54:52.888101Z","shell.execute_reply":"2025-10-27T20:54:52.892978Z"}},"outputs":[],"execution_count":75},{"cell_type":"code","source":"class CausalSelfAttentionMLA(nn.Module):\n    def __init__(self, config: TransformerConfig):\n        super().__init__()\n        self.config = config\n        assert self.config.hidden_dim % self.config.n_head == 0\n        assert self.config.n_head % self.config.n_kv_head == 0\n        self.head_dim = self.config.hidden_dim // self.config.n_head\n        self.scale = self.head_dim ** -0.5\n        self.q_per_kv = self.config.n_head // self.config.n_kv_head\n\n        self.q_proj = nn.Linear(self.config.hidden_dim, self.config.n_head * self.head_dim, bias=False)\n\n        self.c_proj   = nn.Linear(self.config.hidden_dim, self.config.latent_dim, bias=False)\n        self.k_dec    = nn.Linear(self.config.latent_dim, self.config.n_kv_head * self.head_dim, bias=False)\n        self.v_dec    = nn.Linear(self.config.latent_dim, self.config.n_kv_head * self.head_dim, bias=False)\n\n        self.out_proj = nn.Linear(self.config.n_head * self.head_dim, self.config.hidden_dim, bias=False)\n        self.attn_dropout = nn.Dropout(self.config.dropout)\n        mask = torch.tril(torch.ones(self.config.max_seq_len, self.config.max_seq_len, dtype=torch.bool))\n        self.register_buffer(\"causal_mask\", mask.view(1, 1, self.config.max_seq_len, self.config.max_seq_len), persistent=False)\n\n        n = self.config.n_head\n        if (n & (n - 1)) == 0:\n            start = 2 ** (-8.0 / n); slopes = [start ** i for i in range(1, n + 1)]\n        else:\n            closest = 1 << (n.bit_length() - 1)\n            start1 = 2 ** (-8.0 / closest); slopes1 = [start1 ** i for i in range(1, closest + 1)]\n            start2 = 2 ** (-8.0 / (2 * closest)); slopes2 = [start2 ** i for i in range(1, 2 * closest + 1)]\n            slopes = slopes1 + slopes2[: n - closest]\n        self.register_buffer(\"alibi\", torch.tensor(slopes, dtype=torch.float32).view(1, n, 1, 1), persistent=False)\n\n        if self.config.use_rope:\n            half = self.head_dim // 2\n            inv_freq = 1.0 / (self.config.rope_theta ** (torch.arange(half, dtype=torch.float32) / half))\n            self.register_buffer(\"rope_inv_freq\", inv_freq, persistent=False)\n\n    def forward(self, x: Tensor, attention_mask: Tensor = None) -> Tensor:\n        B, L, _ = x.shape\n\n        # Q\n        q = self.q_proj(x).view(B, L, self.config.n_head, self.head_dim).permute(0, 2, 1, 3)\n\n        c  = self.c_proj(x)\n        kd = self.k_dec(c).view(B, L, self.config.n_kv_head, self.head_dim)\n        vd = self.v_dec(c).view(B, L, self.config.n_kv_head, self.head_dim)\n        k = kd.permute(0, 2, 1, 3)\n        v = vd.permute(0, 2, 1, 3)\n\n        if self.q_per_kv > 1:\n            k = k.repeat_interleave(self.q_per_kv, dim=1)\n            v = v.repeat_interleave(self.q_per_kv, dim=1)\n\n        if self.config.use_rope:\n            t = torch.arange(L, device=x.device, dtype=torch.float32)\n            freqs = torch.outer(t, self.rope_inv_freq)\n            cos = freqs.cos().to(q.dtype).view(1, 1, L, -1)\n            sin = freqs.sin().to(q.dtype).view(1, 1, L, -1)\n            h = self.head_dim // 2\n\n            q1, q2 = q[..., :h], q[..., h:]\n            k1, k2 = k[..., :h], k[..., h:]\n            q = torch.cat([q1 * cos - q2 * sin, q2 * cos + q1 * sin], dim=-1)\n            k = torch.cat([k1 * cos - k2 * sin, k2 * cos + k1 * sin], dim=-1)\n\n        att = torch.matmul(q, k.transpose(-2, -1)) * self.scale  # [B,H,L,L]\n\n        if not self.config.use_rope:\n            i = torch.arange(L, device=x.device).view(1, 1, L, 1)\n            j = torch.arange(L, device=x.device).view(1, 1, 1, L)\n            att = att + (self.alibi.to(att.dtype) * (j - i))\n\n        att = att.masked_fill(~self.causal_mask[:, :, :L, :L], torch.finfo(att.dtype).min)\n        if attention_mask is not None:\n            key_mask = attention_mask if attention_mask.dim() == 2 else attention_mask[..., 0]\n            key_mask = (key_mask > 0).view(B, 1, 1, L)\n            att = att.masked_fill(~key_mask, torch.finfo(att.dtype).min)\n\n        att = F.softmax(att, dim=-1)\n        att = self.attn_dropout(att)\n\n        y = torch.matmul(att, v)\n        y = y.permute(0, 2, 1, 3).contiguous().view(B, L, self.config.n_head * self.head_dim)\n        y = self.out_proj(y)\n        return y\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:54:53.325127Z","iopub.execute_input":"2025-10-27T20:54:53.325328Z","iopub.status.idle":"2025-10-27T20:54:53.340770Z","shell.execute_reply.started":"2025-10-27T20:54:53.325314Z","shell.execute_reply":"2025-10-27T20:54:53.340205Z"}},"outputs":[],"execution_count":76},{"cell_type":"code","source":"class RMSNorm(nn.Module):\n    def __init__(self, dim: int, eps: float = 1e-6):\n        \"\"\"Root Mean Square Layer Normalization\n\n        Args:\n            dim: Feature dimension\n            eps: Small constant for numerical stability\n        \"\"\"\n        super().__init__()\n        self.eps = eps\n        self.scale = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x: Tensor) -> Tensor:\n        rms = x.pow(2).mean(dim=-1, keepdim=True)\n        x_norm = x * torch.rsqrt(rms + self.eps)\n        return self.scale * x_norm\n\n\nclass CausalSelfAttention(nn.Module):\n    def __init__(self, config: TransformerConfig):\n        \"\"\"Causal Self-Attention with support of\n        Grouped-Query Attention and ALiBi for positional encoding\n        \"\"\"\n        super().__init__()\n        self.config = config\n        assert self.config.hidden_dim % self.config.n_head == 0\n        assert self.config.n_head % self.config.n_kv_head == 0\n        self.head_dim = self.config.hidden_dim // self.config.n_head\n        self.scale = self.head_dim**-0.5\n        self.q_per_kv = self.config.n_head // self.config.n_kv_head\n\n        # Init projection layers\n        self.q_proj  = nn.Linear(self.config.hidden_dim, self.config.n_head * self.head_dim, bias=False)\n        self.kv_proj = nn.Linear(self.config.hidden_dim, self.config.n_kv_head * self.head_dim * 2, bias=False)\n        self.out_proj = nn.Linear(self.config.n_head * self.head_dim, self.config.hidden_dim, bias=False)\n\n        self.attn_dropout = nn.Dropout(self.config.dropout)\n\n        self.register_buffer(\"causal_mask\", self._create_causal_mask(self.config.max_seq_len))\n        self.register_buffer(\"alibi\", self._build_alibi_bias(self.config.n_head))\n\n        # тут RoPE\n        half = self.head_dim // 2\n        inv = 1.0 / (self.config.rope_theta ** (torch.arange(half, dtype=torch.float32) / half))\n        self.register_buffer(\"rope_inv_freq\", inv, persistent=False)\n\n    def _build_alibi_bias(self, num_heads: int) -> Tensor:\n        \"\"\"Build ALiBi for specified number of heads:\n\n        Returns:\n            Tensor with ALiBi biases, shape: [1, num heads, 1, 1]\n        \"\"\"\n        if (num_heads & (num_heads - 1)) == 0:\n            start = 2 ** (-8.0 / num_heads)\n            slopes = [start ** i for i in range(1, num_heads + 1)]\n        else:\n            closest = 1 << (num_heads.bit_length() - 1)\n            start1 = 2 ** (-8.0 / closest)\n            slopes1 = [start1 ** i for i in range(1, closest + 1)]\n            start2 = 2 ** (-8.0 / (2 * closest))\n            slopes2 = [start2 ** i for i in range(1, 2 * closest + 1)]\n            slopes = slopes1 + slopes2[: num_heads - closest]\n        return torch.tensor(slopes, dtype=torch.float32).view(1, num_heads, 1, 1)\n\n    def _create_causal_mask(self, max_seq_len: int) -> Tensor:\n        \"\"\"Create causal mask with ones where tokens can attend to each other.\n\n        Returns:\n            Tensor with causal mask, shape: [1, 1, seq len, seq len]\n        \"\"\"\n        mask = torch.tril(torch.ones(max_seq_len, max_seq_len, dtype=torch.bool))\n        return mask.view(1, 1, max_seq_len, max_seq_len)\n\n    def forward(self, x: Tensor, attention_mask: Tensor = None) -> Tensor:\n        \"\"\"Apply Self-Attention to input data with respect to pad tokens.\n\n        Args:\n            x: input tensor, shape [bs, seq len, hidden dim]\n            attention_mask: mask with zeros for pad tokens, shape [bs, seq len, hidden dim]\n        Returns:\n            result tensor, shape [bs, seq len, hidden dim]\n        \"\"\"\n        B, L, Z = x.shape\n\n        q = self.q_proj(x).view(B, L, self.config.n_head, self.head_dim).permute(0, 2, 1, 3)\n        kv = self.kv_proj(x).view(B, L, self.config.n_kv_head, 2, self.head_dim)\n        k = kv[:, :, :, 0, :].permute(0, 2, 1, 3)\n        v = kv[:, :, :, 1, :].permute(0, 2, 1, 3)\n\n        if self.q_per_kv > 1:\n            k = k.repeat_interleave(self.q_per_kv, dim=1)\n            v = v.repeat_interleave(self.q_per_kv, dim=1)\n\n        #RoPE\n        if self.config.use_rope:\n            t = torch.arange(L, device=x.device, dtype=torch.float32)\n            freqs = torch.outer(t, self.rope_inv_freq)  # [L, half]\n            cos = freqs.cos().to(q.dtype).view(1, 1, L, -1)\n            sin = freqs.sin().to(q.dtype).view(1, 1, L, -1)\n\n            half = self.head_dim // 2\n            q1, q2 = q[..., :half], q[..., half:]\n            k1, k2 = k[..., :half], k[..., half:]\n            q = torch.cat([q1 * cos - q2 * sin, q2 * cos + q1 * sin], dim=-1)\n            k = torch.cat([k1 * cos - k2 * sin, k2 * cos + k1 * sin], dim=-1)\n\n        \n        att = torch.matmul(q, k.transpose(-2, -1)) * self.scale\n\n        if not self.config.use_rope:\n            i = torch.arange(L, device=x.device).view(1, 1, L, 1)\n            j = torch.arange(L, device=x.device).view(1, 1, 1, L)\n            att = att + (self.alibi.to(att.dtype) * (j - i))\n\n        causal = self.causal_mask[:, :, :L, :L]\n        att = att.masked_fill(~causal, torch.finfo(att.dtype).min)\n\n        if attention_mask is not None:\n            key_mask = attention_mask\n            if key_mask.dim() == 3:\n                key_mask = key_mask[..., 0]\n            key_mask = (key_mask > 0).view(B, 1, 1, L)\n            att = att.masked_fill(~key_mask, torch.finfo(att.dtype).min)\n\n        att = F.softmax(att, dim=-1)\n        att = self.attn_dropout(att)\n\n        y = torch.matmul(att, v)\n        y = y.permute(0, 2, 1, 3).contiguous().view(B, L, self.config.n_head * self.head_dim)\n        y = self.out_proj(y)\n        return y\n\n\nclass SwiGLU(nn.Module):\n    def __init__(self, config: TransformerConfig):\n        \"\"\"Gated Liner Unit with Swish Activation\"\"\"\n        super().__init__()\n        self.config = config\n        # Init up- and down- projection layers\n        self.fc1 = nn.Linear(config.hidden_dim, 2 * config.intermediate_dim, bias=True)\n        self.fc2 = nn.Linear(config.intermediate_dim, config.hidden_dim, bias=True)\n\n    def forward(self, x: Tensor) -> Tensor:\n        \"\"\"Apply SwiGLU to input data.\n\n        Args:\n            x: input tensor, shape [bs, seq len, hidden dim]\n        Returns:\n            result tensor, shape [bs, seq len, hidden dim]\n        \"\"\"\n        h = self.fc1(x)\n        a, b = h.chunk(2, dim=-1)\n        h = F.silu(a) * b\n        h = self.fc2(h)\n        return h\n\n\nclass Block(nn.Module):\n    def __init__(self, config: TransformerConfig):\n        \"\"\"Base Transformer Block\n        - Causal Self-Attention and SwiGLU as main elements\n        - Pre-normalization via RMSNorm\n        - Regularization with dropouts before residuals\n        \"\"\"\n        super().__init__()\n        self.ln_1 = RMSNorm(config.hidden_dim)\n        self.res_dropout_1 = nn.Dropout(config.dropout)\n        self.attn = CausalSelfAttentionMLA(config) if config.use_mla else CausalSelfAttention(config)\n\n        self.ln_2 = RMSNorm(config.hidden_dim)\n        self.res_dropout_2 = nn.Dropout(config.dropout)\n        self.mlp = SwiGLU(config)\n\n    def forward(self, x: Tensor, attention_mask: Tensor = None) -> Tensor:\n        \"\"\"Apply Transformer Block to input data.\n\n        Args:\n            x: input tensor, shape [bs, seq len, hidden dim]\n            attention_mask: mask with zeros for pad tokens, shape [bs, seq len, hidden dim]\n        Returns:\n            result tensor, shape [bs, seq len, hidden dim]\n        \"\"\"\n        x = x + self.res_dropout_1(self.attn(self.ln_1(x), attention_mask=attention_mask))\n        x = x + self.res_dropout_2(self.mlp(self.ln_2(x)))\n        return x\n\n\nclass TransformerForCausalLM(nn.Module, PyTorchModelHubMixin):\n    def __init__(self, config: TransformerConfig):\n        \"\"\"Transformer model for Language Modeling\"\"\"\n        super().__init__()\n        self.vocab_size = config.vocab_size\n        self.max_seq_len = config.max_seq_len\n        self.n_layer = config.n_layer\n        self.n_head = config.n_head\n        self.hidden_dim = config.hidden_dim\n        self.dropout = config.dropout\n\n        self.token_emb = nn.Embedding(config.vocab_size, config.hidden_dim)\n        self.emb_dropout = nn.Dropout(config.dropout)\n        self.layers = nn.ModuleList([Block(config) for _ in range(config.n_layer)])\n        self.ln_final = RMSNorm(config.hidden_dim)\n        self.lm_head = nn.Linear(config.hidden_dim, config.vocab_size, bias=False)\n\n        self.apply(self._init_weights)\n\n        n_params = sum(p.numel() for p in self.parameters())\n        print(f\"Number of parameters: {n_params / 1e6:.2f}M\")\n\n    def _init_weights(self, module):\n        if isinstance(module, nn.Linear):\n            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n            if module.bias is not None:\n                torch.nn.init.zeros_(module.bias)\n        elif isinstance(module, nn.Embedding):\n            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)\n        elif isinstance(module, RMSNorm):\n            torch.nn.init.ones_(module.scale)\n\n    def forward(self, input_ids: torch.Tensor, attention_mask: torch.Tensor = None) -> Tensor:\n        \"\"\"Calculate logits for given input ids.\n\n        Args:\n            x: input tensor, shape [bs, seq len, hidden dim]\n            attention_mask: mask with zeros for pad tokens, shape [bs, seq len, hidden dim]\n        Returns:\n            logits, shape [bs, seq len, hidden dim]\n        \"\"\"\n        B, L = input_ids.shape\n        x = self.token_emb(input_ids)\n        x = self.emb_dropout(x)\n        for layer in self.layers:\n            x = layer(x, attention_mask=attention_mask)\n        x = self.ln_final(x)\n        logits = self.lm_head(x)\n        return logits\n\n    @torch.inference_mode()\n    def generate(\n        self, idx: Tensor, max_new_tokens, eos_token_id, temperature=1.0, do_sample=False, top_k=None\n    ) -> Tensor:\n        \"\"\"Take a conditioning sequence of indices and complete the sequence max_new_tokens times,\n        feeding the predictions back into the model each time.\n\n        Args:\n            idx: tensor with conditional tokens, shape [seq len]\n            max_new_tokens: maximum number of new tokens\n            eos_token_id: index of EOS token to stop generation\n            temperature, do_sample, top_k: generation parameters\n        Return:\n            tensor with generated indexes\n        \"\"\"\n        for _ in range(max_new_tokens):\n            idx_cond = idx if idx.shape[1] <= self.max_seq_len else idx[:, -self.max_seq_len :]\n            logits = self(idx_cond)\n\n            # 1. Pluck the logits at the final step and scale by desired temperature\n            logits = logits[:, -1, :] / max(temperature, 1e-8)\n\n            # 2. Optionally crop the logits to only the top k options\n            if top_k is not None:\n                k = min(top_k, logits.size(-1))\n                topk_vals, _ = torch.topk(logits, k, dim=-1)\n                cutoff = topk_vals[:, [-1]]\n                logits = logits.masked_fill(logits < cutoff, float(\"-inf\"))\n\n            # 3. apply softmax to convert logits to probabilities\n            probs = F.softmax(logits, dim=-1)\n\n            # 4. Either sample from the distribution or take the most likely element\n            if do_sample:\n                idx_next = torch.multinomial(probs, num_samples=1)  \n            else:\n                idx_next = torch.argmax(probs, dim=-1, keepdim=True)\n\n            # 5. Append sampled index to the running sequence and continue\n            idx = torch.cat((idx, idx_next), dim=1)\n            if idx_next == eos_token_id:\n                break\n        return idx","metadata":{"id":"aYXMb5PEDFkt","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:54:55.030416Z","iopub.execute_input":"2025-10-27T20:54:55.030878Z","iopub.status.idle":"2025-10-27T20:54:55.060062Z","shell.execute_reply.started":"2025-10-27T20:54:55.030861Z","shell.execute_reply":"2025-10-27T20:54:55.059508Z"}},"outputs":[],"execution_count":77},{"cell_type":"markdown","source":"# Train Loop [1 + 1 баллов]\n\nНастало время обучать модель.\nНебольшую можно пробовать обучать локально, но лучше всего воспользоваться GPU, например, на Google Colab.\n\nЗа реализацию 1 балл, и еще 1 балл - если модель научилась генерить анекдоты.\n\nНе забудьте проверить, что вы загрузили нужные веса на HF и у проверяющего скачается нужная версия.","metadata":{"id":"IhdavMmSDujw"}},{"cell_type":"code","source":"# Определим датасет и как заворачивать семплы в батч\n# Разные тексты имеют разную длину, поэтому будет падить до самого длина семпла\n# Так же заведем дополнительную маску, чтобы механизм внимания не учитывал падинги\n\n\nclass TextDataset(torch.utils.data.Dataset):\n    def __init__(self, texts, tokenizer):\n        self.texts = texts\n        self.tokenizer = tokenizer\n\n    def __len__(self):\n        return len(self.texts)\n\n    def __getitem__(self, idx):\n        texts = self.texts[idx]\n        tokenized_sequence = self.tokenizer.encode(texts)\n        return tokenized_sequence\n\n\ndef data_collator(\n    tokenized_sequences: list[list[int]], pad_token_id: int, max_seq_len: int = None\n) -> tuple[torch.Tensor, torch.Tensor]:\n    batch_size = len(tokenized_sequences)\n    max_batch_seq_len = min(max_seq_len, max((len(it) for it in tokenized_sequences)))\n\n    input_ids = torch.full((batch_size, max_batch_seq_len), pad_token_id, dtype=torch.long)\n    attention_mask = torch.zeros((batch_size, max_batch_seq_len), dtype=torch.bool)\n\n    for i, tok_seq in enumerate(tokenized_sequences):\n        cur_len = min(len(tok_seq), max_batch_seq_len)\n        input_ids[i, :cur_len] = torch.tensor(tok_seq[:cur_len], dtype=torch.long)\n        attention_mask[i, :cur_len] = 1\n\n    return input_ids, attention_mask\n\n\ndef create_dataloader(dataset, pad_token_id, max_seq_len, batch_size, is_train):\n    collate_fn = partial(data_collator, pad_token_id=pad_token_id, max_seq_len=max_seq_len)\n    return DataLoader(\n        dataset, batch_size=batch_size, shuffle=is_train, drop_last=is_train, collate_fn=collate_fn, pin_memory=True\n    )\n\n\n_d = TextDataset([\"Привет!\", \"Как твои дела?\", \"Осталось совсем немного до конца\"], tokenizer)\n_dl = create_dataloader(_d, tokenizer.eos_token_id, max_seq_len=16, batch_size=2, is_train=False)\n\nfor i, batch in enumerate(_dl):\n    print(f\"Batch #{i}\")\n    input_ids, attn_mask = batch\n    print(input_ids, attn_mask, sep=\"\\n\\n\")","metadata":{"id":"1wtAiR_aDxed","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:54:30.649529Z","iopub.execute_input":"2025-10-27T19:54:30.650084Z","iopub.status.idle":"2025-10-27T19:54:30.660259Z","shell.execute_reply.started":"2025-10-27T19:54:30.650068Z","shell.execute_reply":"2025-10-27T19:54:30.659682Z"}},"outputs":[{"name":"stdout","text":"Batch #0\ntensor([[ 726,  347,  281,   33, 1023, 1023, 1023, 1023],\n        [ 527,  303, 1009,  261,  576,  258,   63, 1023]])\n\ntensor([[ True,  True,  True,  True,  True, False, False, False],\n        [ True,  True,  True,  True,  True,  True,  True,  True]])\nBatch #1\ntensor([[ 488,  294,  298,  644,  827,  263,  323,  276,  323,  531,  692,  596,\n          880, 1023]])\n\ntensor([[True, True, True, True, True, True, True, True, True, True, True, True,\n         True, True]])\n","output_type":"stream"}],"execution_count":42},{"cell_type":"code","source":"def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps):\n    \"\"\"Scheduler for Optimizer with linear warmup and linear decay to the end of training\n\n    Args:\n        optimizer: torch optimizer to control learning rate\n        num_warmup_steps: number of warmup steps\n        num_training_steps: total number of training steps\n    Return:\n        torch learning rate scheduler\n    \"\"\"\n    assert num_training_steps >= num_warmup_steps\n\n    def lr_lambda(current_step):\n        if current_step < num_warmup_steps:\n            return float(current_step) / max(1, num_warmup_steps)\n        return max(\n            0.0,\n            float(num_training_steps - current_step) / max(1, num_training_steps - num_warmup_steps),\n        )\n\n    return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)\n\n\ndef cross_entropy_loss(input_ids: Tensor, attention_mask: Tensor, logits: Tensor) -> Tensor:\n    \"\"\"Calculate Cross-Entropy loss for Language Modeling task\n    Under the hood:\n    1. Create targtes based on input ids\n    2. Masked out tokens corresponded to paddings\n    3. Calculate cross entropy loss\n\n    Args:\n        input_ids: tensor with input ids, shape [bs, seq len]\n        attention_mask: mask with zeros for pad tokens, shape [bs, seq len]\n        logits: predicted logits, shape [bs, seq len, vocab size]\n    Return:\n        cross entropy loss, single-item tensor\n    \"\"\"\n    n_logits = logits[:, :-1, :].contiguous()\n    labels = input_ids[:, 1:].contiguous()\n    mask   = attention_mask[:, 1:].contiguous()\n\n    # лосс покадрово без усреднения\n    loss_tok = F.cross_entropy(\n        n_logits.view(-1, n_logits.size(-1)),\n        labels.view(-1),\n        reduction=\"none\",\n    ).view_as(mask)\n\n    valid = mask.float()\n    loss = (loss_tok * valid).sum() / valid.sum().clamp_min(1.0)\n    return loss","metadata":{"id":"i719AOdQK993","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:54:30.697377Z","iopub.execute_input":"2025-10-27T19:54:30.697550Z","iopub.status.idle":"2025-10-27T19:54:30.703112Z","shell.execute_reply.started":"2025-10-27T19:54:30.697536Z","shell.execute_reply":"2025-10-27T19:54:30.702615Z"}},"outputs":[],"execution_count":43},{"cell_type":"code","source":"# Определим тренера с наиболее важными гиперпараметрами для обучения\n\n\nclass Trainer:\n\n    def __init__(\n        self,\n        learning_rate=3e-4,\n        weight_decay=0.01,\n        clip_grad_norm=1.0,\n        n_steps=10_000,\n        val_every_n_steps=1_000,\n        plot_every_n_steps=100,\n    ):\n        self.learning_rate = learning_rate\n        self.weight_decay = weight_decay\n        self.clip_grad_norm = clip_grad_norm\n        self.n_steps = n_steps\n        self.val_every_n_steps = val_every_n_steps\n        self.plot_every_n_steps = plot_every_n_steps\n\n        if torch.cuda.is_available():\n            self.device = \"cuda\"\n        elif torch.backends.mps.is_available():\n            self.device = \"mps\"\n        else:\n            self.device = \"cpu\"\n        print(\"running on device\", self.device)\n\n    @torch.no_grad()\n    def validate(self, model, val_loader):\n        model.eval()\n        val_loss = 0.0\n        for batch in tqdm(val_loader, desc=\"Validating\", leave=False):\n            input_ids, attention_mask = batch\n            input_ids = input_ids.to(self.device, non_blocking=True).long()\n            attention_mask = attention_mask.to(self.device, non_blocking=True)\n\n            logits = model(input_ids, attention_mask)  # [bs; seq len; vocab size]\n            val_loss += cross_entropy_loss(input_ids, attention_mask, logits)\n        return val_loss / len(val_loader)\n\n    def run(self, model, train_loader, val_loader):\n        model = model.to(self.device)\n        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)\n        scheduler = get_linear_schedule_with_warmup(\n            optimizer, num_warmup_steps=0.1 * self.n_steps, num_training_steps=self.n_steps\n        )\n        model.train()\n\n        plotlosses = PlotLosses(figsize=(15, 9), step_names=\"Step\")\n        logs = {\"lr\": 0, \"epoch\": 0}\n\n        data_iter = iter(train_loader)\n        for iter_num in range(self.n_steps):\n            try:\n                batch = next(data_iter)\n            except StopIteration:\n                data_iter = iter(train_loader)\n                logs[\"epoch\"] += 1\n                batch = next(data_iter)\n\n            input_ids, attention_mask = batch\n            input_ids = input_ids.to(self.device, non_blocking=True)\n            attention_mask = attention_mask.to(self.device, non_blocking=True)\n\n            logits = model(input_ids, attention_mask)  # [bs; seq len; vocab size]\n            loss = cross_entropy_loss(input_ids, attention_mask, logits)\n\n            # backprop and update the parameters\n            model.zero_grad(set_to_none=True)\n            loss.backward()\n            torch.nn.utils.clip_grad_norm_(model.parameters(), self.clip_grad_norm)\n            optimizer.step()\n            scheduler.step()\n\n            if iter_num > 0 and iter_num % self.val_every_n_steps == 0:\n                val_loss = self.validate(model, val_loader)\n                plotlosses.update({\"val_loss\": val_loss.item()}, current_step=iter_num)\n                plotlosses.send()\n                model.train()\n\n            if iter_num % self.plot_every_n_steps == 0:\n                logs[\"loss\"] = loss.item()\n                logs[\"lr\"] = scheduler.get_last_lr()[0]\n                plotlosses.update(logs, current_step=iter_num)\n                plotlosses.send()\n\n        val_loss = self.validate(model, val_loader)\n        plotlosses.update({\"val_loss\": val_loss.item()}, current_step=iter_num)\n        plotlosses.send()","metadata":{"id":"SPYdF52zXtoX","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:54:30.783013Z","iopub.execute_input":"2025-10-27T19:54:30.783417Z","iopub.status.idle":"2025-10-27T19:54:30.793399Z","shell.execute_reply.started":"2025-10-27T19:54:30.783402Z","shell.execute_reply":"2025-10-27T19:54:30.792913Z"}},"outputs":[],"execution_count":44},{"cell_type":"code","source":"# Создаем тренировочный и тестовые даталоадеры\n\n\nMAX_SEQ_LEN = 128\nBATCH_SIZE = 16\n\ntrain_dataset = TextDataset(dataset[\"train\"][\"jokes\"], tokenizer)\ntrain_dataloader = create_dataloader(\n    train_dataset, tokenizer.eos_token_id, max_seq_len=MAX_SEQ_LEN, batch_size=BATCH_SIZE, is_train=True\n)\n\ntest_dataset = TextDataset(dataset[\"test\"][\"jokes\"], tokenizer)\ntest_dataloader = create_dataloader(\n    test_dataset, tokenizer.eos_token_id, max_seq_len=MAX_SEQ_LEN, batch_size=BATCH_SIZE, is_train=False\n)","metadata":{"id":"yK1BpJflMTAi","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:54:30.794289Z","iopub.execute_input":"2025-10-27T19:54:30.794455Z","iopub.status.idle":"2025-10-27T19:54:30.807664Z","shell.execute_reply.started":"2025-10-27T19:54:30.794442Z","shell.execute_reply":"2025-10-27T19:54:30.807176Z"}},"outputs":[],"execution_count":45},{"cell_type":"code","source":"# Инициализируем модель\n\nconfig = model_configs[\"small\"]\nconfig.use_rope = True\nconfig.rope_theta = 10000.0\nmodel = TransformerForCausalLM(config)","metadata":{"id":"53jHSgMZECGl","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:55:01.252088Z","iopub.execute_input":"2025-10-27T19:55:01.252639Z","iopub.status.idle":"2025-10-27T19:55:02.452074Z","shell.execute_reply.started":"2025-10-27T19:55:01.252619Z","shell.execute_reply":"2025-10-27T19:55:02.451366Z"}},"outputs":[{"name":"stdout","text":"Number of parameters: 79.51M\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"# Инициализируем тренера\n\ntrainer = Trainer(learning_rate=3e-4)","metadata":{"id":"uMUsjHl4Nkoa","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:55:10.188082Z","iopub.execute_input":"2025-10-27T19:55:10.188782Z","iopub.status.idle":"2025-10-27T19:55:10.191837Z","shell.execute_reply.started":"2025-10-27T19:55:10.188754Z","shell.execute_reply":"2025-10-27T19:55:10.191314Z"}},"outputs":[{"name":"stdout","text":"running on device cuda\n","output_type":"stream"}],"execution_count":48},{"cell_type":"code","source":"# Обучение goes brrrr!\n\ntrainer.run(model, train_dataloader, test_dataloader)","metadata":{"id":"1nUgUfpKK993","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T19:55:12.429582Z","iopub.execute_input":"2025-10-27T19:55:12.430297Z","iopub.status.idle":"2025-10-27T20:33:13.569608Z","shell.execute_reply.started":"2025-10-27T19:55:12.430271Z","shell.execute_reply":"2025-10-27T20:33:13.569066Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x900 with 4 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           \t (min:    0.000, max:    1.000, cur:    1.000)\nLoss\n\ttraining         \t (min:    2.352, max:    7.058, cur:    2.462)\n\tvalidation       \t (min:    2.624, max:    4.044, cur:    2.624)\nlr\n\tlr               \t (min:    0.000, max:    0.000, cur:    0.000)\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"# Смотрим на качество генерации глазами\n# Для маленьких и слабых моделей \"затягиваем\" гайки генерации\n\ntext = \"Заходит в бар\"\ninput_ids = torch.tensor(tokenizer.encode(text)[:-1], device=trainer.device)[None, :]\nprint(input_ids)\nmodel_output = model.generate(\n    input_ids, max_new_tokens=200, eos_token_id=tokenizer.eos_token_id, do_sample=True, top_k=10\n)\ntokenizer.decode(model_output[0].tolist())","metadata":{"id":"94zNDLEqdQow","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:47:56.568838Z","iopub.execute_input":"2025-10-27T20:47:56.569424Z","iopub.status.idle":"2025-10-27T20:47:57.877185Z","shell.execute_reply.started":"2025-10-27T20:47:56.569401Z","shell.execute_reply":"2025-10-27T20:47:57.876656Z"}},"outputs":[{"name":"stdout","text":"tensor([[717, 258, 618, 275, 302, 338]], device='cuda:0')\n","output_type":"stream"},{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"'Заходит в бар с алкоголем, весь в баре.\\n - Давай. Я хочу спать с тобой, - говорит он, - а я - пиво.\\n - Пойди, - говорит дружок, - я вчера сегодня по пьяни не пил, а сейчас -\\n а я в штопор схожу.\\n '"},"metadata":{}}],"execution_count":68},{"cell_type":"markdown","source":"Поиграйтесь с гиперпараметрами, попробуйте обучить `mini` и `small` версии.\nПостарайтесь добиться как можно более высокого качества как в терминах лосса, так и при визуальной оценке генерации.\n\n### Дополнительные баллы\n\nВы также можно заработать дополнительные баллы:\n- Реализовать Rotary Positional Embedding **[2 балла]**\n- Реализовать Multi-Head Latent Attention **[1 балл]**\n- Оформить репозиторий на 🤗: карточка модели с описанием задания, репортом качества и примерами генерации **[1 балл]**","metadata":{"id":"0WrMwV9zK993"}},{"cell_type":"code","source":"# Загружаем модель на хаб\n\nmodel.push_to_hub(REPO_NAME)","metadata":{"id":"YFi7M9ExHWv9","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:36:38.794788Z","iopub.execute_input":"2025-10-27T20:36:38.795474Z","iopub.status.idle":"2025-10-27T20:36:59.712695Z","shell.execute_reply.started":"2025-10-27T20:36:38.795449Z","shell.execute_reply":"2025-10-27T20:36:59.712182Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Processing Files (0 / 0): |          |  0.00B /  0.00B            ","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7e35e0545821430893e8996cfc34a317"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"New Data Upload: |          |  0.00B /  0.00B            ","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"80513ed0a1414907a8de92716c8e4edc"}},"metadata":{}},{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"CommitInfo(commit_url='https://huggingface.co/Amir337/llm-course-hw1/commit/0c73c6964bba59ea2cf758799211e7bd9304e0ec', commit_message='Push model using huggingface_hub.', commit_description='', oid='0c73c6964bba59ea2cf758799211e7bd9304e0ec', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Amir337/llm-course-hw1', endpoint='https://huggingface.co', repo_type='model', repo_id='Amir337/llm-course-hw1'), pr_revision=None, pr_num=None)"},"metadata":{}}],"execution_count":53},{"cell_type":"markdown","source":"Сделал все бонусы.\nРепа: https://huggingface.co/Amir337/llm-course-hw1","metadata":{}},{"cell_type":"markdown","source":"# Специальный раздел для проверяющего","metadata":{"id":"so8bIDy5dKXM"}},{"cell_type":"code","source":"device = torch.device(\"cuda\")\n\ncfg = TransformerConfig(n_layer=12, n_head=12, n_kv_head=6, hidden_dim=768, intermediate_dim=2048,use_rope=True, rope_theta=10000.0)\ntokenizer = ByteLevelBPETokenizer.from_pretrained(REPO_NAME)\ncheck_model = TransformerForCausalLM.from_pretrained(REPO_NAME, config=cfg)\ncheck_model = check_model.to(device)\ncheck_model = check_model.eval()","metadata":{"id":"0ZplshN5HtRb","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:55:03.203814Z","iopub.execute_input":"2025-10-27T20:55:03.204602Z","iopub.status.idle":"2025-10-27T20:55:04.718824Z","shell.execute_reply.started":"2025-10-27T20:55:03.204574Z","shell.execute_reply":"2025-10-27T20:55:04.718267Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"Fetching 6 files:   0%|          | 0/6 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"594db3eb71ca45da8f44c160765eeb52"}},"metadata":{}},{"name":"stdout","text":"Number of parameters: 79.51M\n","output_type":"stream"}],"execution_count":78},{"cell_type":"code","source":"text = \"Штирлиц пришел домой\"\ninput_ids = torch.tensor(tokenizer.encode(text), device=device)\nmodel_output = check_model.generate(\n    input_ids[None, :], max_new_tokens=200, eos_token_id=tokenizer.eos_token_id, do_sample=True, top_k=10\n)\ntokenizer.decode(model_output[0].tolist())","metadata":{"id":"araF_3noK994","trusted":true,"execution":{"iopub.status.busy":"2025-10-27T20:49:31.799667Z","iopub.execute_input":"2025-10-27T20:49:31.800238Z","iopub.status.idle":"2025-10-27T20:49:32.317337Z","shell.execute_reply.started":"2025-10-27T20:49:31.800218Z","shell.execute_reply":"2025-10-27T20:49:32.316813Z"}},"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"'Штирлиц пришел домой. Спросился с работы, а она ему говорит: \"Милый, ты не можешь сказать, что ты мне не можешь\".\\n '"},"metadata":{}}],"execution_count":72},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}