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- {
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {
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- "id": "wsIPzMNfW3QH"
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- },
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- "outputs": [],
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- "source": [
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- "from torchtext.data.utils import get_tokenizer\n",
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- "from torchtext.vocab import build_vocab_from_iterator\n",
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- "from torchtext.datasets import multi30k, Multi30k\n",
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- "from typing import Iterable, List\n",
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- "\n",
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- "\n",
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- "# We need to modify the URLs for the dataset since the links to the original dataset are broken\n",
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- "# Refer to https://github.com/pytorch/text/issues/1756#issuecomment-1163664163 for more info\n",
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- "multi30k.URL[\"train\"] = \"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/training.tar.gz\"\n",
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- "multi30k.URL[\"valid\"] = \"https://raw.githubusercontent.com/neychev/small_DL_repo/master/datasets/Multi30k/validation.tar.gz\"\n",
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- "\n",
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- "SRC_LANGUAGE = 'de'\n",
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- "TGT_LANGUAGE = 'en'\n",
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- "\n",
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- "# Place-holders\n",
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- "token_transform = {}\n",
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- "vocab_transform = {}"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "T8LEEOd2r-PV",
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- "outputId": "33e10bf6-dd1f-4760-ae2a-5fffd2996edb"
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- },
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
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- ]
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- }
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- ],
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- "source": [
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- "from google.colab import drive\n",
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- "drive.mount('/gdrive')"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "mRx_hiQnLGjV",
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- "outputId": "90fe1bbb-76b7-489b-e864-1b41ffbbeeef"
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- },
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Requirement already satisfied: torchdata in /usr/local/lib/python3.10/dist-packages (0.7.1)\n",
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- "Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.0.7)\n",
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- "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (12.1.105)\n",
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- "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (11.0.2.54)\n",
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- "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (10.3.2.106)\n",
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- "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (2.19.3)\n",
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- "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (2.16.3)\n",
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- "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy) (0.1.4)\n",
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- ]
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- }
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- ],
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- "source": [
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- "!pip install -U torchdata\n",
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- "!pip install -U spacy"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "WdqsXpFuzGrH",
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- "outputId": "f5402068-ed10-445e-82a6-9db4d11d310c"
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- },
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Collecting en-core-web-sm==3.7.1\n",
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- " Using cached https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
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- "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.2.2)\n",
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- "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4)\n",
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- "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
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- "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)\n",
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- "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)\n",
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- "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
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- "You can now load the package via spacy.load('en_core_web_sm')\n",
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- "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
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- "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
192
- "order to load all the package's dependencies. You can do this by selecting the\n",
193
- "'Restart kernel' or 'Restart runtime' option.\n",
194
- "Collecting de-core-news-sm==3.7.0\n",
195
- " Using cached https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-3.7.0/de_core_news_sm-3.7.0-py3-none-any.whl (14.6 MB)\n",
196
- "Requirement already satisfied: spacy<3.8.0,>=3.7.0 in /usr/local/lib/python3.10/dist-packages (from de-core-news-sm==3.7.0) (3.7.4)\n",
197
- "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.0.12)\n",
198
- "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (1.0.5)\n",
199
- "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (1.0.10)\n",
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- "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.0.8)\n",
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- "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.0.9)\n",
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- "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (8.2.3)\n",
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- "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (1.1.2)\n",
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- "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.4.8)\n",
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- "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.0.10)\n",
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- "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.3.4)\n",
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- "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.9.4)\n",
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- "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (6.4.0)\n",
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- "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (4.66.2)\n",
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- "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.31.0)\n",
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- "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.6.4)\n",
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- "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.1.3)\n",
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- "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (67.7.2)\n",
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- "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (24.0)\n",
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- "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.3.0)\n",
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- "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (1.25.2)\n",
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- "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.6.0)\n",
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- "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.16.3)\n",
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- "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (4.10.0)\n",
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- "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.3.2)\n",
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- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (3.6)\n",
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- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.0.7)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2024.2.2)\n",
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- "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.7.11)\n",
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- "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.1.4)\n",
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- "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (8.1.7)\n",
227
- "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (0.16.0)\n",
228
- "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.0->de-core-news-sm==3.7.0) (2.1.5)\n",
229
- "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
230
- "You can now load the package via spacy.load('de_core_news_sm')\n",
231
- "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
232
- "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
233
- "order to load all the package's dependencies. You can do this by selecting the\n",
234
- "'Restart kernel' or 'Restart runtime' option.\n"
235
- ]
236
- }
237
- ],
238
- "source": [
239
- "!python -m spacy download en_core_web_sm\n",
240
- "!python -m spacy download de_core_news_sm"
241
- ]
242
- },
243
- {
244
- "cell_type": "code",
245
- "execution_count": null,
246
- "metadata": {
247
- "id": "Vmir-6Ppki3_"
248
- },
249
- "outputs": [],
250
- "source": [
251
- "!pip install portalocker>=2.0.0"
252
- ]
253
- },
254
- {
255
- "cell_type": "code",
256
- "execution_count": 92,
257
- "metadata": {
258
- "colab": {
259
- "base_uri": "https://localhost:8080/"
260
- },
261
- "id": "nzh92t5UW9bu",
262
- "outputId": "4db35419-1b6d-413f-89b8-791214a07826"
263
- },
264
- "outputs": [
265
- {
266
- "output_type": "stream",
267
- "name": "stderr",
268
- "text": [
269
- "/usr/local/lib/python3.10/dist-packages/spacy/util.py:1740: UserWarning: [W111] Jupyter notebook detected: if using `prefer_gpu()` or `require_gpu()`, include it in the same cell right before `spacy.load()` to ensure that the model is loaded on the correct device. More information: http://spacy.io/usage/v3#jupyter-notebook-gpu\n",
270
- " warnings.warn(Warnings.W111)\n"
271
- ]
272
- }
273
- ],
274
- "source": [
275
- "token_transform[SRC_LANGUAGE] = get_tokenizer('spacy', language='de_core_news_sm')\n",
276
- "token_transform[TGT_LANGUAGE] = get_tokenizer('spacy', language='en_core_web_sm')\n",
277
- "\n",
278
- "\n",
279
- "# helper function to yield list of tokens\n",
280
- "def yield_tokens(data_iter: Iterable, language: str) -> List[str]:\n",
281
- " language_index = {SRC_LANGUAGE: 0, TGT_LANGUAGE: 1}\n",
282
- "\n",
283
- " for data_sample in data_iter:\n",
284
- " yield token_transform[language](data_sample[language_index[language]])\n",
285
- "\n",
286
- "# Define special symbols and indices\n",
287
- "UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3\n",
288
- "# Make sure the tokens are in order of their indices to properly insert them in vocab\n",
289
- "special_symbols = ['<unk>', '<pad>', '<bos>', '<eos>']\n",
290
- "\n",
291
- "for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:\n",
292
- " # Training data Iterator\n",
293
- " train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))\n",
294
- " # Create torchtext's Vocab object\n",
295
- " vocab_transform[ln] = build_vocab_from_iterator(yield_tokens(train_iter, ln),\n",
296
- " min_freq=1,\n",
297
- " specials=special_symbols,\n",
298
- " special_first=True)\n",
299
- "\n",
300
- "# Set ``UNK_IDX`` as the default index. This index is returned when the token is not found.\n",
301
- "# If not set, it throws ``RuntimeError`` when the queried token is not found in the Vocabulary.\n",
302
- "for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:\n",
303
- " vocab_transform[ln].set_default_index(UNK_IDX)"
304
- ]
305
- },
306
- {
307
- "cell_type": "code",
308
- "execution_count": 93,
309
- "metadata": {
310
- "id": "OB_yiHCaXKv8"
311
- },
312
- "outputs": [],
313
- "source": [
314
- "from torch import Tensor\n",
315
- "import torch\n",
316
- "import torch.nn as nn\n",
317
- "from torch.nn import Transformer\n",
318
- "import math\n",
319
- "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
320
- "\n",
321
- "# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.\n",
322
- "class PositionalEncoding(nn.Module):\n",
323
- " def __init__(self,\n",
324
- " emb_size: int,\n",
325
- " dropout: float,\n",
326
- " maxlen: int = 5000):\n",
327
- " super(PositionalEncoding, self).__init__()\n",
328
- " den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)\n",
329
- " pos = torch.arange(0, maxlen).reshape(maxlen, 1)\n",
330
- " pos_embedding = torch.zeros((maxlen, emb_size))\n",
331
- " pos_embedding[:, 0::2] = torch.sin(pos * den)\n",
332
- " pos_embedding[:, 1::2] = torch.cos(pos * den)\n",
333
- " pos_embedding = pos_embedding.unsqueeze(-2)\n",
334
- "\n",
335
- " self.dropout = nn.Dropout(dropout)\n",
336
- " self.register_buffer('pos_embedding', pos_embedding)\n",
337
- "\n",
338
- " def forward(self, token_embedding: Tensor):\n",
339
- " return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])\n",
340
- "\n",
341
- "# helper Module to convert tensor of input indices into corresponding tensor of token embeddings\n",
342
- "class TokenEmbedding(nn.Module):\n",
343
- " def __init__(self, vocab_size: int, emb_size):\n",
344
- " super(TokenEmbedding, self).__init__()\n",
345
- " self.embedding = nn.Embedding(vocab_size, emb_size)\n",
346
- " self.emb_size = emb_size\n",
347
- "\n",
348
- " def forward(self, tokens: Tensor):\n",
349
- " return self.embedding(tokens.long()) * math.sqrt(self.emb_size)\n",
350
- "\n",
351
- "# Seq2Seq Network\n",
352
- "class Seq2SeqTransformer(nn.Module):\n",
353
- " def __init__(self,\n",
354
- " num_encoder_layers: int,\n",
355
- " num_decoder_layers: int,\n",
356
- " emb_size: int,\n",
357
- " nhead: int,\n",
358
- " src_vocab_size: int,\n",
359
- " tgt_vocab_size: int,\n",
360
- " dim_feedforward: int = 512,\n",
361
- " dropout: float = 0.1):\n",
362
- " super(Seq2SeqTransformer, self).__init__()\n",
363
- " self.transformer = Transformer(d_model=emb_size,\n",
364
- " nhead=nhead,\n",
365
- " num_encoder_layers=num_encoder_layers,\n",
366
- " num_decoder_layers=num_decoder_layers,\n",
367
- " dim_feedforward=dim_feedforward,\n",
368
- " dropout=dropout)\n",
369
- " self.generator = nn.Linear(emb_size, tgt_vocab_size)\n",
370
- " self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)\n",
371
- " self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)\n",
372
- " self.positional_encoding = PositionalEncoding(\n",
373
- " emb_size, dropout=dropout)\n",
374
- "\n",
375
- " def forward(self,\n",
376
- " src: Tensor,\n",
377
- " trg: Tensor,\n",
378
- " src_mask: Tensor,\n",
379
- " tgt_mask: Tensor,\n",
380
- " src_padding_mask: Tensor,\n",
381
- " tgt_padding_mask: Tensor,\n",
382
- " memory_key_padding_mask: Tensor):\n",
383
- " src_emb = self.positional_encoding(self.src_tok_emb(src))\n",
384
- " tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))\n",
385
- " outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,\n",
386
- " src_padding_mask, tgt_padding_mask, memory_key_padding_mask)\n",
387
- " return self.generator(outs)\n",
388
- "\n",
389
- " def encode(self, src: Tensor, src_mask: Tensor):\n",
390
- " return self.transformer.encoder(self.positional_encoding(\n",
391
- " self.src_tok_emb(src)), src_mask)\n",
392
- "\n",
393
- " def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):\n",
394
- " return self.transformer.decoder(self.positional_encoding(\n",
395
- " self.tgt_tok_emb(tgt)), memory,\n",
396
- " tgt_mask)"
397
- ]
398
- },
399
- {
400
- "cell_type": "code",
401
- "execution_count": 94,
402
- "metadata": {
403
- "id": "ECpJWZp2r_xa"
404
- },
405
- "outputs": [],
406
- "source": [
407
- "from torch import Tensor\n",
408
- "import torch\n",
409
- "import torch.nn as nn\n",
410
- "from torch.nn import Transformer\n",
411
- "import math\n",
412
- "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
413
- "\n",
414
- "# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.\n",
415
- "class PositionalEncoding(nn.Module):\n",
416
- " def __init__(self,\n",
417
- " emb_size: int,\n",
418
- " dropout: float,\n",
419
- " maxlen: int = 5000):\n",
420
- " super(PositionalEncoding, self).__init__()\n",
421
- " den = torch.exp(- torch.arange(0, emb_size, 2)* math.log(10000) / emb_size)\n",
422
- " pos = torch.arange(0, maxlen).reshape(maxlen, 1)\n",
423
- " pos_embedding = torch.zeros((maxlen, emb_size))\n",
424
- " pos_embedding[:, 0::2] = torch.sin(pos * den)\n",
425
- " pos_embedding[:, 1::2] = torch.cos(pos * den)\n",
426
- " pos_embedding = pos_embedding.unsqueeze(-2)\n",
427
- "\n",
428
- " self.dropout = nn.Dropout(dropout)\n",
429
- " self.register_buffer('pos_embedding', pos_embedding)\n",
430
- "\n",
431
- " def forward(self, token_embedding: Tensor):\n",
432
- " return self.dropout(token_embedding + self.pos_embedding[:token_embedding.size(0), :])\n",
433
- "\n",
434
- "# helper Module to convert tensor of input indices into corresponding tensor of token embeddings\n",
435
- "class TokenEmbedding(nn.Module):\n",
436
- " def __init__(self, vocab_size: int, emb_size):\n",
437
- " super(TokenEmbedding, self).__init__()\n",
438
- " self.embedding = nn.Embedding(vocab_size, emb_size)\n",
439
- " self.emb_size = emb_size\n",
440
- "\n",
441
- " def forward(self, tokens: Tensor):\n",
442
- " return self.embedding(tokens.long()) * math.sqrt(self.emb_size)\n",
443
- "\n",
444
- "# Seq2Seq Network\n",
445
- "class Seq2SeqTransformer(nn.Module):\n",
446
- " def __init__(self,\n",
447
- " num_encoder_layers: int,\n",
448
- " num_decoder_layers: int,\n",
449
- " emb_size: int,\n",
450
- " nhead: int,\n",
451
- " src_vocab_size: int,\n",
452
- " tgt_vocab_size: int,\n",
453
- " dim_feedforward: int = 512,\n",
454
- " dropout: float = 0.1):\n",
455
- " super(Seq2SeqTransformer, self).__init__()\n",
456
- " self.transformer = Transformer(d_model=emb_size,\n",
457
- " nhead=nhead,\n",
458
- " num_encoder_layers=num_encoder_layers,\n",
459
- " num_decoder_layers=num_decoder_layers,\n",
460
- " dim_feedforward=dim_feedforward,\n",
461
- " dropout=dropout)\n",
462
- " self.generator = nn.Linear(emb_size, tgt_vocab_size)\n",
463
- " self.src_tok_emb = TokenEmbedding(src_vocab_size, emb_size)\n",
464
- " self.tgt_tok_emb = TokenEmbedding(tgt_vocab_size, emb_size)\n",
465
- " self.positional_encoding = PositionalEncoding(\n",
466
- " emb_size, dropout=dropout)\n",
467
- "\n",
468
- " def forward(self,\n",
469
- " src: Tensor,\n",
470
- " trg: Tensor,\n",
471
- " src_mask: Tensor,\n",
472
- " tgt_mask: Tensor,\n",
473
- " src_padding_mask: Tensor,\n",
474
- " tgt_padding_mask: Tensor,\n",
475
- " memory_key_padding_mask: Tensor):\n",
476
- " src_emb = self.positional_encoding(self.src_tok_emb(src))\n",
477
- " tgt_emb = self.positional_encoding(self.tgt_tok_emb(trg))\n",
478
- " outs = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask, None,\n",
479
- " src_padding_mask, tgt_padding_mask, memory_key_padding_mask)\n",
480
- " return self.generator(outs)\n",
481
- "\n",
482
- " def encode(self, src: Tensor, src_mask: Tensor):\n",
483
- " return self.transformer.encoder(self.positional_encoding(\n",
484
- " self.src_tok_emb(src)), src_mask)\n",
485
- "\n",
486
- " def decode(self, tgt: Tensor, memory: Tensor, tgt_mask: Tensor):\n",
487
- " return self.transformer.decoder(self.positional_encoding(\n",
488
- " self.tgt_tok_emb(tgt)), memory,\n",
489
- " tgt_mask)"
490
- ]
491
- },
492
- {
493
- "cell_type": "code",
494
- "execution_count": 95,
495
- "metadata": {
496
- "id": "PUIS0MWUZCKc"
497
- },
498
- "outputs": [],
499
- "source": [
500
- "def generate_square_subsequent_mask(sz):\n",
501
- " mask = (torch.triu(torch.ones((sz, sz), device=DEVICE)) == 1).transpose(0, 1)\n",
502
- " mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))\n",
503
- " return mask\n",
504
- "\n",
505
- "\n",
506
- "def create_mask(src, tgt):\n",
507
- " src_seq_len = src.shape[0]\n",
508
- " tgt_seq_len = tgt.shape[0]\n",
509
- "\n",
510
- " tgt_mask = generate_square_subsequent_mask(tgt_seq_len)\n",
511
- " src_mask = torch.zeros((src_seq_len, src_seq_len),device=DEVICE).type(torch.bool)\n",
512
- "\n",
513
- " src_padding_mask = (src == PAD_IDX).transpose(0, 1)\n",
514
- " tgt_padding_mask = (tgt == PAD_IDX).transpose(0, 1)\n",
515
- " return src_mask, tgt_mask, src_padding_mask, tgt_padding_mask"
516
- ]
517
- },
518
- {
519
- "cell_type": "code",
520
- "execution_count": 96,
521
- "metadata": {
522
- "colab": {
523
- "base_uri": "https://localhost:8080/"
524
- },
525
- "id": "DA3eAj9GZFus",
526
- "outputId": "8132fcb6-84c1-44c9-a150-616467d36052"
527
- },
528
- "outputs": [
529
- {
530
- "output_type": "stream",
531
- "name": "stderr",
532
- "text": [
533
- "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
534
- " warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
535
- ]
536
- }
537
- ],
538
- "source": [
539
- "torch.manual_seed(0)\n",
540
- "\n",
541
- "SRC_VOCAB_SIZE = len(vocab_transform[SRC_LANGUAGE])\n",
542
- "TGT_VOCAB_SIZE = len(vocab_transform[TGT_LANGUAGE])\n",
543
- "EMB_SIZE = 512\n",
544
- "NHEAD = 8\n",
545
- "FFN_HID_DIM = 512\n",
546
- "BATCH_SIZE = 128\n",
547
- "NUM_ENCODER_LAYERS = 3\n",
548
- "NUM_DECODER_LAYERS = 3\n",
549
- "\n",
550
- "transformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,\n",
551
- " NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)\n",
552
- "\n",
553
- "for p in transformer.parameters():\n",
554
- " if p.dim() > 1:\n",
555
- " nn.init.xavier_uniform_(p)\n",
556
- "\n",
557
- "transformer = transformer.to(DEVICE)\n",
558
- "\n",
559
- "loss_fn = torch.nn.CrossEntropyLoss(ignore_index=PAD_IDX)\n",
560
- "\n",
561
- "optimizer = torch.optim.Adam(transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9)"
562
- ]
563
- },
564
- {
565
- "cell_type": "code",
566
- "execution_count": 97,
567
- "metadata": {
568
- "id": "IO9Y95SnZKys"
569
- },
570
- "outputs": [],
571
- "source": [
572
- "from torch.nn.utils.rnn import pad_sequence\n",
573
- "\n",
574
- "# helper function to club together sequential operations\n",
575
- "def sequential_transforms(*transforms):\n",
576
- " def func(txt_input):\n",
577
- " for transform in transforms:\n",
578
- " txt_input = transform(txt_input)\n",
579
- " return txt_input\n",
580
- " return func\n",
581
- "\n",
582
- "# function to add BOS/EOS and create tensor for input sequence indices\n",
583
- "def tensor_transform(token_ids: List[int]):\n",
584
- " return torch.cat((torch.tensor([BOS_IDX]),\n",
585
- " torch.tensor(token_ids),\n",
586
- " torch.tensor([EOS_IDX])))\n",
587
- "\n",
588
- "# ``src`` and ``tgt`` language text transforms to convert raw strings into tensors indices\n",
589
- "text_transform = {}\n",
590
- "for ln in [SRC_LANGUAGE, TGT_LANGUAGE]:\n",
591
- " text_transform[ln] = sequential_transforms(token_transform[ln], #Tokenization\n",
592
- " vocab_transform[ln], #Numericalization\n",
593
- " tensor_transform) # Add BOS/EOS and create tensor\n",
594
- "\n",
595
- "\n",
596
- "# function to collate data samples into batch tensors\n",
597
- "def collate_fn(batch):\n",
598
- " src_batch, tgt_batch = [], []\n",
599
- " for src_sample, tgt_sample in batch:\n",
600
- " src_batch.append(text_transform[SRC_LANGUAGE](src_sample.rstrip(\"\\n\")))\n",
601
- " tgt_batch.append(text_transform[TGT_LANGUAGE](tgt_sample.rstrip(\"\\n\")))\n",
602
- "\n",
603
- " src_batch = pad_sequence(src_batch, padding_value=PAD_IDX)\n",
604
- " tgt_batch = pad_sequence(tgt_batch, padding_value=PAD_IDX)\n",
605
- " return src_batch, tgt_batch"
606
- ]
607
- },
608
- {
609
- "cell_type": "code",
610
- "execution_count": 98,
611
- "metadata": {
612
- "id": "qw9lO5xvZSjb"
613
- },
614
- "outputs": [],
615
- "source": [
616
- "from torch.utils.data import DataLoader\n",
617
- "\n",
618
- "def train_epoch(model, optimizer):\n",
619
- " model.train()\n",
620
- " losses = 0\n",
621
- " train_iter = Multi30k(split='train', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))\n",
622
- " train_dataloader = DataLoader(train_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)\n",
623
- "\n",
624
- " for src, tgt in train_dataloader:\n",
625
- " src = src.to(DEVICE)\n",
626
- " tgt = tgt.to(DEVICE)\n",
627
- "\n",
628
- " tgt_input = tgt[:-1, :]\n",
629
- "\n",
630
- " src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)\n",
631
- "\n",
632
- " logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)\n",
633
- "\n",
634
- " optimizer.zero_grad()\n",
635
- "\n",
636
- " tgt_out = tgt[1:, :]\n",
637
- " loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))\n",
638
- " loss.backward()\n",
639
- "\n",
640
- " optimizer.step()\n",
641
- " losses += loss.item()\n",
642
- "\n",
643
- " return losses / len(list(train_dataloader))"
644
- ]
645
- },
646
- {
647
- "cell_type": "code",
648
- "execution_count": 99,
649
- "metadata": {
650
- "id": "frdDbhZ_ZZ9d"
651
- },
652
- "outputs": [],
653
- "source": [
654
- "def evaluate(model):\n",
655
- " model.eval()\n",
656
- " losses = 0\n",
657
- "\n",
658
- " val_iter = Multi30k(split='valid', language_pair=(SRC_LANGUAGE, TGT_LANGUAGE))\n",
659
- " val_dataloader = DataLoader(val_iter, batch_size=BATCH_SIZE, collate_fn=collate_fn)\n",
660
- "\n",
661
- " for src, tgt in val_dataloader:\n",
662
- " src = src.to(DEVICE)\n",
663
- " tgt = tgt.to(DEVICE)\n",
664
- "\n",
665
- " tgt_input = tgt[:-1, :]\n",
666
- "\n",
667
- " src_mask, tgt_mask, src_padding_mask, tgt_padding_mask = create_mask(src, tgt_input)\n",
668
- "\n",
669
- " logits = model(src, tgt_input, src_mask, tgt_mask,src_padding_mask, tgt_padding_mask, src_padding_mask)\n",
670
- "\n",
671
- " tgt_out = tgt[1:, :]\n",
672
- " loss = loss_fn(logits.reshape(-1, logits.shape[-1]), tgt_out.reshape(-1))\n",
673
- " losses += loss.item()\n",
674
- "\n",
675
- " return losses / len(list(val_dataloader))"
676
- ]
677
- },
678
- {
679
- "cell_type": "code",
680
- "execution_count": null,
681
- "metadata": {
682
- "colab": {
683
- "base_uri": "https://localhost:8080/"
684
- },
685
- "id": "xjLl776lZfJc",
686
- "outputId": "6f0965d6-6e53-40b7-fe19-69096b68c3f8"
687
- },
688
- "outputs": [
689
- {
690
- "metadata": {
691
- "tags": null
692
- },
693
- "name": "stderr",
694
- "output_type": "stream",
695
- "text": [
696
- "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py:5109: UserWarning: Support for mismatched key_padding_mask and attn_mask is deprecated. Use same type for both instead.\n",
697
- " warnings.warn(\n",
698
- "/usr/local/lib/python3.10/dist-packages/torch/utils/data/datapipes/iter/combining.py:337: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.\n",
699
- " warnings.warn(\"Some child DataPipes are not exhausted when __iter__ is called. We are resetting \"\n"
700
- ]
701
- },
702
- {
703
- "output_type": "stream",
704
- "name": "stdout",
705
- "text": [
706
- "Epoch: 1, Train loss: 5.344, Val loss: 4.106, Epoch time = 43.253s\n",
707
- "Epoch: 2, Train loss: 3.761, Val loss: 3.309, Epoch time = 43.216s\n",
708
- "Epoch: 3, Train loss: 3.157, Val loss: 2.887, Epoch time = 43.028s\n",
709
- "Epoch: 4, Train loss: 2.767, Val loss: 2.640, Epoch time = 43.509s\n",
710
- "Epoch: 5, Train loss: 2.477, Val loss: 2.442, Epoch time = 44.192s\n",
711
- "Epoch: 6, Train loss: 2.247, Val loss: 2.306, Epoch time = 44.518s\n",
712
- "Epoch: 7, Train loss: 2.055, Val loss: 2.207, Epoch time = 43.989s\n"
713
- ]
714
- }
715
- ],
716
- "source": [
717
- "from timeit import default_timer as timer\n",
718
- "NUM_EPOCHS = 10\n",
719
- "\n",
720
- "for epoch in range(1, NUM_EPOCHS+1):\n",
721
- " start_time = timer()\n",
722
- " train_loss = train_epoch(transformer, optimizer)\n",
723
- " end_time = timer()\n",
724
- " val_loss = evaluate(transformer)\n",
725
- " print((f\"Epoch: {epoch}, Train loss: {train_loss:.3f}, Val loss: {val_loss:.3f}, \"f\"Epoch time = {(end_time - start_time):.3f}s\"))\n"
726
- ]
727
- },
728
- {
729
- "cell_type": "code",
730
- "execution_count": 20,
731
- "metadata": {
732
- "id": "ebEhLx-3slOE"
733
- },
734
- "outputs": [],
735
- "source": [
736
- "torch.save(transformer.state_dict(), '/gdrive/My Drive/transformer_model.pth')"
737
- ]
738
- },
739
- {
740
- "cell_type": "code",
741
- "execution_count": 58,
742
- "metadata": {
743
- "id": "OW8D2ALUtBQq"
744
- },
745
- "outputs": [],
746
- "source": [
747
- "def greedy_decode(model, src, src_mask, max_len, start_symbol):\n",
748
- " src = src.to(DEVICE)\n",
749
- " src_mask = src_mask.to(DEVICE)\n",
750
- "\n",
751
- " memory = model.encode(src, src_mask)\n",
752
- " ys = torch.ones(1, 1).fill_(start_symbol).type(torch.long).to(DEVICE)\n",
753
- " for i in range(max_len-1):\n",
754
- " memory = memory.to(DEVICE)\n",
755
- " tgt_mask = (generate_square_subsequent_mask(ys.size(0))\n",
756
- " .type(torch.bool)).to(DEVICE)\n",
757
- " out = model.decode(ys, memory, tgt_mask)\n",
758
- " out = out.transpose(0, 1)\n",
759
- " prob = model.generator(out[:, -1])\n",
760
- " _, next_word = torch.max(prob, dim=1)\n",
761
- " next_word = next_word.item()\n",
762
- "\n",
763
- " ys = torch.cat([ys,\n",
764
- " torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=0)\n",
765
- " if next_word == EOS_IDX:\n",
766
- " break\n",
767
- " return ys"
768
- ]
769
- },
770
- {
771
- "cell_type": "code",
772
- "execution_count": 59,
773
- "metadata": {
774
- "id": "exM3fCaBtFk2",
775
- "colab": {
776
- "base_uri": "https://localhost:8080/"
777
- },
778
- "outputId": "726a1bab-c145-4861-f4d3-6cb5122a567c"
779
- },
780
- "outputs": [
781
- {
782
- "output_type": "stream",
783
- "name": "stdout",
784
- "text": [
785
- "3\n",
786
- "3\n",
787
- "512\n",
788
- "8\n",
789
- "19214\n",
790
- "10837\n",
791
- "512\n"
792
- ]
793
- }
794
- ],
795
- "source": [
796
- "# Load the saved model\n",
797
- "loaded_model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,\n",
798
- " NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)\n",
799
- "print(NUM_ENCODER_LAYERS)\n",
800
- "print(NUM_DECODER_LAYERS)\n",
801
- "print(EMB_SIZE)\n",
802
- "print(NHEAD)\n",
803
- "print(SRC_VOCAB_SIZE)\n",
804
- "print(TGT_VOCAB_SIZE)\n",
805
- "print(FFN_HID_DIM)\n",
806
- "loaded_model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth'))\n",
807
- "loaded_model.eval() # Make sure to set the model in evaluation mode\n",
808
- "\n",
809
- "# Incorporate the loaded model into the remaining portion of your code\n",
810
- "def translate(model: torch.nn.Module, src_sentence: str):\n",
811
- " model.eval()\n",
812
- " src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)\n",
813
- " num_tokens = src.shape[0]\n",
814
- " src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)\n",
815
- " tgt_tokens = greedy_decode(\n",
816
- " model, src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()\n",
817
- " return \" \".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace(\"<bos>\", \"\").replace(\"<eos>\", \"\")\n"
818
- ]
819
- },
820
- {
821
- "cell_type": "code",
822
- "execution_count": 60,
823
- "metadata": {
824
- "id": "85yPR0zBtOsZ",
825
- "colab": {
826
- "base_uri": "https://localhost:8080/"
827
- },
828
- "outputId": "44efc93c-5d86-4084-fc21-bb3bc5bae207"
829
- },
830
- "outputs": [
831
- {
832
- "output_type": "stream",
833
- "name": "stdout",
834
- "text": [
835
- " Russia cloth spoof Russia sewing Madrid Madrid Russia silhouetted Madrid Russia Madrid Madrid Russia cloth\n"
836
- ]
837
- }
838
- ],
839
- "source": [
840
- "print(translate(transformer, \"Eine Gruppe von Menschen steht vor einem Iglu .\"))"
841
- ]
842
- },
843
- {
844
- "cell_type": "code",
845
- "execution_count": 24,
846
- "metadata": {
847
- "id": "HJF7lXj0tPjO",
848
- "colab": {
849
- "base_uri": "https://localhost:8080/"
850
- },
851
- "outputId": "0237b57f-29cf-4c75-a060-fb928dbd2ced"
852
- },
853
- "outputs": [
854
- {
855
- "output_type": "stream",
856
- "name": "stdout",
857
- "text": [
858
- "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.2)\n",
859
- "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.3)\n",
860
- "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
861
- "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
862
- "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
863
- "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
864
- "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
865
- "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
866
- "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.2)\n",
867
- "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.2)\n",
868
- "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n",
869
- "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
870
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.10.0)\n",
871
- "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
872
- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
873
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
874
- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
875
- ]
876
- }
877
- ],
878
- "source": [
879
- "!pip install transformers"
880
- ]
881
- },
882
- {
883
- "cell_type": "code",
884
- "execution_count": 25,
885
- "metadata": {
886
- "id": "TMLnV5aMtSco"
887
- },
888
- "outputs": [],
889
- "source": [
890
- "from transformers.modeling_utils import PreTrainedModel ,PretrainedConfig"
891
- ]
892
- },
893
- {
894
- "cell_type": "code",
895
- "execution_count": 27,
896
- "metadata": {
897
- "id": "oP9ODxPxtWPI"
898
- },
899
- "outputs": [],
900
- "source": [
901
- "class Seq2SeqTransformer(PreTrainedModel):\n",
902
- " def __init__(self,config):\n",
903
- " super(Seq2SeqTransformer, self).__init__(config)\n",
904
- " self.transformer = Transformer(d_model=config.emb_size,\n",
905
- " nhead=config.nhead,\n",
906
- " num_encoder_layers=config.num_encoder_layers,\n",
907
- " num_decoder_layers=config.num_decoder_layers,\n",
908
- " dim_feedforward=config.dim_feedforward,\n",
909
- " dropout=config.dropout)\n",
910
- " self.generator = nn.Linear(config.emb_size, config.tgt_vocab_size)\n",
911
- " self.src_tok_emb = TokenEmbedding(config.src_vocab_size, config.emb_size)\n",
912
- " self.tgt_tok_emb = TokenEmbedding(config.tgt_vocab_size, config.emb_size)\n",
913
- " self.positional_encoding = PositionalEncoding(\n",
914
- " config.emb_size, dropout=config.dropout)"
915
- ]
916
- },
917
- {
918
- "cell_type": "code",
919
- "execution_count": 30,
920
- "metadata": {
921
- "id": "_uOmJ7oQtdVF"
922
- },
923
- "outputs": [],
924
- "source": [
925
- "config = PretrainedConfig(\n",
926
- " # Specify your vocabulary size\n",
927
- " dim_feedforward =512,\n",
928
- " dropout= 0.1,\n",
929
- " emb_size= 512,\n",
930
- " num_decoder_layers= 3,\n",
931
- " num_encoder_layers= 3,\n",
932
- " nhead= 8,\n",
933
- " src_vocab_size= 19214,\n",
934
- " tgt_vocab_size= 10837\n",
935
- ")"
936
- ]
937
- },
938
- {
939
- "cell_type": "code",
940
- "execution_count": 33,
941
- "metadata": {
942
- "id": "DO15AHGZtjwA"
943
- },
944
- "outputs": [],
945
- "source": [
946
- "model = Seq2SeqTransformer(config)\n",
947
- "model.to(DEVICE)\n",
948
- "\n",
949
- "\n",
950
- "model.save_pretrained('/gdrive/My Drive')"
951
- ]
952
- },
953
- {
954
- "cell_type": "code",
955
- "source": [
956
- "!pip install -q gradio==3.48.0"
957
- ],
958
- "metadata": {
959
- "colab": {
960
- "base_uri": "https://localhost:8080/"
961
- },
962
- "id": "vJicfSC62R86",
963
- "outputId": "ddb7f709-daff-4376-e15a-d936397e8ec3"
964
- },
965
- "execution_count": 35,
966
- "outputs": [
967
- {
968
- "output_type": "stream",
969
- "name": "stdout",
970
- "text": [
971
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.3/20.3 MB\u001b[0m \u001b[31m57.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
972
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973
- "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
974
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m299.2/299.2 kB\u001b[0m \u001b[31m34.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
980
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
981
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
982
- "\u001b[?25h Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
983
- ]
984
- }
985
- ]
986
- },
987
- {
988
- "cell_type": "code",
989
- "source": [
990
- "import gradio as gr\n",
991
- "import torch\n",
992
- "from torchtext.data.utils import get_tokenizer\n",
993
- "from torchtext.vocab import build_vocab_from_iterator\n",
994
- "from torchtext.datasets import Multi30k\n",
995
- "from torch import Tensor\n",
996
- "from typing import Iterable, List\n",
997
- "\n",
998
- "# Define your model, tokenizer, and other necessary components here\n",
999
- "# Ensure you have imported all necessary libraries\n",
1000
- "\n",
1001
- "# Load your transformer model\n",
1002
- "model = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, EMB_SIZE,\n",
1003
- " NHEAD, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE, FFN_HID_DIM)\n",
1004
- "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
1005
- "model.load_state_dict(torch.load('/gdrive/My Drive/transformer_model.pth', map_location=device))\n",
1006
- "model.eval()\n",
1007
- "\n",
1008
- "\n",
1009
- "def translate(model: torch.nn.Module, src_sentence: str):\n",
1010
- " model.eval()\n",
1011
- " src = text_transform[SRC_LANGUAGE](src_sentence).view(-1, 1)\n",
1012
- " num_tokens = src.shape[0]\n",
1013
- " src_mask = (torch.zeros(num_tokens, num_tokens)).type(torch.bool)\n",
1014
- " tgt_tokens = greedy_decode(\n",
1015
- " model, src, src_mask, max_len=num_tokens + 5, start_symbol=BOS_IDX).flatten()\n",
1016
- " return \" \".join(vocab_transform[TGT_LANGUAGE].lookup_tokens(list(tgt_tokens.cpu().numpy()))).replace(\"<bos>\", \"\").replace(\"<eos>\", \"\")\n",
1017
- "\n",
1018
- "\n"
1019
- ],
1020
- "metadata": {
1021
- "colab": {
1022
- "base_uri": "https://localhost:8080/"
1023
- },
1024
- "id": "wgBhx0w7-EUa",
1025
- "outputId": "170f3d83-5c56-4cc6-da52-273b8f63e885"
1026
- },
1027
- "execution_count": 90,
1028
- "outputs": [
1029
- {
1030
- "output_type": "stream",
1031
- "name": "stderr",
1032
- "text": [
1033
- "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:286: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n",
1034
- " warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n"
1035
- ]
1036
- }
1037
- ]
1038
- },
1039
- {
1040
- "cell_type": "code",
1041
- "source": [
1042
- "if __name__ == \"__main__\":\n",
1043
- " # Create the Gradio interface\n",
1044
- " iface = gr.Interface(\n",
1045
- " fn=translate, # Specify the translation function as the main function\n",
1046
- " inputs=[\n",
1047
- " gr.inputs.Textbox(label=\"Text\"),\n",
1048
- " gr.inputs.Textbox(label=\"Text\")\n",
1049
- "\n",
1050
- " ],\n",
1051
- " outputs=[\"text\"], # Define the output type as text\n",
1052
- " #examples=[[\"I'm ready\", \"english\", \"arabic\"]], # Provide an example input for demonstration\n",
1053
- " cache_examples=False, # Disable caching of examples\n",
1054
- " title=\"germanToenglish\", # Set the title of the interface\n",
1055
- " #description=\"This is a translator app for arabic and english. Currently supports only english to arabic.\" # Add a description of the interface\n",
1056
- " )\n",
1057
- "\n",
1058
- " # Launch the interface\n",
1059
- " iface.launch(share=True)"
1060
- ],
1061
- "metadata": {
1062
- "colab": {
1063
- "base_uri": "https://localhost:8080/",
1064
- "height": 819
1065
- },
1066
- "id": "y9CN022m-hGQ",
1067
- "outputId": "34971409-3c9d-46a8-a741-e785d597d18c"
1068
- },
1069
- "execution_count": 91,
1070
- "outputs": [
1071
- {
1072
- "output_type": "stream",
1073
- "name": "stderr",
1074
- "text": [
1075
- "<ipython-input-91-b142228ac367>:6: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
1076
- " gr.inputs.Textbox(label=\"Text\"),\n",
1077
- "<ipython-input-91-b142228ac367>:6: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
1078
- " gr.inputs.Textbox(label=\"Text\"),\n",
1079
- "<ipython-input-91-b142228ac367>:6: GradioDeprecationWarning: `numeric` parameter is deprecated, and it has no effect\n",
1080
- " gr.inputs.Textbox(label=\"Text\"),\n",
1081
- "<ipython-input-91-b142228ac367>:7: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
1082
- " gr.inputs.Textbox(label=\"Text\")\n",
1083
- "<ipython-input-91-b142228ac367>:7: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
1084
- " gr.inputs.Textbox(label=\"Text\")\n",
1085
- "<ipython-input-91-b142228ac367>:7: GradioDeprecationWarning: `numeric` parameter is deprecated, and it has no effect\n",
1086
- " gr.inputs.Textbox(label=\"Text\")\n"
1087
- ]
1088
- },
1089
- {
1090
- "output_type": "stream",
1091
- "name": "stdout",
1092
- "text": [
1093
- "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
1094
- "Running on public URL: https://05da874e546ecf0271.gradio.live\n",
1095
- "\n",
1096
- "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
1097
- ]
1098
- },
1099
- {
1100
- "output_type": "display_data",
1101
- "data": {
1102
- "text/plain": [
1103
- "<IPython.core.display.HTML object>"
1104
- ],
1105
- "text/html": [
1106
- "<div><iframe src=\"https://05da874e546ecf0271.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
1107
- ]
1108
- },
1109
- "metadata": {}
1110
- }
1111
- ]
1112
- },
1113
- {
1114
- "cell_type": "code",
1115
- "source": [
1116
- "if __name__ == \"__main__\":\n",
1117
- " # Create the Gradio interface\n",
1118
- " iface = gr.Interface(\n",
1119
- " fn=translate, # Specify the translation function as the main function\n",
1120
- " inputs=[\n",
1121
- " gr.components.Textbox(label=\"Text\"), # Add a textbox input for entering text\n",
1122
- " gr.components.Dropdown(label=\"Source Language\", choices=language), # Add a dropdown for selecting source language\n",
1123
- " gr.components.Dropdown(label=\"Target Language\", choices=language), # Add a dropdown for selecting target language\n",
1124
- " ],\n",
1125
- " outputs=[\"text\"], # Define the output type as text\n",
1126
- " #examples=[[\"I'm ready\", \"english\", \"arabic\"]], # Provide an example input for demonstration\n",
1127
- " cache_examples=False, # Disable caching of examples\n",
1128
- " title=\"germanToenglish\", # Set the title of the interface\n",
1129
- " #description=\"This is a translator app for arabic and english. Currently supports only english to arabic.\" # Add a description of the interface\n",
1130
- " )\n",
1131
- "\n",
1132
- " # Launch the interface\n",
1133
- " iface.launch(share=True)"
1134
- ],
1135
- "metadata": {
1136
- "colab": {
1137
- "base_uri": "https://localhost:8080/",
1138
- "height": 680
1139
- },
1140
- "id": "NRTdTJ8E72LQ",
1141
- "outputId": "6d76e9c7-8f46-498b-e0a6-b6aa74b48fc6"
1142
- },
1143
- "execution_count": 45,
1144
- "outputs": [
1145
- {
1146
- "output_type": "stream",
1147
- "name": "stderr",
1148
- "text": [
1149
- "/usr/local/lib/python3.10/dist-packages/gradio/utils.py:812: UserWarning: Expected 2 arguments for function <function translate at 0x7d1bb879fc70>, received 3.\n",
1150
- " warnings.warn(\n",
1151
- "/usr/local/lib/python3.10/dist-packages/gradio/utils.py:820: UserWarning: Expected maximum 2 arguments for function <function translate at 0x7d1bb879fc70>, received 3.\n",
1152
- " warnings.warn(\n"
1153
- ]
1154
- },
1155
- {
1156
- "output_type": "stream",
1157
- "name": "stdout",
1158
- "text": [
1159
- "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
1160
- "Running on public URL: https://652be12920500f856f.gradio.live\n",
1161
- "\n",
1162
- "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
1163
- ]
1164
- },
1165
- {
1166
- "output_type": "display_data",
1167
- "data": {
1168
- "text/plain": [
1169
- "<IPython.core.display.HTML object>"
1170
- ],
1171
- "text/html": [
1172
- "<div><iframe src=\"https://652be12920500f856f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
1173
- ]
1174
- },
1175
- "metadata": {}
1176
- }
1177
- ]
1178
- },
1179
- {
1180
- "cell_type": "code",
1181
- "source": [],
1182
- "metadata": {
1183
- "id": "5RuYPqUT3M3M"
1184
- },
1185
- "execution_count": null,
1186
- "outputs": []
1187
- }
1188
- ],
1189
- "metadata": {
1190
- "accelerator": "GPU",
1191
- "colab": {
1192
- "gpuType": "T4",
1193
- "provenance": []
1194
- },
1195
- "kernelspec": {
1196
- "display_name": "Python 3",
1197
- "name": "python3"
1198
- },
1199
- "language_info": {
1200
- "name": "python"
1201
- }
1202
- },
1203
- "nbformat": 4,
1204
- "nbformat_minor": 0
1205
- }