From 973b85e136e37573a66eef1195316b6dceb41603 Mon Sep 17 00:00:00 2001 From: MKhalusova Date: Mon, 19 Feb 2024 08:33:19 -0500 Subject: [PATCH] authors added --- .../en/faiss_with_hf_datasets_and_clip.ipynb | 1104 +++++---- .../fine_tuning_code_llm_on_single_gpu.ipynb | 2206 +++++++++-------- notebooks/en/rag_zephyr_langchain.ipynb | 1010 ++++---- 3 files changed, 2163 insertions(+), 2157 deletions(-) diff --git a/notebooks/en/faiss_with_hf_datasets_and_clip.ipynb b/notebooks/en/faiss_with_hf_datasets_and_clip.ipynb index fdbc2932..3d409cfe 100644 --- a/notebooks/en/faiss_with_hf_datasets_and_clip.ipynb +++ b/notebooks/en/faiss_with_hf_datasets_and_clip.ipynb @@ -1,574 +1,576 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "q3n0GCRvMXNc" - }, - "source": [ - "# Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS\n", - "\n", - "Embeddings are semantically meaningful compressions of information. They can be used to do similarity search, zero-shot classification or simply train a new model. Use cases for similarity search include searching for similar products in e-commerce, content search in social media and more.\n", - "This notebook walks you through using 🤗transformers, 🤗datasets and FAISS to create and index embeddings from a feature extraction model to later use them for similarity search.\n", - "Let's install necessary libraries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Gqmxny3tNASX" - }, - "outputs": [], - "source": [ - "!pip install -q datasets faiss-gpu transformers sentencepiece" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X4z-2K6MM4yW" - }, - "source": [ - "For this tutorial, we will use [CLIP model](https://huggingface.co/openai/clip-vit-base-patch16) to extract the features. CLIP is a revolutionary model that introduced joint training of a text encoder and an image encoder to connect two modalities." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "5WY6waypNCjT" - }, - "outputs": [], - "source": [ - "import torch\n", - "from PIL import Image\n", - "from transformers import AutoImageProcessor, AutoModel, AutoTokenizer\n", - "import faiss\n", - "import numpy as np\n", - "\n", - "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n", - "\n", - "model = AutoModel.from_pretrained(\"openai/clip-vit-base-patch16\").to(device)\n", - "processor = AutoImageProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n", - "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch16\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_jBbLzJUSOwQ" - }, - "source": [ - "Load the dataset. To keep this notebook light, we will use a small captioning dataset, [jmhessel/newyorker_caption_contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "wMxvOhkA0l-k" - }, - "outputs": [], - "source": [ - "from datasets import load_dataset\n", - "\n", - "ds = load_dataset(\"jmhessel/newyorker_caption_contest\", \"explanation\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_hbosSHI10zy" - }, - "source": [ - "See an example." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 305 - }, - "id": "5gpAhbAcMrm7", - "outputId": "682033f9-da37-4cae-e1bc-4a5fbbb7f2fa" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" - }, - "metadata": {}, - "execution_count": 4 - } - ], - "source": [ - "ds[\"train\"][0][\"image\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "FOxmdk-HM7L6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "outputId": "ff7c2ca8-0c6a-49d0-cfd6-4be775e012a1" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'Two women are looking out a window. There is snow outside, and there is a snowman with human arms.'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 5 - } - ], - "source": [ - "ds[\"train\"][0][\"image_description\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ri187NrFNMaF" - }, - "source": [ - "We don't have to write any function to embed examples or create an index. 🤗 datasets library's FAISS integration abstracts these processes. We can simply use `map` method of the dataset to create a new column with the embeddings for each example like below. Let's create one for text features on the prompt column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "xB0EfabiBHgR" - }, - "outputs": [], - "source": [ - "dataset = ds[\"train\"]\n", - "ds_with_embeddings = dataset.map(lambda example:\n", - " {'embeddings': model.get_text_features(\n", - " **tokenizer([example[\"image_description\"]],\n", - " truncation=True, return_tensors=\"pt\")\n", - " .to(\"cuda\"))[0].detach().cpu().numpy()})\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iUWvvRB3DJwy" - }, - "outputs": [], - "source": [ - "ds_with_embeddings.add_faiss_index(column='embeddings')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qZcZNgSpCH5e" - }, - "source": [ - "We can do the same and get the image embeddings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "AwXh-WlZB6q-" - }, - "outputs": [], - "source": [ - "ds_with_embeddings = ds_with_embeddings.map(lambda example:\n", - " {'image_embeddings': model.get_image_features(\n", - " **processor([example[\"image\"]], return_tensors=\"pt\")\n", - " .to(\"cuda\"))[0].detach().cpu().numpy()})\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "s9OX--PsDMNE" - }, - "outputs": [], - "source": [ - "ds_with_embeddings.add_faiss_index(column='image_embeddings')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1BS3TvQO5GGJ" - }, - "source": [ - "## Querying the data with text prompts" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pxx9fTf83xgE" - }, - "source": [ - "We can now query the dataset with text or image to get similar items from it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "2UQQyXAbNKGa" - }, - "outputs": [], - "source": [ - "prmt = \"a snowy day\"\n", - "prmt_embedding = model.get_text_features(**tokenizer([prmt], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n", - "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', prmt_embedding, k=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 190 - }, - "id": "O5bkNf4M3_Nt", - "outputId": "b56009fe-dc99-4cc3-84e5-559fb3625d30" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "def downscale_images(image):\n", - " width = 200\n", - " ratio = (width / float(image.size[0]))\n", - " height = int((float(image.size[1]) * float(ratio)))\n", - " img = image.resize((width, height), Image.Resampling.LANCZOS)\n", - " return img\n", - "\n", - "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n", - "# see the closest text and image\n", - "print(retrieved_examples[\"image_description\"])\n", - "display(images[0])\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ufn0oqPx5DUR" - }, - "source": [ - "## Querying the data with image prompts" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "R6fNviJ28fns" - }, - "source": [ - "Image similarity inference is similar, where you just call `get_image_features`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "t1BGXpT659Px", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 217 - }, - "outputId": "53478699-5753-4946-90d6-0aa8b76694a6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "import requests\n", - "# image of a beaver\n", - "url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png\"\n", - "image = Image.open(requests.get(url, stream=True).raw)\n", - "display(downscale_images(image))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3kmz4g1v6SJ_" - }, - "source": [ - "Search for the similar image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "qWf-G_Iz4RcD" - }, - "outputs": [], - "source": [ - "img_embedding = model.get_image_features(**processor([image], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n", - "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('image_embeddings', img_embedding, k=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iFGNp5hp6VsV" - }, - "source": [ - "Display the most similar image to the beaver image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 197 - }, - "id": "Pq7IR86k54kP", - "outputId": "fa620b08-4435-4929-f67f-32b3f8f46b70" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n", - "# see the closest text and image\n", - "print(retrieved_examples[\"image_description\"])\n", - "display(images[0])" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Saving, pushing and loading the embeddings\n", - "We can save the dataset with embeddings with `save_faiss_index`.\n" - ], - "metadata": { - "id": "6JEZJlkD8UrZ" - } - }, - { - "cell_type": "code", - "source": [ - "ds_with_embeddings.save_faiss_index('embeddings', 'embeddings/embeddings.faiss')" - ], - "metadata": { - "id": "dXrBMAHx8k51" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "ds_with_embeddings.save_faiss_index('image_embeddings', 'embeddings/image_embeddings.faiss')" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "q3n0GCRvMXNc" + }, + "source": [ + "# Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS\n", + "\n", + "_Authored by: [Merve Noyan](https://huggingface.co/merve)_\n", + "\n", + "Embeddings are semantically meaningful compressions of information. They can be used to do similarity search, zero-shot classification or simply train a new model. Use cases for similarity search include searching for similar products in e-commerce, content search in social media and more.\n", + "This notebook walks you through using 🤗transformers, 🤗datasets and FAISS to create and index embeddings from a feature extraction model to later use them for similarity search.\n", + "Let's install necessary libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Gqmxny3tNASX" + }, + "outputs": [], + "source": [ + "!pip install -q datasets faiss-gpu transformers sentencepiece" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X4z-2K6MM4yW" + }, + "source": [ + "For this tutorial, we will use [CLIP model](https://huggingface.co/openai/clip-vit-base-patch16) to extract the features. CLIP is a revolutionary model that introduced joint training of a text encoder and an image encoder to connect two modalities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5WY6waypNCjT" + }, + "outputs": [], + "source": [ + "import torch\n", + "from PIL import Image\n", + "from transformers import AutoImageProcessor, AutoModel, AutoTokenizer\n", + "import faiss\n", + "import numpy as np\n", + "\n", + "device = torch.device('cuda' if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "model = AutoModel.from_pretrained(\"openai/clip-vit-base-patch16\").to(device)\n", + "processor = AutoImageProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"openai/clip-vit-base-patch16\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_jBbLzJUSOwQ" + }, + "source": [ + "Load the dataset. To keep this notebook light, we will use a small captioning dataset, [jmhessel/newyorker_caption_contest](https://huggingface.co/datasets/jmhessel/newyorker_caption_contest)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wMxvOhkA0l-k" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"jmhessel/newyorker_caption_contest\", \"explanation\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_hbosSHI10zy" + }, + "source": [ + "See an example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 305 + }, + "id": "5gpAhbAcMrm7", + "outputId": "682033f9-da37-4cae-e1bc-4a5fbbb7f2fa" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" ], - "metadata": { - "id": "51dgxmGm-c3x" - }, - "execution_count": null, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {}, + "execution_count": 4 + } + ], + "source": [ + "ds[\"train\"][0][\"image\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FOxmdk-HM7L6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 }, + "outputId": "ff7c2ca8-0c6a-49d0-cfd6-4be775e012a1" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "It's a good practice to store the embeddings in a dataset repository, so we will create one and push our embeddings there to pull later.\n", - "We will login to Hugging Face Hub, create a dataset repository there and push our indexes there and load using `snapshot_download`." + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Two women are looking out a window. There is snow outside, and there is a snowman with human arms.'" ], - "metadata": { - "id": "xO0i-dkY-nK5" + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" } + }, + "metadata": {}, + "execution_count": 5 + } + ], + "source": [ + "ds[\"train\"][0][\"image_description\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ri187NrFNMaF" + }, + "source": [ + "We don't have to write any function to embed examples or create an index. 🤗 datasets library's FAISS integration abstracts these processes. We can simply use `map` method of the dataset to create a new column with the embeddings for each example like below. Let's create one for text features on the prompt column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xB0EfabiBHgR" + }, + "outputs": [], + "source": [ + "dataset = ds[\"train\"]\n", + "ds_with_embeddings = dataset.map(lambda example:\n", + " {'embeddings': model.get_text_features(\n", + " **tokenizer([example[\"image_description\"]],\n", + " truncation=True, return_tensors=\"pt\")\n", + " .to(\"cuda\"))[0].detach().cpu().numpy()})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iUWvvRB3DJwy" + }, + "outputs": [], + "source": [ + "ds_with_embeddings.add_faiss_index(column='embeddings')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qZcZNgSpCH5e" + }, + "source": [ + "We can do the same and get the image embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AwXh-WlZB6q-" + }, + "outputs": [], + "source": [ + "ds_with_embeddings = ds_with_embeddings.map(lambda example:\n", + " {'image_embeddings': model.get_image_features(\n", + " **processor([example[\"image\"]], return_tensors=\"pt\")\n", + " .to(\"cuda\"))[0].detach().cpu().numpy()})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "s9OX--PsDMNE" + }, + "outputs": [], + "source": [ + "ds_with_embeddings.add_faiss_index(column='image_embeddings')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1BS3TvQO5GGJ" + }, + "source": [ + "## Querying the data with text prompts" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pxx9fTf83xgE" + }, + "source": [ + "We can now query the dataset with text or image to get similar items from it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2UQQyXAbNKGa" + }, + "outputs": [], + "source": [ + "prmt = \"a snowy day\"\n", + "prmt_embedding = model.get_text_features(**tokenizer([prmt], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n", + "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embeddings', prmt_embedding, k=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 190 }, + "id": "O5bkNf4M3_Nt", + "outputId": "b56009fe-dc99-4cc3-84e5-559fb3625d30" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "from huggingface_hub import HfApi, notebook_login, snapshot_download\n", - "notebook_login()" - ], - "metadata": { - "id": "ETmGo_KiAiOr" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from huggingface_hub import HfApi\n", - "api = HfApi()\n", - "api.create_repo(\"merve/faiss_embeddings\", repo_type=\"dataset\")\n", - "api.upload_folder(\n", - " folder_path=\"./embeddings\",\n", - " repo_id=\"merve/faiss_embeddings\",\n", - " repo_type=\"dataset\",\n", - ")" - ], - "metadata": { - "id": "K3hmtWQn-k9O" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "snapshot_download(repo_id=\"merve/faiss_embeddings\", repo_type=\"dataset\",\n", - " local_dir=\"downloaded_embeddings\")" - ], - "metadata": { - "id": "UTVoI9LWBp1x" - }, - "execution_count": null, - "outputs": [] + "output_type": "stream", + "name": "stdout", + "text": [ + "['A man is in the snow. A boy with a huge snow shovel is there too. They are outside a house.']\n" + ] }, { - "cell_type": "markdown", - "source": [ - " We can load the embeddings to the dataset with no embeddings using `load_faiss_index`." + "output_type": "display_data", + "data": { + "text/plain": [ + "" ], - "metadata": { - "id": "HGkYTJsM9BVx" - } + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "def downscale_images(image):\n", + " width = 200\n", + " ratio = (width / float(image.size[0]))\n", + " height = int((float(image.size[1]) * float(ratio)))\n", + " img = image.resize((width, height), Image.Resampling.LANCZOS)\n", + " return img\n", + "\n", + "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n", + "# see the closest text and image\n", + "print(retrieved_examples[\"image_description\"])\n", + "display(images[0])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ufn0oqPx5DUR" + }, + "source": [ + "## Querying the data with image prompts" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R6fNviJ28fns" + }, + "source": [ + "Image similarity inference is similar, where you just call `get_image_features`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t1BGXpT659Px", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 217 }, + "outputId": "53478699-5753-4946-90d6-0aa8b76694a6" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "ds = ds[\"train\"]\n", - "ds.load_faiss_index('embeddings', './downloaded_embeddings/embeddings.faiss')\n", - "# infer again\n", - "prmt = \"people under the rain\"\n" + "output_type": "display_data", + "data": { + "text/plain": [ + "" ], - "metadata": { - "id": "mbPvs8kV8xTy" - }, - "execution_count": null, - "outputs": [] + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "import requests\n", + "# image of a beaver\n", + "url = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/beaver.png\"\n", + "image = Image.open(requests.get(url, stream=True).raw)\n", + "display(downscale_images(image))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3kmz4g1v6SJ_" + }, + "source": [ + "Search for the similar image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qWf-G_Iz4RcD" + }, + "outputs": [], + "source": [ + "img_embedding = model.get_image_features(**processor([image], return_tensors=\"pt\", truncation=True).to(\"cuda\"))[0].detach().cpu().numpy()\n", + "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('image_embeddings', img_embedding, k=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iFGNp5hp6VsV" + }, + "source": [ + "Display the most similar image to the beaver image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 }, + "id": "Pq7IR86k54kP", + "outputId": "fa620b08-4435-4929-f67f-32b3f8f46b70" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "prmt_embedding = model.get_text_features(\n", - " **tokenizer([prmt], return_tensors=\"pt\", truncation=True)\n", - " .to(\"cuda\"))[0].detach().cpu().numpy()\n", - "\n", - "scores, retrieved_examples = ds.get_nearest_examples('embeddings', prmt_embedding, k=1)" - ], - "metadata": { - "id": "mc9JmZSG71WZ" - }, - "execution_count": null, - "outputs": [] + "output_type": "stream", + "name": "stdout", + "text": [ + "['Salmon swim upstream but they see a grizzly bear and are in shock. The bear has a smug look on his face when he sees the salmon.']\n" + ] }, { - "cell_type": "code", - "source": [ - "display(retrieved_examples[\"image\"][0])" + "output_type": "display_data", + "data": { + "text/plain": [ + "" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 341 - }, - "id": "wckNsAX-9zox", - "outputId": "8d5008b4-ab8f-4b42-92e7-b29e57c126cb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "image/png": 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\n" + }, + "metadata": {} } - ], - "metadata": { - "accelerator": "GPU", + ], + "source": [ + "images = [downscale_images(image) for image in retrieved_examples[\"image\"]]\n", + "# see the closest text and image\n", + "print(retrieved_examples[\"image_description\"])\n", + "display(images[0])" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Saving, pushing and loading the embeddings\n", + "We can save the dataset with embeddings with `save_faiss_index`.\n" + ], + "metadata": { + "id": "6JEZJlkD8UrZ" + } + }, + { + "cell_type": "code", + "source": [ + "ds_with_embeddings.save_faiss_index('embeddings', 'embeddings/embeddings.faiss')" + ], + "metadata": { + "id": "dXrBMAHx8k51" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "ds_with_embeddings.save_faiss_index('image_embeddings', 'embeddings/image_embeddings.faiss')" + ], + "metadata": { + "id": "51dgxmGm-c3x" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "It's a good practice to store the embeddings in a dataset repository, so we will create one and push our embeddings there to pull later.\n", + "We will login to Hugging Face Hub, create a dataset repository there and push our indexes there and load using `snapshot_download`." + ], + "metadata": { + "id": "xO0i-dkY-nK5" + } + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import HfApi, notebook_login, snapshot_download\n", + "notebook_login()" + ], + "metadata": { + "id": "ETmGo_KiAiOr" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import HfApi\n", + "api = HfApi()\n", + "api.create_repo(\"merve/faiss_embeddings\", repo_type=\"dataset\")\n", + "api.upload_folder(\n", + " folder_path=\"./embeddings\",\n", + " repo_id=\"merve/faiss_embeddings\",\n", + " repo_type=\"dataset\",\n", + ")" + ], + "metadata": { + "id": "K3hmtWQn-k9O" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "snapshot_download(repo_id=\"merve/faiss_embeddings\", repo_type=\"dataset\",\n", + " local_dir=\"downloaded_embeddings\")" + ], + "metadata": { + "id": "UTVoI9LWBp1x" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + " We can load the embeddings to the dataset with no embeddings using `load_faiss_index`." + ], + "metadata": { + "id": "HGkYTJsM9BVx" + } + }, + { + "cell_type": "code", + "source": [ + "ds = ds[\"train\"]\n", + "ds.load_faiss_index('embeddings', './downloaded_embeddings/embeddings.faiss')\n", + "# infer again\n", + "prmt = \"people under the rain\"\n" + ], + "metadata": { + "id": "mbPvs8kV8xTy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "prmt_embedding = model.get_text_features(\n", + " **tokenizer([prmt], return_tensors=\"pt\", truncation=True)\n", + " .to(\"cuda\"))[0].detach().cpu().numpy()\n", + "\n", + "scores, retrieved_examples = ds.get_nearest_examples('embeddings', prmt_embedding, k=1)" + ], + "metadata": { + "id": "mc9JmZSG71WZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "display(retrieved_examples[\"image\"][0])" + ], + "metadata": { "colab": { - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/", + "height": 341 + }, + "id": "wckNsAX-9zox", + "outputId": "8d5008b4-ab8f-4b42-92e7-b29e57c126cb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "image/png": 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\n" + }, + "metadata": {} } + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/en/fine_tuning_code_llm_on_single_gpu.ipynb b/notebooks/en/fine_tuning_code_llm_on_single_gpu.ipynb index fc947e40..153522df 100644 --- a/notebooks/en/fine_tuning_code_llm_on_single_gpu.ipynb +++ b/notebooks/en/fine_tuning_code_llm_on_single_gpu.ipynb @@ -1,1126 +1,1128 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "machine_shape": "hm", - "gpuType": "A100" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "machine_shape": "hm", + "gpuType": "A100" }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Fine-tuning a Code LLM on Custom Code on a single GPU\n", - "\n", - "Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization's internal conventions, or be aware of proprietary libraries.\n", - "\n", - "In this notebook, we'll see show how you can fine-tune a code LLM on private code bases to enhance its contextual awareness and improve a model's usefulness to your organization's needs. Since the code LLMs are quite large, fine-tuning them in a traditional manner can be resource-draining. Worry not! We will show how you can optimize fine-tuning to fit on a single GPU.\n", - "\n", - "\n", - "## Dataset\n", - "\n", - "For this example, we picked the top 10 Hugging Face public repositories on GitHub. We have excluded non-code files from the data, such as images, audio files, presentations, and so on. For Jupyter notebooks, we've kept only cells containing code. The resulting code is stored as a dataset that you can find on the Hugging Face Hub under [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1). It contains repo id, file path, and file content.\n", - "\n", - "\n", - "## Model\n", - "\n", - "We'll finetune [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b), which is a 1B parameter model trained on 80+ programming languages. This is a gated model, so if you plan to run this notebook with this exact model, you'll need to gain access to it on the model's page. Log in to your Hugging Face account to do so:" - ], - "metadata": { - "id": "FNdZ-kD0l78P" - } - }, - { - "cell_type": "code", - "source": [ - "from huggingface_hub import notebook_login\n", - "\n", - "notebook_login()" - ], - "metadata": { - "id": "bPlCJYDK6vrF" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "To get started, let's install all the necessary libraries. As you can see, in addition to `transformers` and `datasets`, we'll be using `peft`, `bitsandbytes`, and `flash-attn` to optimize the training.\n", - "\n", - "By employing parameter-efficient training techniques, we can run this notebook on a single A100 High-RAM GPU." - ], - "metadata": { - "id": "WMVe_c8q43Qo" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Fp7i8WMCjKJG" - }, - "outputs": [], - "source": [ - "!pip install -q transformers datasets peft bitsandbytes flash-attn" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Let's define some variables now. Feel free to play with these." - ], - "metadata": { - "id": "16EdABzt3_Ig" - } - }, - { - "cell_type": "code", - "source": [ - "MODEL=\"bigcode/starcoderbase-1b\" # Model checkpoint on the Hugging Face Hub\n", - "DATASET=\"smangrul/hf-stack-v1\" # Dataset on the Hugging Face Hub\n", - "DATA_COLUMN=\"content\" # Column name containing the code content\n", - "\n", - "SEQ_LENGTH=2048 # Sequence length\n", - "\n", - "# Training arguments\n", - "MAX_STEPS=2000 # max_steps\n", - "BATCH_SIZE=16 # batch_size\n", - "GR_ACC_STEPS=1 # gradient_accumulation_steps\n", - "LR=5e-4 # learning_rate\n", - "LR_SCHEDULER_TYPE=\"cosine\" # lr_scheduler_type\n", - "WEIGHT_DECAY=0.01 # weight_decay\n", - "NUM_WARMUP_STEPS=30 # num_warmup_steps\n", - "EVAL_FREQ=100 # eval_freq\n", - "SAVE_FREQ=100 # save_freq\n", - "LOG_FREQ=25 # log_freq\n", - "OUTPUT_DIR=\"peft-starcoder-lora-a100\" # output_dir\n", - "BF16=True # bf16\n", - "FP16=False # no_fp16\n", - "\n", - "# FIM trasformations arguments\n", - "FIM_RATE=0.5 # fim_rate\n", - "FIM_SPM_RATE=0.5 # fim_spm_rate\n", - "\n", - "# LORA\n", - "LORA_R=8 # lora_r\n", - "LORA_ALPHA=32 # lora_alpha\n", - "LORA_DROPOUT=0.0 # lora_dropout\n", - "LORA_TARGET_MODULES=\"c_proj,c_attn,q_attn,c_fc,c_proj\" # lora_target_modules\n", - "\n", - "# bitsandbytes config\n", - "USE_NESTED_QUANT=True # use_nested_quant\n", - "BNB_4BIT_COMPUTE_DTYPE=\"bfloat16\"# bnb_4bit_compute_dtype\n", - "\n", - "SEED=0" - ], - "metadata": { - "id": "hru3G-CLmqis" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from transformers import (\n", - " AutoModelForCausalLM,\n", - " AutoTokenizer,\n", - " Trainer,\n", - " TrainingArguments,\n", - " logging,\n", - " set_seed,\n", - " BitsAndBytesConfig,\n", - ")\n", - "\n", - "set_seed(SEED)" - ], - "metadata": { - "id": "FyZSXTbJrcnC" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Prepare the data" - ], - "metadata": { - "id": "pO7F5L5AtKo1" - } - }, - { - "cell_type": "markdown", - "source": [ - "Begin by loading the data. As the dataset is likely to be quite large, make sure to enable the streaming mode. Streaming allows us to load the data progressively as we iterate over the dataset instead of downloading the whole dataset at once.\n", - "\n", - "We'll reserve the first 4000 examples as the validation set, and everything else will be the training data." - ], - "metadata": { - "id": "1LmrIZqP0oUE" - } - }, - { - "cell_type": "code", - "source": [ - "from datasets import load_dataset\n", - "import torch\n", - "from tqdm import tqdm\n", - "\n", - "\n", - "dataset = load_dataset(\n", - " DATASET,\n", - " data_dir=\"data\",\n", - " split=\"train\",\n", - " streaming=True,\n", - ")\n", - "\n", - "valid_data = dataset.take(4000)\n", - "train_data = dataset.skip(4000)\n", - "train_data = train_data.shuffle(buffer_size=5000, seed=SEED)" - ], - "metadata": { - "id": "4oJZvZb-1J88" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "At this step, the dataset still contains raw data with code of arbitraty length. For training, we need inputs of fixed length. Let's create an Iterable dataset that would return constant-length chunks of tokens from a stream of text files.\n", - "\n", - "First, let's estimate the average number of characters per token in the dataset, which will help us later estimate the number of tokens in the text buffer later. By default, we'll only take 400 examples (`nb_examples`) from the dataset. Using only a subset of the entire dataset will reduce computational cost while still providing a reasonable estimate of the overall character-to-token ratio." - ], - "metadata": { - "id": "sLQ8t0LM2GR6" - } - }, - { - "cell_type": "code", - "source": [ - "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n", - "\n", - "def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n", - " \"\"\"\n", - " Estimate the average number of characters per token in the dataset.\n", - " \"\"\"\n", - "\n", - " total_characters, total_tokens = 0, 0\n", - " for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n", - " total_characters += len(example[data_column])\n", - " total_tokens += len(tokenizer(example[data_column]).tokens())\n", - "\n", - " return total_characters / total_tokens\n", - "\n", - "\n", - "chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n", - "print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KCiAvydztNsu", - "outputId": "cabf7fd0-a922-4371-cbc6-60ee99ef7469" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 400/400 [00:10<00:00, 39.87it/s] " - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "The character to token ratio of the dataset is: 2.43\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "The character-to-token ratio can also be used as an indicator of the quality of text tokenization. For instance, a character-to-token ratio of 1.0 would mean that each character is represented with a token, which is not very meaningful. This would indicate poor tokenization. In standard English text, one token is typically equivalent to approximately four characters, meaning the character-to-token ratio is around 4.0. We can expect a lower ratio in the code dataset, but generally speaking, a number between 2.0 and 3.5 can be considered good enough." - ], - "metadata": { - "id": "6F13VGobB3Ma" - } - }, - { - "cell_type": "markdown", - "source": [ - "**Optional FIM transformations**\n", - "\n", - "\n", - "Autoregressive language models typically generate sequences from left to right. By applying the FIM transformations, the model can also learn to infill text. Check out [\"Efficient Training of Language Models to Fill in the Middle\" paper](https://arxiv.org/pdf/2207.14255.pdf) to learn more about the technique.\n", - "We'll define the FIM transformations here and will use them when creating the Iterable Dataset. However, if you want to omit transformations, feel free to set `fim_rate` to 0." - ], - "metadata": { - "id": "rcwYFRPpwxea" - } - }, - { - "cell_type": "code", - "source": [ - "import functools\n", - "import numpy as np\n", - "\n", - "\n", - "# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n", - "@functools.lru_cache(maxsize=None)\n", - "def get_fim_token_ids(tokenizer):\n", - " try:\n", - " FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n", - " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n", - " tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n", - " )\n", - " except KeyError:\n", - " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n", - " return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n", - "\n", - "\n", - "## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n", - "def permute(\n", - " sample,\n", - " np_rng,\n", - " suffix_tok_id,\n", - " prefix_tok_id,\n", - " middle_tok_id,\n", - " pad_tok_id,\n", - " fim_rate=0.5,\n", - " fim_spm_rate=0.5,\n", - " truncate_or_pad=False,\n", - "):\n", - " \"\"\"\n", - " Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n", - " PSM and SPM (with a probability of fim_spm_rate).\n", - " \"\"\"\n", - "\n", - " # The if condition will trigger with the probability of fim_rate\n", - " # This means FIM transformations will apply to samples with a probability of fim_rate\n", - " if np_rng.binomial(1, fim_rate):\n", - "\n", - " # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n", - " boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n", - " boundaries.sort()\n", - "\n", - " prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n", - " middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n", - " suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n", - "\n", - " if truncate_or_pad:\n", - " # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n", - " new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n", - " diff = new_length - len(sample)\n", - "\n", - " # trancate or pad if there's a difference in length between the new length and the original\n", - " if diff > 0:\n", - " if suffix.shape[0] <= diff:\n", - " return sample, np_rng\n", - " suffix = suffix[: suffix.shape[0] - diff]\n", - " elif diff < 0:\n", - " suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n", - "\n", - " # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n", - " # SPM: suffix, prefix, middle\n", - " if np_rng.binomial(1, fim_spm_rate):\n", - " new_sample = np.concatenate(\n", - " [\n", - " [prefix_tok_id, suffix_tok_id],\n", - " suffix,\n", - " [middle_tok_id],\n", - " prefix,\n", - " middle,\n", - " ]\n", - " )\n", - " # Otherwise, apply the PSM variant of FIM transformations\n", - " # PSM: prefix, suffix, middle\n", - " else:\n", - "\n", - " new_sample = np.concatenate(\n", - " [\n", - " [prefix_tok_id],\n", - " prefix,\n", - " [suffix_tok_id],\n", - " suffix,\n", - " [middle_tok_id],\n", - " middle,\n", - " ]\n", - " )\n", - " else:\n", - " # don't apply FIM transformations\n", - " new_sample = sample\n", - "\n", - " return list(new_sample), np_rng\n" - ], - "metadata": { - "id": "zmejYvEKw1E-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Let's define the `ConstantLengthDataset`, an Iterable dataset that will return constant-length chunks of tokens. To do so, we'll read a buffer of text from the original dataset until we hit the size limits and then apply tokenizer to convert the raw text into tokenized inputs. Optionally, we'll perform FIM transformations on some sequences (the proportion of sequences affected is controlled by `fim_rate`).\n", - "\n", - "Once defined, we can create instances of the `ConstantLengthDataset` from both training and validation data." - ], - "metadata": { - "id": "AwW5FviD9xBH" - } - }, - { - "cell_type": "code", - "source": [ - "from torch.utils.data import IterableDataset\n", - "from torch.utils.data.dataloader import DataLoader\n", - "import random\n", - "\n", - "# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n", - "\n", - "class ConstantLengthDataset(IterableDataset):\n", - " \"\"\"\n", - " Iterable dataset that returns constant length chunks of tokens from stream of text files.\n", - " Args:\n", - " tokenizer (Tokenizer): The processor used for proccessing the data.\n", - " dataset (dataset.Dataset): Dataset with text files.\n", - " infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n", - " seq_length (int): Length of token sequences to return.\n", - " num_of_sequences (int): Number of token sequences to keep in buffer.\n", - " chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n", - " fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n", - " fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n", - " seed (int): Seed for random number generator.\n", - " \"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " tokenizer,\n", - " dataset,\n", - " infinite=False,\n", - " seq_length=1024,\n", - " num_of_sequences=1024,\n", - " chars_per_token=3.6,\n", - " content_field=\"content\",\n", - " fim_rate=0.5,\n", - " fim_spm_rate=0.5,\n", - " seed=0,\n", - " ):\n", - " self.tokenizer = tokenizer\n", - " self.concat_token_id = tokenizer.eos_token_id\n", - " self.dataset = dataset\n", - " self.seq_length = seq_length\n", - " self.infinite = infinite\n", - " self.current_size = 0\n", - " self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n", - " self.content_field = content_field\n", - " self.fim_rate = fim_rate\n", - " self.fim_spm_rate = fim_spm_rate\n", - " self.seed = seed\n", - "\n", - " (\n", - " self.suffix_tok_id,\n", - " self.prefix_tok_id,\n", - " self.middle_tok_id,\n", - " self.pad_tok_id,\n", - " ) = get_fim_token_ids(self.tokenizer)\n", - " if not self.suffix_tok_id and self.fim_rate > 0:\n", - " print(\"FIM is not supported by tokenizer, disabling FIM\")\n", - " self.fim_rate = 0\n", - "\n", - " def __iter__(self):\n", - " iterator = iter(self.dataset)\n", - " more_examples = True\n", - " np_rng = np.random.RandomState(seed=self.seed)\n", - " while more_examples:\n", - " buffer, buffer_len = [], 0\n", - " while True:\n", - " if buffer_len >= self.max_buffer_size:\n", - " break\n", - " try:\n", - " buffer.append(next(iterator)[self.content_field])\n", - " buffer_len += len(buffer[-1])\n", - " except StopIteration:\n", - " if self.infinite:\n", - " iterator = iter(self.dataset)\n", - " else:\n", - " more_examples = False\n", - " break\n", - " tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n", - " all_token_ids = []\n", - "\n", - " for tokenized_input in tokenized_inputs:\n", - " # optionally do FIM permutations\n", - " if self.fim_rate > 0:\n", - " tokenized_input, np_rng = permute(\n", - " tokenized_input,\n", - " np_rng,\n", - " self.suffix_tok_id,\n", - " self.prefix_tok_id,\n", - " self.middle_tok_id,\n", - " self.pad_tok_id,\n", - " fim_rate=self.fim_rate,\n", - " fim_spm_rate=self.fim_spm_rate,\n", - " truncate_or_pad=False,\n", - " )\n", - "\n", - " all_token_ids.extend(tokenized_input + [self.concat_token_id])\n", - " examples = []\n", - " for i in range(0, len(all_token_ids), self.seq_length):\n", - " input_ids = all_token_ids[i : i + self.seq_length]\n", - " if len(input_ids) == self.seq_length:\n", - " examples.append(input_ids)\n", - " random.shuffle(examples)\n", - " for example in examples:\n", - " self.current_size += 1\n", - " yield {\n", - " \"input_ids\": torch.LongTensor(example),\n", - " \"labels\": torch.LongTensor(example),\n", - " }\n", - "\n", - "\n", - "train_dataset = ConstantLengthDataset(\n", - " tokenizer,\n", - " train_data,\n", - " infinite=True,\n", - " seq_length=SEQ_LENGTH,\n", - " chars_per_token=chars_per_token,\n", - " content_field=DATA_COLUMN,\n", - " fim_rate=FIM_RATE,\n", - " fim_spm_rate=FIM_SPM_RATE,\n", - " seed=SEED,\n", - ")\n", - "eval_dataset = ConstantLengthDataset(\n", - " tokenizer,\n", - " valid_data,\n", - " infinite=False,\n", - " seq_length=SEQ_LENGTH,\n", - " chars_per_token=chars_per_token,\n", - " content_field=DATA_COLUMN,\n", - " fim_rate=FIM_RATE,\n", - " fim_spm_rate=FIM_SPM_RATE,\n", - " seed=SEED,\n", - ")" - ], - "metadata": { - "id": "AgDW-692wzOl" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Prepare the model" - ], - "metadata": { - "id": "rxev1sk6tRW9" - } - }, - { - "cell_type": "markdown", - "source": [ - "Now that the data is prepared, it's time to load the model! We're going to load the quantized version of the model.\n", - "\n", - "This will allow us to reduce memory usage, as quantization represents data with fewer bits. We'll use the `bitsandbytes` library to quantize the model, as it has a nice integration with `transformers`. All we need to do is define a `bitsandbytes` config, and then use it when loading the model.\n", - "\n", - "There are different variants of 4bit quantization, but generally, we recommend using NF4 quantization for better performance (`bnb_4bit_quant_type=\"nf4\"`).\n", - "\n", - "The `bnb_4bit_use_double_quant` option adds a second quantization after the first one to save an additional 0.4 bits per parameter.\n", - "\n", - "To learn more about quantization, check out the [\"Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA\" blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).\n", - "\n", - "Once defined, pass the config to the `from_pretrained` method to load the quantized version of the model." - ], - "metadata": { - "id": "UCtWV-U42Eq_" - } - }, - { - "cell_type": "code", - "source": [ - "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n", - "from peft.tuners.lora import LoraLayer\n", - "\n", - "load_in_8bit = False\n", - "\n", - "# 4-bit quantization\n", - "compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n", - "\n", - "bnb_config = BitsAndBytesConfig(\n", - " load_in_4bit=True,\n", - " bnb_4bit_quant_type=\"nf4\",\n", - " bnb_4bit_compute_dtype=compute_dtype,\n", - " bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n", - ")\n", - "\n", - "device_map = {\"\": 0}\n", - "\n", - "model = AutoModelForCausalLM.from_pretrained(\n", - " MODEL,\n", - " load_in_8bit=load_in_8bit,\n", - " quantization_config=bnb_config,\n", - " device_map=device_map,\n", - " use_cache=False, # We will be using gradient checkpointing\n", - " trust_remote_code=True,\n", - " use_flash_attention_2=True,\n", - ")\n" - ], - "metadata": { - "id": "XuwoX6U2DUvK" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "When using a quantized model for training, you need to call the `prepare_model_for_kbit_training()` function to preprocess the quantized model for training." - ], - "metadata": { - "id": "bO9e2FV8D8ZF" - } - }, - { - "cell_type": "code", - "source": [ - "model = prepare_model_for_kbit_training(model)" - ], - "metadata": { - "id": "Qb_eB4xzEDBk" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Now that the quantized model is ready, we can set up a LoRA configuration. LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.\n", - "\n", - "To train a model using LoRA technique, we need to wrap the base model as a `PeftModel`. This involves definign LoRA configuration with `LoraConfig`, and wrapping the original model with `get_peft_model()` using the `LoraConfig`.\n", - "\n", - "To learn more about LoRA and its parameters, refer to [PEFT documentation](https://huggingface.co/docs/peft/conceptual_guides/lora)." - ], - "metadata": { - "id": "lmnLjPZpDVtg" - } - }, - { - "cell_type": "code", - "source": [ - "# Set up lora\n", - "peft_config = LoraConfig(\n", - " lora_alpha=LORA_ALPHA,\n", - " lora_dropout=LORA_DROPOUT,\n", - " r=LORA_R,\n", - " bias=\"none\",\n", - " task_type=\"CAUSAL_LM\",\n", - " target_modules=LORA_TARGET_MODULES.split(\",\"),\n", - ")\n", - "\n", - "model = get_peft_model(model, peft_config)\n", - "model.print_trainable_parameters()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_pAUU2FR2Gey", - "outputId": "63328c2b-e693-49b1-ce0a-3ca8722f852a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "trainable params: 5,554,176 || all params: 1,142,761,472 || trainable%: 0.4860310866343243\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As you can see, by applying LoRA technique we will now need to train less than 1% of the parameters." - ], - "metadata": { - "id": "tHe7AElXzXVV" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Train the model" - ], - "metadata": { - "id": "T_CqVydc40IM" - } - }, - { - "cell_type": "markdown", - "source": [ - "Now that we have prepared the data, and optimized the model, we are ready to bring everything together to start the training.\n", - "\n", - "To instantiate a `Trainer`, you need to define the training configuration. The most important is the `TrainingArguments`, which is a class that contains all the attributes to configure the training.\n", - "\n", - "These are similar to any other kind of model training you may run, so we won't go into detail here." - ], - "metadata": { - "id": "Q_iN2khjrbD3" - } - }, - { - "cell_type": "code", - "source": [ - "train_data.start_iteration = 0\n", - "\n", - "\n", - "training_args = TrainingArguments(\n", - " output_dir=f\"Your_HF_username/{OUTPUT_DIR}\",\n", - " dataloader_drop_last=True,\n", - " evaluation_strategy=\"steps\",\n", - " save_strategy=\"steps\",\n", - " max_steps=MAX_STEPS,\n", - " eval_steps=EVAL_FREQ,\n", - " save_steps=SAVE_FREQ,\n", - " logging_steps=LOG_FREQ,\n", - " per_device_train_batch_size=BATCH_SIZE,\n", - " per_device_eval_batch_size=BATCH_SIZE,\n", - " learning_rate=LR,\n", - " lr_scheduler_type=LR_SCHEDULER_TYPE,\n", - " warmup_steps=NUM_WARMUP_STEPS,\n", - " gradient_accumulation_steps=GR_ACC_STEPS,\n", - " gradient_checkpointing=True,\n", - " fp16=FP16,\n", - " bf16=BF16,\n", - " weight_decay=WEIGHT_DECAY,\n", - " push_to_hub=True,\n", - " include_tokens_per_second=True,\n", - ")\n" - ], - "metadata": { - "id": "65QHS8l1tKQe" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "As a final step, instantiate the `Trainer` and call the `train` method. " - ], - "metadata": { - "id": "kB_fLRex09ut" - } - }, - { - "cell_type": "code", - "source": [ - "trainer = Trainer(\n", - " model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n", - ")\n", - "\n", - "print(\"Training...\")\n", - "trainer.train()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "rS3nVwhUC69O", - "outputId": "61a5bdb2-b7d0-4aed-8290-4bf20c2ccd38" - }, - "execution_count": null, - "outputs": [ - { - "metadata": { - "tags": null - }, - "name": "stdout", - "output_type": "stream", - "text": [ - "Training...\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
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20004.7042005.655665

" - ] - }, - "metadata": {} - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "TrainOutput(global_step=2000, training_loss=4.885598585128784, metrics={'train_runtime': 15380.3075, 'train_samples_per_second': 2.081, 'train_steps_per_second': 0.13, 'train_tokens_per_second': 4261.033, 'total_flos': 4.0317260660736e+17, 'train_loss': 4.885598585128784, 'epoch': 1.0})" - ] - }, - "metadata": {}, - "execution_count": 19 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Finally, you can push the fine-tuned model to your Hub repository to share with your team." - ], - "metadata": { - "id": "aAERlCnt1PEW" - } - }, - { - "cell_type": "code", - "source": [ - "trainer.push_to_hub()" - ], - "metadata": { - "id": "1h7_AUTTDwE1" - }, - "execution_count": null, - "outputs": [] + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Fine-tuning a Code LLM on Custom Code on a single GPU\n", + "\n", + "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n", + "\n", + "Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization's internal conventions, or be aware of proprietary libraries.\n", + "\n", + "In this notebook, we'll see show how you can fine-tune a code LLM on private code bases to enhance its contextual awareness and improve a model's usefulness to your organization's needs. Since the code LLMs are quite large, fine-tuning them in a traditional manner can be resource-draining. Worry not! We will show how you can optimize fine-tuning to fit on a single GPU.\n", + "\n", + "\n", + "## Dataset\n", + "\n", + "For this example, we picked the top 10 Hugging Face public repositories on GitHub. We have excluded non-code files from the data, such as images, audio files, presentations, and so on. For Jupyter notebooks, we've kept only cells containing code. The resulting code is stored as a dataset that you can find on the Hugging Face Hub under [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1). It contains repo id, file path, and file content.\n", + "\n", + "\n", + "## Model\n", + "\n", + "We'll finetune [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b), which is a 1B parameter model trained on 80+ programming languages. This is a gated model, so if you plan to run this notebook with this exact model, you'll need to gain access to it on the model's page. Log in to your Hugging Face account to do so:" + ], + "metadata": { + "id": "FNdZ-kD0l78P" + } + }, + { + "cell_type": "code", + "source": [ + "from huggingface_hub import notebook_login\n", + "\n", + "notebook_login()" + ], + "metadata": { + "id": "bPlCJYDK6vrF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "To get started, let's install all the necessary libraries. As you can see, in addition to `transformers` and `datasets`, we'll be using `peft`, `bitsandbytes`, and `flash-attn` to optimize the training.\n", + "\n", + "By employing parameter-efficient training techniques, we can run this notebook on a single A100 High-RAM GPU." + ], + "metadata": { + "id": "WMVe_c8q43Qo" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Fp7i8WMCjKJG" + }, + "outputs": [], + "source": [ + "!pip install -q transformers datasets peft bitsandbytes flash-attn" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's define some variables now. Feel free to play with these." + ], + "metadata": { + "id": "16EdABzt3_Ig" + } + }, + { + "cell_type": "code", + "source": [ + "MODEL=\"bigcode/starcoderbase-1b\" # Model checkpoint on the Hugging Face Hub\n", + "DATASET=\"smangrul/hf-stack-v1\" # Dataset on the Hugging Face Hub\n", + "DATA_COLUMN=\"content\" # Column name containing the code content\n", + "\n", + "SEQ_LENGTH=2048 # Sequence length\n", + "\n", + "# Training arguments\n", + "MAX_STEPS=2000 # max_steps\n", + "BATCH_SIZE=16 # batch_size\n", + "GR_ACC_STEPS=1 # gradient_accumulation_steps\n", + "LR=5e-4 # learning_rate\n", + "LR_SCHEDULER_TYPE=\"cosine\" # lr_scheduler_type\n", + "WEIGHT_DECAY=0.01 # weight_decay\n", + "NUM_WARMUP_STEPS=30 # num_warmup_steps\n", + "EVAL_FREQ=100 # eval_freq\n", + "SAVE_FREQ=100 # save_freq\n", + "LOG_FREQ=25 # log_freq\n", + "OUTPUT_DIR=\"peft-starcoder-lora-a100\" # output_dir\n", + "BF16=True # bf16\n", + "FP16=False # no_fp16\n", + "\n", + "# FIM trasformations arguments\n", + "FIM_RATE=0.5 # fim_rate\n", + "FIM_SPM_RATE=0.5 # fim_spm_rate\n", + "\n", + "# LORA\n", + "LORA_R=8 # lora_r\n", + "LORA_ALPHA=32 # lora_alpha\n", + "LORA_DROPOUT=0.0 # lora_dropout\n", + "LORA_TARGET_MODULES=\"c_proj,c_attn,q_attn,c_fc,c_proj\" # lora_target_modules\n", + "\n", + "# bitsandbytes config\n", + "USE_NESTED_QUANT=True # use_nested_quant\n", + "BNB_4BIT_COMPUTE_DTYPE=\"bfloat16\"# bnb_4bit_compute_dtype\n", + "\n", + "SEED=0" + ], + "metadata": { + "id": "hru3G-CLmqis" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " Trainer,\n", + " TrainingArguments,\n", + " logging,\n", + " set_seed,\n", + " BitsAndBytesConfig,\n", + ")\n", + "\n", + "set_seed(SEED)" + ], + "metadata": { + "id": "FyZSXTbJrcnC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Prepare the data" + ], + "metadata": { + "id": "pO7F5L5AtKo1" + } + }, + { + "cell_type": "markdown", + "source": [ + "Begin by loading the data. As the dataset is likely to be quite large, make sure to enable the streaming mode. Streaming allows us to load the data progressively as we iterate over the dataset instead of downloading the whole dataset at once.\n", + "\n", + "We'll reserve the first 4000 examples as the validation set, and everything else will be the training data." + ], + "metadata": { + "id": "1LmrIZqP0oUE" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import load_dataset\n", + "import torch\n", + "from tqdm import tqdm\n", + "\n", + "\n", + "dataset = load_dataset(\n", + " DATASET,\n", + " data_dir=\"data\",\n", + " split=\"train\",\n", + " streaming=True,\n", + ")\n", + "\n", + "valid_data = dataset.take(4000)\n", + "train_data = dataset.skip(4000)\n", + "train_data = train_data.shuffle(buffer_size=5000, seed=SEED)" + ], + "metadata": { + "id": "4oJZvZb-1J88" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "At this step, the dataset still contains raw data with code of arbitraty length. For training, we need inputs of fixed length. Let's create an Iterable dataset that would return constant-length chunks of tokens from a stream of text files.\n", + "\n", + "First, let's estimate the average number of characters per token in the dataset, which will help us later estimate the number of tokens in the text buffer later. By default, we'll only take 400 examples (`nb_examples`) from the dataset. Using only a subset of the entire dataset will reduce computational cost while still providing a reasonable estimate of the overall character-to-token ratio." + ], + "metadata": { + "id": "sLQ8t0LM2GR6" + } + }, + { + "cell_type": "code", + "source": [ + "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n", + "\n", + "def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n", + " \"\"\"\n", + " Estimate the average number of characters per token in the dataset.\n", + " \"\"\"\n", + "\n", + " total_characters, total_tokens = 0, 0\n", + " for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n", + " total_characters += len(example[data_column])\n", + " total_tokens += len(tokenizer(example[data_column]).tokens())\n", + "\n", + " return total_characters / total_tokens\n", + "\n", + "\n", + "chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n", + "print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "KCiAvydztNsu", + "outputId": "cabf7fd0-a922-4371-cbc6-60ee99ef7469" + }, + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "## Inference\n", - "\n", - "Once the model is uploaded to Hub, we can use it for inference. To do so we first initialize the original base model and its tokenizer. Next, we need to merge the fine-duned weights with the base model." - ], - "metadata": { - "id": "KBVH7uFOM_UF" - } + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 400/400 [00:10<00:00, 39.87it/s] " + ] }, { - "cell_type": "code", - "source": [ - "from peft import PeftModel\n", - "import torch\n", - "\n", - "# load the original model first\n", - "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n", - "base_model = AutoModelForCausalLM.from_pretrained(\n", - " MODEL,\n", - " quantization_config=None,\n", - " device_map=None,\n", - " trust_remote_code=True,\n", - " torch_dtype=torch.bfloat16,\n", - ").cuda()\n", - "\n", - "# merge fine-tuned weights with the base model\n", - "peft_model_id = f\"Your_HF_username/{OUTPUT_DIR}\"\n", - "model = PeftModel.from_pretrained(base_model, peft_model_id)\n", - "model.merge_and_unload()" - ], - "metadata": { - "id": "jtL37piINBFe" - }, - "execution_count": null, - "outputs": [] + "output_type": "stream", + "name": "stdout", + "text": [ + "The character to token ratio of the dataset is: 2.43\n" + ] }, { - "cell_type": "markdown", - "source": [ - "Now we can use the merged model for inference. For convenience, we'll define a `get_code_completion` - feel free to experiment with text generation parameters!" - ], - "metadata": { - "id": "3USQ2suvDi9M" - } + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The character-to-token ratio can also be used as an indicator of the quality of text tokenization. For instance, a character-to-token ratio of 1.0 would mean that each character is represented with a token, which is not very meaningful. This would indicate poor tokenization. In standard English text, one token is typically equivalent to approximately four characters, meaning the character-to-token ratio is around 4.0. We can expect a lower ratio in the code dataset, but generally speaking, a number between 2.0 and 3.5 can be considered good enough." + ], + "metadata": { + "id": "6F13VGobB3Ma" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Optional FIM transformations**\n", + "\n", + "\n", + "Autoregressive language models typically generate sequences from left to right. By applying the FIM transformations, the model can also learn to infill text. Check out [\"Efficient Training of Language Models to Fill in the Middle\" paper](https://arxiv.org/pdf/2207.14255.pdf) to learn more about the technique.\n", + "We'll define the FIM transformations here and will use them when creating the Iterable Dataset. However, if you want to omit transformations, feel free to set `fim_rate` to 0." + ], + "metadata": { + "id": "rcwYFRPpwxea" + } + }, + { + "cell_type": "code", + "source": [ + "import functools\n", + "import numpy as np\n", + "\n", + "\n", + "# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n", + "@functools.lru_cache(maxsize=None)\n", + "def get_fim_token_ids(tokenizer):\n", + " try:\n", + " FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n", + " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n", + " tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n", + " )\n", + " except KeyError:\n", + " suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n", + " return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n", + "\n", + "\n", + "## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n", + "def permute(\n", + " sample,\n", + " np_rng,\n", + " suffix_tok_id,\n", + " prefix_tok_id,\n", + " middle_tok_id,\n", + " pad_tok_id,\n", + " fim_rate=0.5,\n", + " fim_spm_rate=0.5,\n", + " truncate_or_pad=False,\n", + "):\n", + " \"\"\"\n", + " Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n", + " PSM and SPM (with a probability of fim_spm_rate).\n", + " \"\"\"\n", + "\n", + " # The if condition will trigger with the probability of fim_rate\n", + " # This means FIM transformations will apply to samples with a probability of fim_rate\n", + " if np_rng.binomial(1, fim_rate):\n", + "\n", + " # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n", + " boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n", + " boundaries.sort()\n", + "\n", + " prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n", + " middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n", + " suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n", + "\n", + " if truncate_or_pad:\n", + " # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n", + " new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n", + " diff = new_length - len(sample)\n", + "\n", + " # trancate or pad if there's a difference in length between the new length and the original\n", + " if diff > 0:\n", + " if suffix.shape[0] <= diff:\n", + " return sample, np_rng\n", + " suffix = suffix[: suffix.shape[0] - diff]\n", + " elif diff < 0:\n", + " suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n", + "\n", + " # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n", + " # SPM: suffix, prefix, middle\n", + " if np_rng.binomial(1, fim_spm_rate):\n", + " new_sample = np.concatenate(\n", + " [\n", + " [prefix_tok_id, suffix_tok_id],\n", + " suffix,\n", + " [middle_tok_id],\n", + " prefix,\n", + " middle,\n", + " ]\n", + " )\n", + " # Otherwise, apply the PSM variant of FIM transformations\n", + " # PSM: prefix, suffix, middle\n", + " else:\n", + "\n", + " new_sample = np.concatenate(\n", + " [\n", + " [prefix_tok_id],\n", + " prefix,\n", + " [suffix_tok_id],\n", + " suffix,\n", + " [middle_tok_id],\n", + " middle,\n", + " ]\n", + " )\n", + " else:\n", + " # don't apply FIM transformations\n", + " new_sample = sample\n", + "\n", + " return list(new_sample), np_rng\n" + ], + "metadata": { + "id": "zmejYvEKw1E-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's define the `ConstantLengthDataset`, an Iterable dataset that will return constant-length chunks of tokens. To do so, we'll read a buffer of text from the original dataset until we hit the size limits and then apply tokenizer to convert the raw text into tokenized inputs. Optionally, we'll perform FIM transformations on some sequences (the proportion of sequences affected is controlled by `fim_rate`).\n", + "\n", + "Once defined, we can create instances of the `ConstantLengthDataset` from both training and validation data." + ], + "metadata": { + "id": "AwW5FviD9xBH" + } + }, + { + "cell_type": "code", + "source": [ + "from torch.utils.data import IterableDataset\n", + "from torch.utils.data.dataloader import DataLoader\n", + "import random\n", + "\n", + "# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n", + "\n", + "class ConstantLengthDataset(IterableDataset):\n", + " \"\"\"\n", + " Iterable dataset that returns constant length chunks of tokens from stream of text files.\n", + " Args:\n", + " tokenizer (Tokenizer): The processor used for proccessing the data.\n", + " dataset (dataset.Dataset): Dataset with text files.\n", + " infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n", + " seq_length (int): Length of token sequences to return.\n", + " num_of_sequences (int): Number of token sequences to keep in buffer.\n", + " chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n", + " fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n", + " fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n", + " seed (int): Seed for random number generator.\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " tokenizer,\n", + " dataset,\n", + " infinite=False,\n", + " seq_length=1024,\n", + " num_of_sequences=1024,\n", + " chars_per_token=3.6,\n", + " content_field=\"content\",\n", + " fim_rate=0.5,\n", + " fim_spm_rate=0.5,\n", + " seed=0,\n", + " ):\n", + " self.tokenizer = tokenizer\n", + " self.concat_token_id = tokenizer.eos_token_id\n", + " self.dataset = dataset\n", + " self.seq_length = seq_length\n", + " self.infinite = infinite\n", + " self.current_size = 0\n", + " self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n", + " self.content_field = content_field\n", + " self.fim_rate = fim_rate\n", + " self.fim_spm_rate = fim_spm_rate\n", + " self.seed = seed\n", + "\n", + " (\n", + " self.suffix_tok_id,\n", + " self.prefix_tok_id,\n", + " self.middle_tok_id,\n", + " self.pad_tok_id,\n", + " ) = get_fim_token_ids(self.tokenizer)\n", + " if not self.suffix_tok_id and self.fim_rate > 0:\n", + " print(\"FIM is not supported by tokenizer, disabling FIM\")\n", + " self.fim_rate = 0\n", + "\n", + " def __iter__(self):\n", + " iterator = iter(self.dataset)\n", + " more_examples = True\n", + " np_rng = np.random.RandomState(seed=self.seed)\n", + " while more_examples:\n", + " buffer, buffer_len = [], 0\n", + " while True:\n", + " if buffer_len >= self.max_buffer_size:\n", + " break\n", + " try:\n", + " buffer.append(next(iterator)[self.content_field])\n", + " buffer_len += len(buffer[-1])\n", + " except StopIteration:\n", + " if self.infinite:\n", + " iterator = iter(self.dataset)\n", + " else:\n", + " more_examples = False\n", + " break\n", + " tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n", + " all_token_ids = []\n", + "\n", + " for tokenized_input in tokenized_inputs:\n", + " # optionally do FIM permutations\n", + " if self.fim_rate > 0:\n", + " tokenized_input, np_rng = permute(\n", + " tokenized_input,\n", + " np_rng,\n", + " self.suffix_tok_id,\n", + " self.prefix_tok_id,\n", + " self.middle_tok_id,\n", + " self.pad_tok_id,\n", + " fim_rate=self.fim_rate,\n", + " fim_spm_rate=self.fim_spm_rate,\n", + " truncate_or_pad=False,\n", + " )\n", + "\n", + " all_token_ids.extend(tokenized_input + [self.concat_token_id])\n", + " examples = []\n", + " for i in range(0, len(all_token_ids), self.seq_length):\n", + " input_ids = all_token_ids[i : i + self.seq_length]\n", + " if len(input_ids) == self.seq_length:\n", + " examples.append(input_ids)\n", + " random.shuffle(examples)\n", + " for example in examples:\n", + " self.current_size += 1\n", + " yield {\n", + " \"input_ids\": torch.LongTensor(example),\n", + " \"labels\": torch.LongTensor(example),\n", + " }\n", + "\n", + "\n", + "train_dataset = ConstantLengthDataset(\n", + " tokenizer,\n", + " train_data,\n", + " infinite=True,\n", + " seq_length=SEQ_LENGTH,\n", + " chars_per_token=chars_per_token,\n", + " content_field=DATA_COLUMN,\n", + " fim_rate=FIM_RATE,\n", + " fim_spm_rate=FIM_SPM_RATE,\n", + " seed=SEED,\n", + ")\n", + "eval_dataset = ConstantLengthDataset(\n", + " tokenizer,\n", + " valid_data,\n", + " infinite=False,\n", + " seq_length=SEQ_LENGTH,\n", + " chars_per_token=chars_per_token,\n", + " content_field=DATA_COLUMN,\n", + " fim_rate=FIM_RATE,\n", + " fim_spm_rate=FIM_SPM_RATE,\n", + " seed=SEED,\n", + ")" + ], + "metadata": { + "id": "AgDW-692wzOl" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Prepare the model" + ], + "metadata": { + "id": "rxev1sk6tRW9" + } + }, + { + "cell_type": "markdown", + "source": [ + "Now that the data is prepared, it's time to load the model! We're going to load the quantized version of the model.\n", + "\n", + "This will allow us to reduce memory usage, as quantization represents data with fewer bits. We'll use the `bitsandbytes` library to quantize the model, as it has a nice integration with `transformers`. All we need to do is define a `bitsandbytes` config, and then use it when loading the model.\n", + "\n", + "There are different variants of 4bit quantization, but generally, we recommend using NF4 quantization for better performance (`bnb_4bit_quant_type=\"nf4\"`).\n", + "\n", + "The `bnb_4bit_use_double_quant` option adds a second quantization after the first one to save an additional 0.4 bits per parameter.\n", + "\n", + "To learn more about quantization, check out the [\"Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA\" blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).\n", + "\n", + "Once defined, pass the config to the `from_pretrained` method to load the quantized version of the model." + ], + "metadata": { + "id": "UCtWV-U42Eq_" + } + }, + { + "cell_type": "code", + "source": [ + "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n", + "from peft.tuners.lora import LoraLayer\n", + "\n", + "load_in_8bit = False\n", + "\n", + "# 4-bit quantization\n", + "compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n", + "\n", + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=compute_dtype,\n", + " bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n", + ")\n", + "\n", + "device_map = {\"\": 0}\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL,\n", + " load_in_8bit=load_in_8bit,\n", + " quantization_config=bnb_config,\n", + " device_map=device_map,\n", + " use_cache=False, # We will be using gradient checkpointing\n", + " trust_remote_code=True,\n", + " use_flash_attention_2=True,\n", + ")\n" + ], + "metadata": { + "id": "XuwoX6U2DUvK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "When using a quantized model for training, you need to call the `prepare_model_for_kbit_training()` function to preprocess the quantized model for training." + ], + "metadata": { + "id": "bO9e2FV8D8ZF" + } + }, + { + "cell_type": "code", + "source": [ + "model = prepare_model_for_kbit_training(model)" + ], + "metadata": { + "id": "Qb_eB4xzEDBk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now that the quantized model is ready, we can set up a LoRA configuration. LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.\n", + "\n", + "To train a model using LoRA technique, we need to wrap the base model as a `PeftModel`. This involves definign LoRA configuration with `LoraConfig`, and wrapping the original model with `get_peft_model()` using the `LoraConfig`.\n", + "\n", + "To learn more about LoRA and its parameters, refer to [PEFT documentation](https://huggingface.co/docs/peft/conceptual_guides/lora)." + ], + "metadata": { + "id": "lmnLjPZpDVtg" + } + }, + { + "cell_type": "code", + "source": [ + "# Set up lora\n", + "peft_config = LoraConfig(\n", + " lora_alpha=LORA_ALPHA,\n", + " lora_dropout=LORA_DROPOUT,\n", + " r=LORA_R,\n", + " bias=\"none\",\n", + " task_type=\"CAUSAL_LM\",\n", + " target_modules=LORA_TARGET_MODULES.split(\",\"),\n", + ")\n", + "\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_pAUU2FR2Gey", + "outputId": "63328c2b-e693-49b1-ce0a-3ca8722f852a" + }, + "execution_count": null, + "outputs": [ { - "cell_type": "code", - "source": [ - "def get_code_completion(prefix, suffix):\n", - " text = prompt = f\"\"\"{prefix}{suffix}\"\"\"\n", - " model.eval()\n", - " outputs = model.generate(\n", - " input_ids=tokenizer(text, return_tensors=\"pt\").input_ids.cuda(),\n", - " max_new_tokens=128,\n", - " temperature=0.2,\n", - " top_k=50,\n", - " top_p=0.95,\n", - " do_sample=True,\n", - " repetition_penalty=1.0,\n", - " )\n", - " return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]" - ], - "metadata": { - "id": "RoTGpNbjDeWI" - }, - "execution_count": null, - "outputs": [] + "output_type": "stream", + "name": "stdout", + "text": [ + "trainable params: 5,554,176 || all params: 1,142,761,472 || trainable%: 0.4860310866343243\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As you can see, by applying LoRA technique we will now need to train less than 1% of the parameters." + ], + "metadata": { + "id": "tHe7AElXzXVV" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Train the model" + ], + "metadata": { + "id": "T_CqVydc40IM" + } + }, + { + "cell_type": "markdown", + "source": [ + "Now that we have prepared the data, and optimized the model, we are ready to bring everything together to start the training.\n", + "\n", + "To instantiate a `Trainer`, you need to define the training configuration. The most important is the `TrainingArguments`, which is a class that contains all the attributes to configure the training.\n", + "\n", + "These are similar to any other kind of model training you may run, so we won't go into detail here." + ], + "metadata": { + "id": "Q_iN2khjrbD3" + } + }, + { + "cell_type": "code", + "source": [ + "train_data.start_iteration = 0\n", + "\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=f\"Your_HF_username/{OUTPUT_DIR}\",\n", + " dataloader_drop_last=True,\n", + " evaluation_strategy=\"steps\",\n", + " save_strategy=\"steps\",\n", + " max_steps=MAX_STEPS,\n", + " eval_steps=EVAL_FREQ,\n", + " save_steps=SAVE_FREQ,\n", + " logging_steps=LOG_FREQ,\n", + " per_device_train_batch_size=BATCH_SIZE,\n", + " per_device_eval_batch_size=BATCH_SIZE,\n", + " learning_rate=LR,\n", + " lr_scheduler_type=LR_SCHEDULER_TYPE,\n", + " warmup_steps=NUM_WARMUP_STEPS,\n", + " gradient_accumulation_steps=GR_ACC_STEPS,\n", + " gradient_checkpointing=True,\n", + " fp16=FP16,\n", + " bf16=BF16,\n", + " weight_decay=WEIGHT_DECAY,\n", + " push_to_hub=True,\n", + " include_tokens_per_second=True,\n", + ")\n" + ], + "metadata": { + "id": "65QHS8l1tKQe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "As a final step, instantiate the `Trainer` and call the `train` method. " + ], + "metadata": { + "id": "kB_fLRex09ut" + } + }, + { + "cell_type": "code", + "source": [ + "trainer = Trainer(\n", + " model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n", + ")\n", + "\n", + "print(\"Training...\")\n", + "trainer.train()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "id": "rS3nVwhUC69O", + "outputId": "61a5bdb2-b7d0-4aed-8290-4bf20c2ccd38" + }, + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "Now all we need to do to get code completion is call the `get_code_complete` function and pass the first few lines that we want to be completed as a prefix, and an empty string as a suffix." - ], - "metadata": { - "id": "0kMJiGDfDrBf" - } + "metadata": { + "tags": null + }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Training...\n" + ] }, { - "cell_type": "code", - "source": [ - "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n", - "from transformers import AutoModelForCausalLM\n", - "peft_config = LoraConfig(\n", - "\"\"\"\n", - "suffix =\"\"\"\"\"\"\n", - "\n", - "print(get_code_completion(prefix, suffix))" + "output_type": "display_data", + "data": { + "text/plain": [ + "" ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nXlco2_-YcvM", - "outputId": "41c411ad-b7dc-4277-f975-c173888234bb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "from peft import LoraConfig, TaskType, get_peft_model\n", - "from transformers import AutoModelForCausalLM\n", - "peft_config = LoraConfig(\n", - " task_type=TaskType.CAUSAL_LM,\n", - " r=8,\n", - " lora_alpha=32,\n", - " target_modules=[\"q_proj\", \"v_proj\"],\n", - " lora_dropout=0.1,\n", - " bias=\"none\",\n", - " modules_to_save=[\"q_proj\", \"v_proj\"],\n", - " inference_mode=False,\n", - ")\n", - "model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n", - "model = get_peft_model(model, peft_config)\n", - "model.print_trainable_parameters()\n" - ] - } + "text/html": [ + "\n", + "

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" ] + }, + "metadata": {} }, { - "cell_type": "markdown", - "source": [ - "As someone who has just used the PEFT library earlier in this notebook, you can see that the generated result for creating a `LoraConfig` is rather good!\n", - "\n", - "If you go back to the cell where we instantiate the model for inference, and comment out the lines where we merge the fine-tuned weights, you can see what the original model would've generated for the exact same prefix:" - ], - "metadata": { - "id": "Ql2563kGlnmu" - } - }, - { - "cell_type": "code", - "source": [ - "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n", - "from transformers import AutoModelForCausalLM\n", - "peft_config = LoraConfig(\n", - "\"\"\"\n", - "suffix =\"\"\"\"\"\"\n", - "\n", - "print(get_code_completion(prefix, suffix))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "29xxp1eHTgJ9", - "outputId": "c6d597a2-01da-4d25-a32f-3a551212c5b4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "from peft import LoraConfig, TaskType, get_peft_model\n", - "from transformers import AutoModelForCausalLM\n", - "peft_config = LoraConfig(\n", - " model_name_or_path=\"facebook/wav2vec2-base-960h\",\n", - " num_labels=1,\n", - " num_features=1,\n", - " num_hidden_layers=1,\n", - " num_attention_heads=1,\n", - " num_hidden_layers_per_attention_head=1,\n", - " num_attention_heads_per_hidden_layer=1,\n", - " hidden_size=1024,\n", - " hidden_dropout_prob=0.1,\n", - " hidden_act=\"gelu\",\n", - " hidden_act_dropout_prob=0.1,\n", - " hidden\n" - ] - } + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=2000, training_loss=4.885598585128784, metrics={'train_runtime': 15380.3075, 'train_samples_per_second': 2.081, 'train_steps_per_second': 0.13, 'train_tokens_per_second': 4261.033, 'total_flos': 4.0317260660736e+17, 'train_loss': 4.885598585128784, 'epoch': 1.0})" ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Finally, you can push the fine-tuned model to your Hub repository to share with your team." + ], + "metadata": { + "id": "aAERlCnt1PEW" + } + }, + { + "cell_type": "code", + "source": [ + "trainer.push_to_hub()" + ], + "metadata": { + "id": "1h7_AUTTDwE1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Inference\n", + "\n", + "Once the model is uploaded to Hub, we can use it for inference. To do so we first initialize the original base model and its tokenizer. Next, we need to merge the fine-duned weights with the base model." + ], + "metadata": { + "id": "KBVH7uFOM_UF" + } + }, + { + "cell_type": "code", + "source": [ + "from peft import PeftModel\n", + "import torch\n", + "\n", + "# load the original model first\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n", + "base_model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL,\n", + " quantization_config=None,\n", + " device_map=None,\n", + " trust_remote_code=True,\n", + " torch_dtype=torch.bfloat16,\n", + ").cuda()\n", + "\n", + "# merge fine-tuned weights with the base model\n", + "peft_model_id = f\"Your_HF_username/{OUTPUT_DIR}\"\n", + "model = PeftModel.from_pretrained(base_model, peft_model_id)\n", + "model.merge_and_unload()" + ], + "metadata": { + "id": "jtL37piINBFe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now we can use the merged model for inference. For convenience, we'll define a `get_code_completion` - feel free to experiment with text generation parameters!" + ], + "metadata": { + "id": "3USQ2suvDi9M" + } + }, + { + "cell_type": "code", + "source": [ + "def get_code_completion(prefix, suffix):\n", + " text = prompt = f\"\"\"{prefix}{suffix}\"\"\"\n", + " model.eval()\n", + " outputs = model.generate(\n", + " input_ids=tokenizer(text, return_tensors=\"pt\").input_ids.cuda(),\n", + " max_new_tokens=128,\n", + " temperature=0.2,\n", + " top_k=50,\n", + " top_p=0.95,\n", + " do_sample=True,\n", + " repetition_penalty=1.0,\n", + " )\n", + " return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]" + ], + "metadata": { + "id": "RoTGpNbjDeWI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now all we need to do to get code completion is call the `get_code_complete` function and pass the first few lines that we want to be completed as a prefix, and an empty string as a suffix." + ], + "metadata": { + "id": "0kMJiGDfDrBf" + } + }, + { + "cell_type": "code", + "source": [ + "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n", + "from transformers import AutoModelForCausalLM\n", + "peft_config = LoraConfig(\n", + "\"\"\"\n", + "suffix =\"\"\"\"\"\"\n", + "\n", + "print(get_code_completion(prefix, suffix))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "nXlco2_-YcvM", + "outputId": "41c411ad-b7dc-4277-f975-c173888234bb" + }, + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "While it is Python syntax, you can see that the original model has no understanding of what a `LoraConfig` should be doing." - ], - "metadata": { - "id": "Pwy2ZC7U8Ema" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "from peft import LoraConfig, TaskType, get_peft_model\n", + "from transformers import AutoModelForCausalLM\n", + "peft_config = LoraConfig(\n", + " task_type=TaskType.CAUSAL_LM,\n", + " r=8,\n", + " lora_alpha=32,\n", + " target_modules=[\"q_proj\", \"v_proj\"],\n", + " lora_dropout=0.1,\n", + " bias=\"none\",\n", + " modules_to_save=[\"q_proj\", \"v_proj\"],\n", + " inference_mode=False,\n", + ")\n", + "model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n", + "model = get_peft_model(model, peft_config)\n", + "model.print_trainable_parameters()\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As someone who has just used the PEFT library earlier in this notebook, you can see that the generated result for creating a `LoraConfig` is rather good!\n", + "\n", + "If you go back to the cell where we instantiate the model for inference, and comment out the lines where we merge the fine-tuned weights, you can see what the original model would've generated for the exact same prefix:" + ], + "metadata": { + "id": "Ql2563kGlnmu" + } + }, + { + "cell_type": "code", + "source": [ + "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n", + "from transformers import AutoModelForCausalLM\n", + "peft_config = LoraConfig(\n", + "\"\"\"\n", + "suffix =\"\"\"\"\"\"\n", + "\n", + "print(get_code_completion(prefix, suffix))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "29xxp1eHTgJ9", + "outputId": "c6d597a2-01da-4d25-a32f-3a551212c5b4" + }, + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "To learn how this kind of fine-tuning compares to full fine-tuning, and how to use a model like this as your copilot in VS Code via Inference Endpoints, or locally, check out the [\"Personal Copilot: Train Your Own Coding Assistant\" blog post](https://huggingface.co/blog/personal-copilot). This notebook complements the original blog post.\n" - ], - "metadata": { - "id": "CATYE8pp2drQ" - } + "output_type": "stream", + "name": "stdout", + "text": [ + "from peft import LoraConfig, TaskType, get_peft_model\n", + "from transformers import AutoModelForCausalLM\n", + "peft_config = LoraConfig(\n", + " model_name_or_path=\"facebook/wav2vec2-base-960h\",\n", + " num_labels=1,\n", + " num_features=1,\n", + " num_hidden_layers=1,\n", + " num_attention_heads=1,\n", + " num_hidden_layers_per_attention_head=1,\n", + " num_attention_heads_per_hidden_layer=1,\n", + " hidden_size=1024,\n", + " hidden_dropout_prob=0.1,\n", + " hidden_act=\"gelu\",\n", + " hidden_act_dropout_prob=0.1,\n", + " hidden\n" + ] } - ] -} \ No newline at end of file + ] + }, + { + "cell_type": "markdown", + "source": [ + "While it is Python syntax, you can see that the original model has no understanding of what a `LoraConfig` should be doing." + ], + "metadata": { + "id": "Pwy2ZC7U8Ema" + } + }, + { + "cell_type": "markdown", + "source": [ + "To learn how this kind of fine-tuning compares to full fine-tuning, and how to use a model like this as your copilot in VS Code via Inference Endpoints, or locally, check out the [\"Personal Copilot: Train Your Own Coding Assistant\" blog post](https://huggingface.co/blog/personal-copilot). This notebook complements the original blog post.\n" + ], + "metadata": { + "id": "CATYE8pp2drQ" + } + } + ] +} diff --git a/notebooks/en/rag_zephyr_langchain.ipynb b/notebooks/en/rag_zephyr_langchain.ipynb index f07c9ea7..0b29c04b 100644 --- a/notebooks/en/rag_zephyr_langchain.ipynb +++ b/notebooks/en/rag_zephyr_langchain.ipynb @@ -1,511 +1,513 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain\n", - "\n", - "This notebook demonstrates how you can quickly build a RAG (Retrieval Augmented Generation) for a project's GitHub issues using [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model, and LangChain.\n", - "\n", - "\n", - "**What is RAG?**\n", - "\n", - "RAG is a popular approach to address the issue of a powerful LLM not being aware of specific content due to said content not being in its training data, or hallucinating even when it has seen it before. Such specific content may be proprietary, sensitive, or, as in this example, recent and updated often.\n", - "\n", - "If your data is static and doesn't change regularly, you may consider fine-tuning a large model. In many cases, however, fine-tuning can be costly, and, when done repeatedly (e.g. to address data drift), leads to \"model shift\". This is when the model's behavior changes in ways that are not desirable.\n", - "\n", - "**RAG (Retrieval Augmented Generation)** does not require model fine-tuning. Instead, RAG works by providing an LLM with additional context that is retrieved from relevant data so that it can generate a better-informed response.\n", - "\n", - "Here's a quick illustration:\n", - "\n", - "![RAG diagram](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/rag-diagram.png)\n", - "\n", - "* The external data is converted into embedding vectors with a separate embeddings model, and the vectors are kept in a database. Embeddings models are typically small, so updating the embedding vectors on a regular basis is faster, cheaper, and easier than fine-tuning a model.\n", - "\n", - "* At the same time, the fact that fine-tuning is not required gives you the freedom to swap your LLM for a more powerful one when it becomes available, or switch to a smaller distilled version, should you need faster inference.\n", - "\n", - "Let's illustrate building a RAG using an open-source LLM, embeddings model, and LandChain.\n", - "\n", - "First, install the required dependencies:" - ], - "metadata": { - "id": "Kih21u1tyr-I" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lC9frDOlyi38" - }, - "outputs": [], - "source": [ - "!pip install -q torch transformers accelerate bitsandbytes transformers sentence-transformers faiss-gpu" - ] - }, - { - "cell_type": "code", - "source": [ - "# If running in Google Colab, you may need to run this cell to make sure you're using UTF-8 locale to install LangChain\n", - "import locale\n", - "locale.getpreferredencoding = lambda: \"UTF-8\"" - ], - "metadata": { - "id": "-aYENQwZ-p_c" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!pip install -q langchain" - ], - "metadata": { - "id": "W5HhMZ2c-NfU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Prepare the data\n" - ], - "metadata": { - "id": "R8po01vMWzXL" - } - }, - { - "cell_type": "markdown", - "source": [ - "In this example, we'll load all of the issues (both open and closed) from [PEFT library's repo](https://github.com/huggingface/peft).\n", - "\n", - "First, you need to acquire a [GitHub personal access token](https://github.com/settings/tokens?type=beta) to access the GitHub API." - ], - "metadata": { - "id": "3cCmQywC04x6" - } - }, - { - "cell_type": "code", - "source": [ - "from getpass import getpass\n", - "ACCESS_TOKEN = getpass(\"YOUR_GITHUB_PERSONAL_TOKEN\")" - ], - "metadata": { - "id": "8MoD7NbsNjlM" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Next, we'll load all of the issues in the [huggingface/peft](https://github.com/huggingface/peft) repo:\n", - "- By default, pull requests are considered issues as well, here we chose to exclude them from data with by setting `include_prs=False`\n", - "- Setting `state = \"all\"` means we will load both open and closed issues." - ], - "metadata": { - "id": "fccecm3a10N6" - } - }, - { - "cell_type": "code", - "source": [ - "from langchain.document_loaders import GitHubIssuesLoader\n", - "\n", - "loader = GitHubIssuesLoader(\n", - " repo=\"huggingface/peft\",\n", - " access_token=ACCESS_TOKEN,\n", - " include_prs=False,\n", - " state=\"all\"\n", - ")\n", - "\n", - "docs = loader.load()" - ], - "metadata": { - "id": "8EKMit4WNDY8" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The content of individual GitHub issues may be longer than what an embedding model can take as input. If we want to embed all of the available content, we need to chunk the documents into appropriately sized pieces.\n", - "\n", - "The most common and straightforward approach to chunking is to define a fixed size of chunks and whether there should be any overlap between them. Keeping some overlap between chunks allows us to preserve some semantic context between the chunks.\n", - "\n", - "Other approaches are typically more involved and take into account the documents' structure and context. For example, one may want to split a document based on sentences or paragraphs, or create chunks based on the\n", - "\n", - "The fixed-size chunking, however, works well for most common cases, so that is what we'll do here." - ], - "metadata": { - "id": "CChTrY-k2qO5" - } - }, - { - "cell_type": "code", - "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", - "\n", - "splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n", - "\n", - "chunked_docs = splitter.split_documents(docs)" - ], - "metadata": { - "id": "OmsXOf59Pmm-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Create the embeddings + retriever" - ], - "metadata": { - "id": "DAt_zPVlXOn7" - } - }, - { - "cell_type": "markdown", - "source": [ - "Now that the docs are all of the appropriate size, we can create a database with their embeddings.\n", - "\n", - "To create document chunk embeddings we'll use the `HuggingFaceEmbeddings` and the [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model. There are many other embeddings models available on the Hub, and you can keep an eye on the best performing ones by checking the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).\n", - "\n", - "\n", - "To create the vector database, we'll use `FAISS`, a library developed by Facebook AI. This library offers efficient similarity search and clustering of dense vectors, which is what we need here. FAISS is currently one of the most used libraries for NN search in massive datasets.\n", - "\n", - "We'll access both the embeddings model and FAISS via LangChain API." - ], - "metadata": { - "id": "-mvat6JQl4yp" - } - }, - { - "cell_type": "code", - "source": [ - "from langchain.vectorstores import FAISS\n", - "from langchain.embeddings import HuggingFaceEmbeddings\n", - "\n", - "db = FAISS.from_documents(chunked_docs,\n", - " HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))" - ], - "metadata": { - "id": "ixmCdRzBQ5gu" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We need a way to return(retrieve) the documents given an unstructured query. For that, we'll use the `as_retriever` method using the `db` as a backbone:\n", - "- `search_type=\"similarity\"` means we want to perform similarity search between the query and documents\n", - "- `search_kwargs={'k': 4}` instructs the retriever to return top 4 results.\n" - ], - "metadata": { - "id": "2iCgEPi0nnN6" - } - }, - { - "cell_type": "code", - "source": [ - "retriever = db.as_retriever(\n", - " search_type=\"similarity\",\n", - " search_kwargs={'k': 4}\n", - ")" - ], - "metadata": { - "id": "mBTreCQ9noHK" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "The vector database and retriever are now set up, next we need to set up the next piece of the chain - the model." - ], - "metadata": { - "id": "WgEhlISJpTgj" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Load quantized model" - ], - "metadata": { - "id": "tzQxx0HkXVFU" - } - }, - { - "cell_type": "markdown", - "source": [ - "For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.\n", - "\n", - "With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n", - "\n", - "To make inference faster, we will load the quantized version of the model:" - ], - "metadata": { - "id": "9jy1cC65p_GD" - } - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", - "\n", - "model_name = 'HuggingFaceH4/zephyr-7b-beta'\n", - "\n", - "bnb_config = BitsAndBytesConfig(\n", - " load_in_4bit=True,\n", - " bnb_4bit_use_double_quant=True,\n", - " bnb_4bit_quant_type=\"nf4\",\n", - " bnb_4bit_compute_dtype=torch.bfloat16\n", - ")\n", - "\n", - "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)\n", - "tokenizer = AutoTokenizer.from_pretrained(model_name)" - ], - "metadata": { - "id": "L-ggaa763VRo" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Setup the LLM chain" - ], - "metadata": { - "id": "hVNRJALyXYHG" - } - }, - { - "cell_type": "markdown", - "source": [ - "Finally, we have all the pieces we need to set up the LLM chain.\n", - "\n", - "First, create a text_generation pipeline using the loaded model and its tokenizer.\n", - "\n", - "Next, create a prompt template - this should follow the format of the model, so if you substitute the model checkpoint, make sure to use the appropriate formatting." - ], - "metadata": { - "id": "RUUNneJ1smhl" - } - }, - { - "cell_type": "code", - "source": [ - "from langchain.llms import HuggingFacePipeline\n", - "from langchain.prompts import PromptTemplate\n", - "from transformers import pipeline\n", - "from langchain.chains import LLMChain\n", - "\n", - "text_generation_pipeline = pipeline(\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " task=\"text-generation\",\n", - " temperature=0.2,\n", - " repetition_penalty=1.1,\n", - " return_full_text=True,\n", - " max_new_tokens=400,\n", - ")\n", - "\n", - "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n", - "\n", - "prompt_template = \"\"\"\n", - "<|system|>\n", - "Answer the question based on your knowledge. Use the following context to help:\n", - "\n", - "{context}\n", - "\n", - "\n", - "<|user|>\n", - "{question}\n", - "\n", - "<|assistant|>\n", - "\n", - " \"\"\"\n", - "\n", - "prompt = PromptTemplate(\n", - " input_variables=[\"context\", \"question\"],\n", - " template=prompt_template,\n", - ")\n", - "\n", - "llm_chain = LLMChain(llm=llm, prompt=prompt)" - ], - "metadata": { - "id": "cR0k1cRWz8Pm" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Note: _You can also use `tokenizer.apply_chat_template` to convert a list of messages (as dicts: `{'role': 'user', 'content': '(...)'}`) into a string with the appropriate chat format._\n", - "\n", - "\n", - "Finally, we need to combine the `llm_chain` with the retriever to create the RAG:" - ], - "metadata": { - "id": "l19UKq5HXfSp" - } - }, - { - "cell_type": "code", - "source": [ - "from langchain.schema.runnable import RunnablePassthrough\n", - "\n", - "retriever = db.as_retriever()\n", - "\n", - "rag_chain = (\n", - " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", - " | llm_chain\n", - ")\n" - ], - "metadata": { - "id": "_rI3YNp9Xl4s" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Compare the results\n", - "\n", - "Let's see the difference RAG makes in generating answers to the library-specific questions." - ], - "metadata": { - "id": "UsCOhfDDXpaS" - } - }, - { - "cell_type": "code", - "source": [ - "question = \"How do you combine multiple adapters?\"" - ], - "metadata": { - "id": "W7F07fQLXusU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "First, let's see what kind of answer we can get with just the model itself, no context added:" - ], - "metadata": { - "id": "KC0rJYU1x1ir" - } - }, - { - "cell_type": "code", - "source": [ - "llm_chain.invoke({\"context\":\"\", \"question\": question})['text']\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 216 - }, - "id": "GYh-HG1l0De5", - "outputId": "549e0bdd-b186-4d16-e7fa-90b3865d6f83" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\" To combine multiple adapters, you need to ensure that they are compatible with each other and the devices you want to connect. Here's how you can do it:\\n\\n1. Identify the adapters you need: Determine which adapters you require to connect the devices you want to use. For example, if you want to connect a USB-C device to an HDMI monitor, you may need a USB-C to HDMI adapter and a USB-C to USB-A adapter (if your computer doesn't have a USB-C port).\\n\\n2. Connect the first adapter: Plug in the first adapter into the device you want to connect. For instance, if you're connecting a laptop to a monitor, plug the USB-C to HDMI adapter into your laptop's USB-C port.\\n\\n3. Connect the second adapter: If necessary, connect the second adapter to the first one. In our example, you would connect the USB-C to USB-A adapter to the USB-C port on the USB-C to HDMI adapter.\\n\\n4. Connect the final device: Finally, connect the device you want to use to the second adapter. In our case, you would connect the HDMI cable from the monitor to the HDMI port on the USB-C to HDMI adapter.\\n\\n5. Test the connection: Turn on both devices and check whether everything is working correctly. You should now be able to use the connected device as normal.\\n\\nRemember to always check compatibility before purchasing any adapters to ensure they will work together and with your specific devices.\"" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As you can see, the model interpreted the question as one about physical computer adapters, while in the context of PEFT, \"adapters\" refer to LoRA adapters.\n", - "Let's see if adding context from GitHub issues helps the model give a more relevant answer:" + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain\n", + "\n", + "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n", + "\n", + "This notebook demonstrates how you can quickly build a RAG (Retrieval Augmented Generation) for a project's GitHub issues using [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model, and LangChain.\n", + "\n", + "\n", + "**What is RAG?**\n", + "\n", + "RAG is a popular approach to address the issue of a powerful LLM not being aware of specific content due to said content not being in its training data, or hallucinating even when it has seen it before. Such specific content may be proprietary, sensitive, or, as in this example, recent and updated often.\n", + "\n", + "If your data is static and doesn't change regularly, you may consider fine-tuning a large model. In many cases, however, fine-tuning can be costly, and, when done repeatedly (e.g. to address data drift), leads to \"model shift\". This is when the model's behavior changes in ways that are not desirable.\n", + "\n", + "**RAG (Retrieval Augmented Generation)** does not require model fine-tuning. Instead, RAG works by providing an LLM with additional context that is retrieved from relevant data so that it can generate a better-informed response.\n", + "\n", + "Here's a quick illustration:\n", + "\n", + "![RAG diagram](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/rag-diagram.png)\n", + "\n", + "* The external data is converted into embedding vectors with a separate embeddings model, and the vectors are kept in a database. Embeddings models are typically small, so updating the embedding vectors on a regular basis is faster, cheaper, and easier than fine-tuning a model.\n", + "\n", + "* At the same time, the fact that fine-tuning is not required gives you the freedom to swap your LLM for a more powerful one when it becomes available, or switch to a smaller distilled version, should you need faster inference.\n", + "\n", + "Let's illustrate building a RAG using an open-source LLM, embeddings model, and LandChain.\n", + "\n", + "First, install the required dependencies:" + ], + "metadata": { + "id": "Kih21u1tyr-I" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lC9frDOlyi38" + }, + "outputs": [], + "source": [ + "!pip install -q torch transformers accelerate bitsandbytes transformers sentence-transformers faiss-gpu" + ] + }, + { + "cell_type": "code", + "source": [ + "# If running in Google Colab, you may need to run this cell to make sure you're using UTF-8 locale to install LangChain\n", + "import locale\n", + "locale.getpreferredencoding = lambda: \"UTF-8\"" + ], + "metadata": { + "id": "-aYENQwZ-p_c" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!pip install -q langchain" + ], + "metadata": { + "id": "W5HhMZ2c-NfU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Prepare the data\n" + ], + "metadata": { + "id": "R8po01vMWzXL" + } + }, + { + "cell_type": "markdown", + "source": [ + "In this example, we'll load all of the issues (both open and closed) from [PEFT library's repo](https://github.com/huggingface/peft).\n", + "\n", + "First, you need to acquire a [GitHub personal access token](https://github.com/settings/tokens?type=beta) to access the GitHub API." + ], + "metadata": { + "id": "3cCmQywC04x6" + } + }, + { + "cell_type": "code", + "source": [ + "from getpass import getpass\n", + "ACCESS_TOKEN = getpass(\"YOUR_GITHUB_PERSONAL_TOKEN\")" + ], + "metadata": { + "id": "8MoD7NbsNjlM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we'll load all of the issues in the [huggingface/peft](https://github.com/huggingface/peft) repo:\n", + "- By default, pull requests are considered issues as well, here we chose to exclude them from data with by setting `include_prs=False`\n", + "- Setting `state = \"all\"` means we will load both open and closed issues." + ], + "metadata": { + "id": "fccecm3a10N6" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.document_loaders import GitHubIssuesLoader\n", + "\n", + "loader = GitHubIssuesLoader(\n", + " repo=\"huggingface/peft\",\n", + " access_token=ACCESS_TOKEN,\n", + " include_prs=False,\n", + " state=\"all\"\n", + ")\n", + "\n", + "docs = loader.load()" + ], + "metadata": { + "id": "8EKMit4WNDY8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The content of individual GitHub issues may be longer than what an embedding model can take as input. If we want to embed all of the available content, we need to chunk the documents into appropriately sized pieces.\n", + "\n", + "The most common and straightforward approach to chunking is to define a fixed size of chunks and whether there should be any overlap between them. Keeping some overlap between chunks allows us to preserve some semantic context between the chunks.\n", + "\n", + "Other approaches are typically more involved and take into account the documents' structure and context. For example, one may want to split a document based on sentences or paragraphs, or create chunks based on the\n", + "\n", + "The fixed-size chunking, however, works well for most common cases, so that is what we'll do here." + ], + "metadata": { + "id": "CChTrY-k2qO5" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.text_splitter import CharacterTextSplitter\n", + "\n", + "splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=30)\n", + "\n", + "chunked_docs = splitter.split_documents(docs)" + ], + "metadata": { + "id": "OmsXOf59Pmm-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Create the embeddings + retriever" + ], + "metadata": { + "id": "DAt_zPVlXOn7" + } + }, + { + "cell_type": "markdown", + "source": [ + "Now that the docs are all of the appropriate size, we can create a database with their embeddings.\n", + "\n", + "To create document chunk embeddings we'll use the `HuggingFaceEmbeddings` and the [`BAAI/bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5) embeddings model. There are many other embeddings models available on the Hub, and you can keep an eye on the best performing ones by checking the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).\n", + "\n", + "\n", + "To create the vector database, we'll use `FAISS`, a library developed by Facebook AI. This library offers efficient similarity search and clustering of dense vectors, which is what we need here. FAISS is currently one of the most used libraries for NN search in massive datasets.\n", + "\n", + "We'll access both the embeddings model and FAISS via LangChain API." + ], + "metadata": { + "id": "-mvat6JQl4yp" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.vectorstores import FAISS\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "\n", + "db = FAISS.from_documents(chunked_docs,\n", + " HuggingFaceEmbeddings(model_name='BAAI/bge-base-en-v1.5'))" + ], + "metadata": { + "id": "ixmCdRzBQ5gu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We need a way to return(retrieve) the documents given an unstructured query. For that, we'll use the `as_retriever` method using the `db` as a backbone:\n", + "- `search_type=\"similarity\"` means we want to perform similarity search between the query and documents\n", + "- `search_kwargs={'k': 4}` instructs the retriever to return top 4 results.\n" + ], + "metadata": { + "id": "2iCgEPi0nnN6" + } + }, + { + "cell_type": "code", + "source": [ + "retriever = db.as_retriever(\n", + " search_type=\"similarity\",\n", + " search_kwargs={'k': 4}\n", + ")" + ], + "metadata": { + "id": "mBTreCQ9noHK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The vector database and retriever are now set up, next we need to set up the next piece of the chain - the model." + ], + "metadata": { + "id": "WgEhlISJpTgj" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Load quantized model" + ], + "metadata": { + "id": "tzQxx0HkXVFU" + } + }, + { + "cell_type": "markdown", + "source": [ + "For this example, we chose [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a small but powerful model.\n", + "\n", + "With many models being released every week, you may want to substitute this model to the latest and greatest. The best way to keep track of open source LLMs is to check the [Open-source LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n", + "\n", + "To make inference faster, we will load the quantized version of the model:" + ], + "metadata": { + "id": "9jy1cC65p_GD" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", + "\n", + "model_name = 'HuggingFaceH4/zephyr-7b-beta'\n", + "\n", + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.bfloat16\n", + ")\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)" + ], + "metadata": { + "id": "L-ggaa763VRo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Setup the LLM chain" + ], + "metadata": { + "id": "hVNRJALyXYHG" + } + }, + { + "cell_type": "markdown", + "source": [ + "Finally, we have all the pieces we need to set up the LLM chain.\n", + "\n", + "First, create a text_generation pipeline using the loaded model and its tokenizer.\n", + "\n", + "Next, create a prompt template - this should follow the format of the model, so if you substitute the model checkpoint, make sure to use the appropriate formatting." + ], + "metadata": { + "id": "RUUNneJ1smhl" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.llms import HuggingFacePipeline\n", + "from langchain.prompts import PromptTemplate\n", + "from transformers import pipeline\n", + "from langchain.chains import LLMChain\n", + "\n", + "text_generation_pipeline = pipeline(\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " task=\"text-generation\",\n", + " temperature=0.2,\n", + " repetition_penalty=1.1,\n", + " return_full_text=True,\n", + " max_new_tokens=400,\n", + ")\n", + "\n", + "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n", + "\n", + "prompt_template = \"\"\"\n", + "<|system|>\n", + "Answer the question based on your knowledge. Use the following context to help:\n", + "\n", + "{context}\n", + "\n", + "\n", + "<|user|>\n", + "{question}\n", + "\n", + "<|assistant|>\n", + "\n", + " \"\"\"\n", + "\n", + "prompt = PromptTemplate(\n", + " input_variables=[\"context\", \"question\"],\n", + " template=prompt_template,\n", + ")\n", + "\n", + "llm_chain = LLMChain(llm=llm, prompt=prompt)" + ], + "metadata": { + "id": "cR0k1cRWz8Pm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Note: _You can also use `tokenizer.apply_chat_template` to convert a list of messages (as dicts: `{'role': 'user', 'content': '(...)'}`) into a string with the appropriate chat format._\n", + "\n", + "\n", + "Finally, we need to combine the `llm_chain` with the retriever to create the RAG:" + ], + "metadata": { + "id": "l19UKq5HXfSp" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.schema.runnable import RunnablePassthrough\n", + "\n", + "retriever = db.as_retriever()\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | llm_chain\n", + ")\n" + ], + "metadata": { + "id": "_rI3YNp9Xl4s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Compare the results\n", + "\n", + "Let's see the difference RAG makes in generating answers to the library-specific questions." + ], + "metadata": { + "id": "UsCOhfDDXpaS" + } + }, + { + "cell_type": "code", + "source": [ + "question = \"How do you combine multiple adapters?\"" + ], + "metadata": { + "id": "W7F07fQLXusU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "First, let's see what kind of answer we can get with just the model itself, no context added:" + ], + "metadata": { + "id": "KC0rJYU1x1ir" + } + }, + { + "cell_type": "code", + "source": [ + "llm_chain.invoke({\"context\":\"\", \"question\": question})['text']\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 216 + }, + "id": "GYh-HG1l0De5", + "outputId": "549e0bdd-b186-4d16-e7fa-90b3865d6f83" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\" To combine multiple adapters, you need to ensure that they are compatible with each other and the devices you want to connect. Here's how you can do it:\\n\\n1. Identify the adapters you need: Determine which adapters you require to connect the devices you want to use. For example, if you want to connect a USB-C device to an HDMI monitor, you may need a USB-C to HDMI adapter and a USB-C to USB-A adapter (if your computer doesn't have a USB-C port).\\n\\n2. Connect the first adapter: Plug in the first adapter into the device you want to connect. For instance, if you're connecting a laptop to a monitor, plug the USB-C to HDMI adapter into your laptop's USB-C port.\\n\\n3. Connect the second adapter: If necessary, connect the second adapter to the first one. In our example, you would connect the USB-C to USB-A adapter to the USB-C port on the USB-C to HDMI adapter.\\n\\n4. Connect the final device: Finally, connect the device you want to use to the second adapter. In our case, you would connect the HDMI cable from the monitor to the HDMI port on the USB-C to HDMI adapter.\\n\\n5. Test the connection: Turn on both devices and check whether everything is working correctly. You should now be able to use the connected device as normal.\\n\\nRemember to always check compatibility before purchasing any adapters to ensure they will work together and with your specific devices.\"" ], - "metadata": { - "id": "i-TIWr3wx9w8" + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" } - }, - { - "cell_type": "code", - "source": [ - "rag_chain.invoke(question)['text']" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 198 - }, - "id": "FZpNA3o10H10", - "outputId": "9ddc0eef-0503-445d-8f70-26be3ec19de6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "\" Based on the provided context, it seems like combining multiple adapters is still being explored and discussed within the community. Here are some insights from the issues raised:\\n\\n 1. In issue #1040, AlbertoZerbinati asks about merging multiple adapters, suggesting that it might be useful to add multiple distinct behaviors to a base model by merging multiple Lora adapters. However, no clear recommendation was given.\\n\\n 2. In issue #1025, Ali1858 encountered a ValueError while trying to load multiple adapters simultaneously for inference. This suggests that currently, loading multiple adapters at once may not be supported or straightforward.\\n\\n 3. In issue #449, TheShy-Dream expressed interest in incorporating multimodal information into an adapter they were creating themselves. It's unclear whether this involves combining multiple adapters or just modifying a single one.\\n\\n Overall, it seems that combining multiple adapters is still an open question, and more exploration and experimentation is needed to determine how best to do it. If you're interested in contributing to this discussion, you might consider joining the conversation in these issues or opening a new one with your own ideas and questions.\"" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As we can see, the added context, really helps the exact same model, provide a much more relevant and informed answer to the library-specific question.\n", - "\n", - "Notably, combining multiple adapters for inference has been added to the library, and one can find this information in the documentation, so for the next iteration of this RAG it may be worth including documentation embeddings." + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As you can see, the model interpreted the question as one about physical computer adapters, while in the context of PEFT, \"adapters\" refer to LoRA adapters.\n", + "Let's see if adding context from GitHub issues helps the model give a more relevant answer:" + ], + "metadata": { + "id": "i-TIWr3wx9w8" + } + }, + { + "cell_type": "code", + "source": [ + "rag_chain.invoke(question)['text']" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 198 + }, + "id": "FZpNA3o10H10", + "outputId": "9ddc0eef-0503-445d-8f70-26be3ec19de6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\" Based on the provided context, it seems like combining multiple adapters is still being explored and discussed within the community. Here are some insights from the issues raised:\\n\\n 1. In issue #1040, AlbertoZerbinati asks about merging multiple adapters, suggesting that it might be useful to add multiple distinct behaviors to a base model by merging multiple Lora adapters. However, no clear recommendation was given.\\n\\n 2. In issue #1025, Ali1858 encountered a ValueError while trying to load multiple adapters simultaneously for inference. This suggests that currently, loading multiple adapters at once may not be supported or straightforward.\\n\\n 3. In issue #449, TheShy-Dream expressed interest in incorporating multimodal information into an adapter they were creating themselves. It's unclear whether this involves combining multiple adapters or just modifying a single one.\\n\\n Overall, it seems that combining multiple adapters is still an open question, and more exploration and experimentation is needed to determine how best to do it. If you're interested in contributing to this discussion, you might consider joining the conversation in these issues or opening a new one with your own ideas and questions.\"" ], - "metadata": { - "id": "hZQedZKSyrwO" + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" } + }, + "metadata": {}, + "execution_count": 14 } - ] -} \ No newline at end of file + ] + }, + { + "cell_type": "markdown", + "source": [ + "As we can see, the added context, really helps the exact same model, provide a much more relevant and informed answer to the library-specific question.\n", + "\n", + "Notably, combining multiple adapters for inference has been added to the library, and one can find this information in the documentation, so for the next iteration of this RAG it may be worth including documentation embeddings." + ], + "metadata": { + "id": "hZQedZKSyrwO" + } + } + ] +}