Adding Llama Guard notebooks #400
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,261 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "0980720f-4d3c-464c-97c7-7ee9125239bd", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Running Llama Guard inference\n", | ||
| "\n", | ||
| "This notebook is intented to showcase how to run Llama Guard inference on a sample prompt for testing." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "c4c946e0-4b84-4d51-80f1-be1a43c75b90", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install --upgrade huggingface_hub" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "7b6d0f79-4886-4b86-a7c7-fb42321d8495", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", | ||
| "\n", | ||
| "from llama_recipes.inference.prompt_format_utils import build_prompt, create_conversation, LLAMA_GUARD_CATEGORY\n", | ||
| "from llama_recipes.inference.llama.generation import Llama\n", | ||
| "\n", | ||
| "from typing import List, Optional, Tuple, Dict\n", | ||
| "from enum import Enum\n", | ||
| "\n", | ||
| "import torch\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "eb0c5970-5e3a-4340-913e-61fd5a5e780a", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Defining the main functions\n", | ||
| "\n", | ||
| "Agent type enum to define what type of inference Llama Guard should be doing, either User or Agent.\n", | ||
| "\n", | ||
| "The llm_eval function loads the Llama Guard model from Hugging Face. Then iterates over the prompts and generates the results for each token." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "acd9b498-3457-4df4-b829-828e53d5bf5a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "class AgentType(Enum):\n", | ||
| " AGENT = \"Agent\"\n", | ||
| " USER = \"User\"\n", | ||
| "\n", | ||
| "def llm_eval(prompts, load_in_8bit=True, load_in_4bit = False, logprobs = False) -> Tuple[List[str], Optional[List[List[Tuple[int, float]]]]]:\n", | ||
| "\n", | ||
| " model_id = \"meta-llama/LlamaGuard-7b\"\n", | ||
| " \n", | ||
| " tokenizer = AutoTokenizer.from_pretrained(model_id)\n", | ||
| "\n", | ||
| " torch_dtype = torch.float32\n", | ||
| " if load_in_4bit:\n", | ||
| " torch_dtype = torch.bfloat16\n", | ||
| "\n", | ||
| " bnb_config = BitsAndBytesConfig(\n", | ||
| " load_in_8bit=load_in_8bit,\n", | ||
| " load_in_4bit=load_in_4bit,\n", | ||
| " bnb_4bit_use_double_quant=True,\n", | ||
| " bnb_4bit_quant_type=\"nf4\",\n", | ||
| " bnb_4bit_compute_dtype=torch_dtype\n", | ||
| " )\n", | ||
| "\n", | ||
| " \n", | ||
| " model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map=\"auto\")\n", | ||
| "\n", | ||
| " results: List[str] = []\n", | ||
| " if logprobs:\n", | ||
| " result_logprobs: List[List[Tuple[int, float]]] = []\n", | ||
| "\n", | ||
| " for prompt in prompts:\n", | ||
| " formatted_prompt = build_prompt(\n", | ||
| " prompt[\"agent_type\"], \n", | ||
| " LLAMA_GUARD_CATEGORY, \n", | ||
| " create_conversation(prompt[\"prompt\"]))\n", | ||
| "\n", | ||
| "\n", | ||
| " input = tokenizer([formatted_prompt], return_tensors=\"pt\").to(\"cuda\")\n", | ||
| " prompt_len = input[\"input_ids\"].shape[-1]\n", | ||
| " output = model.generate(**input, max_new_tokens=10, pad_token_id=0, return_dict_in_generate=True, output_scores=logprobs)\n", | ||
| " \n", | ||
| " if logprobs:\n", | ||
| " transition_scores = model.compute_transition_scores(\n", | ||
| " output.sequences, output.scores, normalize_logits=True)\n", | ||
| "\n", | ||
| " generated_tokens = output.sequences[:, prompt_len:]\n", | ||
| " \n", | ||
| " if logprobs:\n", | ||
| " temp_logprobs: List[Tuple[int, float]] = []\n", | ||
| " for tok, score in zip(generated_tokens[0], transition_scores[0]):\n", | ||
| " temp_logprobs.append((tok.cpu().numpy(), score.cpu().numpy()))\n", | ||
| " \n", | ||
| " result_logprobs.append(temp_logprobs)\n", | ||
| " prompt[\"logprobs\"] = temp_logprobs\n", | ||
| " \n", | ||
| " result = tokenizer.decode(generated_tokens[0], skip_special_tokens=True) \n", | ||
| "\n", | ||
| " prompt[\"result\"] = result\n", | ||
| " results.append(result)\n", | ||
| "\n", | ||
| " return (results, result_logprobs if logprobs else None) " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "26966de0-a477-4a56-830f-5bd8565bede8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def pytorch_llm_eval(prompts: List[Tuple[List[str], AgentType, str, str, str]], ckpt_dir, logprobs: bool = False):\n", | ||
| " # defaults\n", | ||
| " temperature = 1\n", | ||
| " top_p = 1\n", | ||
| " max_seq_len = 4096\n", | ||
| " max_gen_len = 32\n", | ||
| " max_batch_size = 1\n", | ||
| "\n", | ||
| " generator = Llama.build(\n", | ||
| " ckpt_dir=ckpt_dir,\n", | ||
| " tokenizer_path=ckpt_dir + \"/tokenizer.model\",\n", | ||
| " max_seq_len=max_seq_len,\n", | ||
| " max_batch_size=max_batch_size,\n", | ||
| " )\n", | ||
| "\n", | ||
| "\n", | ||
| " results: List[str] = []\n", | ||
| " for prompt in prompts:\n", | ||
| " formatted_prompt = build_prompt(\n", | ||
| " prompt[\"agent_type\"], \n", | ||
| " LLAMA_GUARD_CATEGORY, \n", | ||
| " create_conversation(prompt[\"prompt\"]))\n", | ||
| "\n", | ||
| " result = generator.text_completion(\n", | ||
| " [formatted_prompt],\n", | ||
| " temperature,\n", | ||
| " top_p,\n", | ||
| " max_gen_len,\n", | ||
| " logprobs\n", | ||
| " )\n", | ||
| " # getting the first value only, as only a single prompt was sent to the function\n", | ||
| " generation_result = result[0][\"generation\"]\n", | ||
| " prompt[\"result\"] = generation_result\n", | ||
| " if logprobs:\n", | ||
| " prompt[\"logprobs\"] = result[0][\"logprobs\"]\n", | ||
| "\n", | ||
| " results.append(generation_result)\n", | ||
| "\n", | ||
| " return results" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "bd2ed6f5-e561-4aeb-9427-ae8bb261a772", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Setting variables used by the Llama classes\n", | ||
| "import os\n", | ||
| "os.environ[\"RANK\"] = \"0\"\n", | ||
| "os.environ[\"WORLD_SIZE\"] = \"1\"\n", | ||
| "os.environ[\"MASTER_ADDR\"] = \"127.0.0.1\"\n", | ||
| "os.environ[\"MASTER_PORT\"] = \"29500\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b54b4e41-2379-4163-adc6-465e9b7fc57a", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "def main():\n", | ||
| " \n", | ||
| " prompts: List[Dict[List[str], AgentType]] = [\n", | ||
| " {\n", | ||
| " \"prompt\": [\"<Sample user prompt>\"],\n", | ||
| " \"agent_type\": AgentType.USER\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"prompt\": [\"<Sample user prompt>\", \"<Sample agent response>\"],\n", | ||
| " \"agent_type\": AgentType.AGENT\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"prompt\": [\"<Sample user prompt>\", \n", | ||
| " \"<Sample agent response>\", \n", | ||
| " \"<Sample user reply>\", \n", | ||
| " \"<Sample agent response>\"],\n", | ||
| " \"agent_type\": AgentType.AGENT\n", | ||
| " }\n", | ||
| " ]\n", | ||
| "\n", | ||
| " \n", | ||
| " results = llm_eval(prompts, load_in_8bit = False, load_in_4bit = True)\n", | ||
| " \n", | ||
| " for i, prompt in enumerate(prompts):\n", | ||
| " print(prompt['prompt'])\n", | ||
| " print(f\"> {results[0][i]}\")\n", | ||
| " print(\"\\n==================================\\n\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "2345c875-d610-4b8a-b73c-464581575e93", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# used to be able to import this script in another notebook and not run the main function\n", | ||
| "if __name__ == '__main__' and '__file__' not in globals():\n", | ||
| " from huggingface_hub import login\n", | ||
| " login()\n", | ||
| " main()\n", | ||
| " " | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "jupytext": { | ||
| "formats": "ipynb,auto:light" | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.13" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } |
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Looks like inference.py seems to be deleted? Where should this command be run from? Should we provide a
cdinstruction beforepythoncommand?There was a problem hiding this comment.
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This is the inference.py script I'm referencing here, not the one in the llama_guard directory.