diff --git a/src/notebooks/scenic_dataset_gen.ipynb b/src/notebooks/scenic_dataset_gen.ipynb new file mode 100644 index 0000000..74452a9 --- /dev/null +++ b/src/notebooks/scenic_dataset_gen.ipynb @@ -0,0 +1,4897 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3pyhxTW3uaA5", + "outputId": "5068844d-b8e1-4170-b31a-65343657c129" + }, + "outputs": [], + "source": [ + "# %%capture\n", + "# !pip install transformers torch datasets\n", + "# !git clone https://github.com/BerkeleyLearnVerify/Scenic.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lg5wwtC1uUfM" + }, + "outputs": [], + "source": [ + "from glob import glob\n", + "from typing import Dict, List\n", + "import datasets\n", + "from torch.utils.data import Dataset\n", + "import torch\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x9BuK3OYukVf" + }, + "outputs": [], + "source": [ + "base_path = \"./Scenic\"\n", + "\n", + "scenic_files = glob(base_path + \"/**/*.scenic\", recursive=True)\n", + "source_files = glob(base_path + \"/**/*.py\", recursive=True)\n", + "doc_files = glob(base_path + \"/**/*.rst\", recursive=True)\n", + "\n", + "model_name = \"mistralai/Mistral-7B-Instruct-v0.1\"\n", + "\n", + "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name, device_map = 'auto')\n", + "model = AutoModelForCausalLM.from_pretrained(model_name, device_map = 'auto', offload_folder=\"offload\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "description_prompt = \"\"\"\n", + "Your task is to imagine a real world driving scenario that could have been sampled from the following Scenic code. \n", + "\n", + "Recall that Scenic is a domain-specific probabilistic programming language for modeling the environments of cyber-physical systems like robots and autonomous cars. A Scenic program defines a distribution over scenes, configurations of physical objects and agents; sampling from this distribution yields concrete scenes which can be simulated to produce training or testing data.\n", + "\n", + "Your description scenario should include relevant details so that one can reconstruct a Scenic program from just your description. No mention of code, scenic, Carla, simulation, constraints, or programs should be part of this description. Assume that the reader of the description has no idea how to read or write programs so they would not understand code references and it should not be overly technical. Assume the reader is someone like a clerk in a law office. The full description should be at most 120 words. Here is an example of a description that is reasonable: \n", + "\"A Cruise autonomous vehicle (“AV”), operating in driverless autonomous mode, was traveling northbound on 2nd Street between Stillman Street and Bryant Street. As the AV was approaching the intersection with Stillman Street, a blue Model 3 Tesla exited out of a parking garage into the AV’s lane of travel. The AV braked and, shortly thereafter, the AV made contact with the Model 3 Tesla, damaging the AV’s front bumper fascia and front passenger side wheel opening molding. The driver of the Model 3 Tesla left the scene without exchanging information. There were no reported injuries. The driver of the Model 3 Tesla later reported damages.\"\n", + " \n", + "This is the Scenic program you should generate a description for:\n", + "{scenic_program}\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t5KnBZnjukXo" + }, + "outputs": [], + "source": [ + "def generate_description_from_scenic(scenic : str, verbose : bool = False) -> str:\n", + " \"\"\"Utility function to generate a description from a scenic file\"\"\"\n", + " # return \"a scenic program\"\n", + " # @TODO: Karim add a prompt which tells the LLM to give back a text description of the scenic\n", + " scenic_query = description_prompt.format(scenic_program=scenic)\n", + " input_ids = tokenizer.encode(scenic_query, return_tensors=\"pt\")\n", + " output = model.generate(input_ids, max_new_tokens=500, do_sample=False, top_k=0, top_p=0.9) # do_sample=False <==> temperature=0.0\n", + " generated = tokenizer.decode(output, skip_special_tokens=True)\n", + " if verbose:\n", + " print(generated)\n", + " return generated\n", + "\n", + "\n", + "def get_files(files : List[str], debug : bool = False) -> List[Dict[str, str]]:\n", + " \"\"\"Utility function to read a list of files and return\n", + " a list of dictionaries with the file path and content\"\"\"\n", + "\n", + " out_files = []\n", + "\n", + " for path in files:\n", + " if debug:\n", + " print(path)\n", + "\n", + " with open(path, 'rb') as f:\n", + " content = f.read()\n", + " content = content.decode('utf-8')\n", + " path = path.split(\"/Scenic/\")[1]\n", + " file_dict = {}\n", + " file_dict['path'] = path\n", + " file_dict['description'] = generate_description_from_scenic(content, verbose=debug)\n", + " file_dict['scenic'] = content\n", + " # let's pretty print the content so that new lines are actually printed\n", + " # and not just \\n\n", + " if debug:\n", + " print(\"--------------------\")\n", + " print(content)\n", + " print(\"--------------------\")\n", + "\n", + " out_files.append(file_dict)\n", + "\n", + " return out_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WkNEsEJFuzEj" + }, + "outputs": [], + "source": [ + "def build_dataset(\n", + " scenic_examples : list,\n", + " source_examples : list | None = None,\n", + " doc_examples : list | None = None,\n", + " train_split : float = 0.8,\n", + " verbose : bool = False,\n", + " ) -> (datasets.Dataset, datasets.Dataset):\n", + " \"\"\"Builds a dataset from the three dictionaries of files\"\"\"\n", + "\n", + " scenic_dataset = {}\n", + " source_dataset = {}\n", + " doc_dataset = {}\n", + " train_dataset = []\n", + " test_dataset = []\n", + "\n", + " if verbose:\n", + " print(\"total number of scenic files: \", len(scenic_examples))\n", + "\n", + " scenic_split_index = int(len(scenic_examples) * train_split)\n", + " scenic_dataset['train'] = scenic_examples[:scenic_split_index]\n", + " scenic_dataset['test'] = scenic_examples[scenic_split_index:]\n", + " train_dataset.extend(scenic_dataset['train'])\n", + " test_dataset.extend(scenic_dataset['test'])\n", + "\n", + " # until we know how to bias the logits to do prediction/learning\n", + " # on the source and doc files, we will not include them in the dataset\n", + " if source_examples is not None:\n", + " source_split_index = int(len(source_examples) * train_split)\n", + " source_dataset['train'] = source_examples[:source_split_index]\n", + " source_dataset['test'] = source_examples[source_split_index:]\n", + " train_dataset.extend(source_dataset['train'])\n", + " test_dataset.extend(source_dataset['test'])\n", + "\n", + " if doc_examples is not None:\n", + " doc_split_index = int(len(doc_examples) * train_split)\n", + " doc_dataset['train'] = doc_examples[:doc_split_index]\n", + " doc_dataset['test'] = doc_examples[doc_split_index:]\n", + " train_dataset.extend(doc_dataset['train'])\n", + " test_dataset.extend(doc_dataset['test'])\n", + "\n", + " if verbose:\n", + " print(\"total number of training examples: \", len(train_dataset))\n", + " print(\"total number of testing examples: \", len(test_dataset))\n", + "\n", + " return train_dataset, test_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R7KjRYHBuXE6" + }, + "outputs": [], + "source": [ + "def generate_dataset_from_scratch(\n", + " tokenizer : PreTrainedTokenizerBase,\n", + " include_source : bool = False,\n", + " include_docs : bool = False,\n", + " verbose : bool = False\n", + " ) -> (datasets.Dataset, datasets.Dataset):\n", + " scenic_examples = get_files(scenic_files, debug=verbose)\n", + " source_examples = get_files(source_files, debug=verbose) if include_source else None\n", + " doc_examples = get_files(doc_files, debug=verbose) if include_docs else None\n", + " train, test = build_dataset(scenic_examples, source_examples, doc_examples)\n", + "\n", + " train_dataset = ScenicFineTuneDataset(train, tokenizer)\n", + " test_dataset = ScenicFineTuneDataset(test, tokenizer)\n", + "\n", + " return train_dataset, test_dataset\n", + "\n", + "\n", + "class ScenicFineTuneDataset(Dataset):\n", + " PROMPT = \"[INST] Generate a scene from the following description:\" + \\\n", + " \"\\n\\n Description: {description}\\n\\n\" + \\\n", + " \"Scenic: [\\INST]{scenic}\"\n", + "\n", + " def __init__(\n", + " self, \n", + " data: List, \n", + " tokenizer : PreTrainedTokenizerBase,\n", + " verbose : bool = False,\n", + " ):\n", + " self.data = data\n", + " self.tokenizer = tokenizer\n", + "\n", + " tokenizer.padding_side = \"right\"\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + " self.input_ids = []\n", + " self.attention_masks = []\n", + " self.labels = []\n", + "\n", + " training_texts = []\n", + " for example in self.data:\n", + " training_text = ScenicFineTuneDataset.PROMPT.format(description=example['description'], scenic=example['scenic']) + \"<|endoftext|>\" # include the end token so model knows when to stop!\n", + " training_texts.append(training_text)\n", + "\n", + " encodings_dict = tokenizer(training_texts, truncation=True, padding=True)\n", + " for i, (example, training_text) in enumerate(zip(self.data, training_texts)):\n", + " if verbose and i % 10 == 0:\n", + " print(\"training text =\", training_text)\n", + " self.input_ids.append(torch.tensor(encodings_dict['input_ids'][i]))\n", + " self.attention_masks.append(torch.tensor(encodings_dict['attention_mask'][i]))\n", + "\n", + " # here is the magic where we label the parts to ignore (like the prompt) with -100 so it's ignored as loss\n", + " prompt_len = len(self.tokenizer.encode(ScenicFineTuneDataset.PROMPT.format(description=example['description'], scenic='').strip()))\n", + " whole_input_len = len(self.tokenizer.encode(training_text))\n", + " max_len_incl_padding = len(encodings_dict['input_ids'][i])\n", + " label = [-100] * prompt_len + encodings_dict['input_ids'][i][prompt_len:whole_input_len] + [-100] * (max_len_incl_padding - whole_input_len)\n", + " self.labels.append(torch.tensor(label))\n", + "\n", + " def __len__(self):\n", + " return len(self.input_ids)\n", + "\n", + " def __getitem__(self, idx):\n", + " return {\n", + " 'input_ids': self.input_ids[idx],\n", + " 'attention_mask': self.attention_masks[idx],\n", + " 'labels': self.labels[idx]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 433, + "referenced_widgets": [ + "ef1b75b24c94461f81a2d7f4cf3a0fc3", + "c3a9b6658d004dfdbeb7f501af4dfb9f", + "670bbbc54133425783dc2ff7d7233573", + "dd5bd20309614fb398ec72b238ebab70", + "1d1e2a5c39ce4ffd8757ac803d528036", + "83f76e98219b41ba8933f93a2983a54c", + "f1d3d07870664bb28132a5553352a8f3", + "689d5bfd8d774376b99cbd6a54a2efc1", + "d4b8bb10dce14d6fb9e68759d5a3e484", + "9b632b3b1171471caa69458108dcf67c", + "b1eba99ebbb840498df40dcfa7bce65f", + "d79462e7a31f4be783b2a3b88080377c", + 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a/src/notebooks/similar_prompts.ipynb b/src/notebooks/similar_prompts.ipynb new file mode 100644 index 0000000..649527c --- /dev/null +++ b/src/notebooks/similar_prompts.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install np sentence_transformers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sentence_transformers import SentenceTransformer\n", + "\n", + "# https://www.sbert.net/ example model - \"all-MiniLM-L6-v2\"\n", + "sentence_encoder = SentenceTransformer('all-MiniLM-L6-v2')\n", + "\n", + "def compute_description_encodings(data):\n", + " vectors = sentence_encoder.encode([d['description'] for d in data], show_progress_bar=False)\n", + " for example, vector in zip(data, vectors):\n", + " # save this embedding as a new key\n", + " example['description_encoding'] = np.array(vector)\n", + "\n", + "\n", + "def select_similar_descriptions(new_description, few_shot_data, num_examples=4):\n", + " # Recall that in few-shot prompting, the best example should be the most recent one.\n", + " \n", + " description_vector = sentence_encoder.encode(new_description, show_progress_bar=False)\n", + " vectors = [d['description_encoding'] for d in few_shot_data]\n", + "\n", + " # TODO: pick the best similarity metric. cosine dist is okay for now\n", + " cos_sim = np.sum(vectors * description_vector, axis=1)\n", + " best_indices = np.argsort(cos_sim)[-num_examples:]\n", + " return [few_shot_data[i] for i in best_indices]" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}