forked from Archives/langchain
Add RELLM and JSONFormer experimental LLM decoding (#4185)
[RELLM](https://github.com/r2d4/rellm) is a library that wraps local HuggingFace pipeline models for structured decoding. RELLM works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression. [JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directlydynamic_agent_tools
Zander Chase
1 year ago
committed by
GitHub
parent
54f5523197
commit
d85b04be7f
@ -0,0 +1,280 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fdd7864c-93e6-4eb4-a923-b80d2ae4377d",
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"metadata": {},
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"source": [
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"# Structured Decoding with JSONFormer\n",
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"\n",
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"[JSONFormer](https://github.com/1rgs/jsonformer) is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema.\n",
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"\n",
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"It works by filling in the structure tokens and then sampling the content tokens from the model.\n",
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"\n",
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"**Warning - this module is still experimental**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1617e327-d9a2-4ab6-aa9f-30a3167a3393",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install --upgrade jsonformer > /dev/null"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66bd89f1-8daa-433d-bb8f-5b0b3ae34b00",
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"metadata": {},
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"source": [
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"### HuggingFace Baseline\n",
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"\n",
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"First, let's establish a qualitative baseline by checking the output of the model without structured decoding."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d4d616ae-4d11-425f-b06c-c706d0386c68",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import logging\n",
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"logging.basicConfig(level=logging.ERROR)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1bdc7b60-6ffb-4099-9fa6-13efdfc45b04",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from typing import Optional\n",
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"from langchain.tools import tool\n",
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"import os\n",
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"import json\n",
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"import requests\n",
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"\n",
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"HF_TOKEN = os.environ.get(\"HUGGINGFACE_API_KEY\")\n",
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"\n",
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"@tool\n",
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"def ask_star_coder(query: str, \n",
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" temperature: float = 1.0,\n",
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" max_new_tokens: float = 250):\n",
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" \"\"\"Query the BigCode StarCoder model about coding questions.\"\"\"\n",
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" url = \"https://api-inference.huggingface.co/models/bigcode/starcoder\"\n",
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" headers = {\n",
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" \"Authorization\": f\"Bearer {HF_TOKEN}\",\n",
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" \"content-type\": \"application/json\"\n",
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" }\n",
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" payload = {\n",
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" \"inputs\": f\"{query}\\n\\nAnswer:\",\n",
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" \"temperature\": temperature,\n",
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" \"max_new_tokens\": int(max_new_tokens),\n",
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" }\n",
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" response = requests.post(url, headers=headers, data=json.dumps(payload))\n",
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" response.raise_for_status()\n",
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" return json.loads(response.content.decode(\"utf-8\"))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d5522977-51e8-40eb-9403-8ab70b14908e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"prompt = \"\"\"You must respond using JSON format, with a single action and single action input.\n",
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"You may 'ask_star_coder' for help on coding problems.\n",
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"\n",
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"{arg_schema}\n",
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"\n",
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"EXAMPLES\n",
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"----\n",
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"Human: \"So what's all this about a GIL?\"\n",
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"AI Assistant:{{\n",
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" \"action\": \"ask_star_coder\",\n",
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" \"action_input\": {{\"query\": \"What is a GIL?\", \"temperature\": 0.0, \"max_new_tokens\": 100}}\"\n",
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"}}\n",
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"Observation: \"The GIL is python's Global Interpreter Lock\"\n",
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"Human: \"Could you please write a calculator program in LISP?\"\n",
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"AI Assistant:{{\n",
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" \"action\": \"ask_star_coder\",\n",
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" \"action_input\": {{\"query\": \"Write a calculator program in LISP\", \"temperature\": 0.0, \"max_new_tokens\": 250}}\n",
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"}}\n",
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"Observation: \"(defun add (x y) (+ x y))\\n(defun sub (x y) (- x y ))\"\n",
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"Human: \"What's the difference between an SVM and an LLM?\"\n",
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"AI Assistant:{{\n",
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" \"action\": \"ask_star_coder\",\n",
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" \"action_input\": {{\"query\": \"What's the difference between SGD and an SVM?\", \"temperature\": 1.0, \"max_new_tokens\": 250}}\n",
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"}}\n",
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"Observation: \"SGD stands for stochastic gradient descent, while an SVM is a Support Vector Machine.\"\n",
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"\n",
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"BEGIN! Answer the Human's question as best as you are able.\n",
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"------\n",
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"Human: 'What's the difference between an iterator and an iterable?'\n",
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"AI Assistant:\"\"\".format(arg_schema=ask_star_coder.args)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "9148e4b8-d370-4c05-a873-c121b65057b5",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 'What's the difference between an iterator and an iterable?'\n",
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"\n"
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]
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}
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],
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"source": [
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"from transformers import pipeline\n",
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"from langchain.llms import HuggingFacePipeline\n",
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"\n",
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"hf_model = pipeline(\"text-generation\", model=\"cerebras/Cerebras-GPT-590M\", max_new_tokens=200)\n",
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"\n",
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"original_model = HuggingFacePipeline(pipeline=hf_model)\n",
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"\n",
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"generated = original_model.predict(prompt, stop=[\"Observation:\", \"Human:\"])\n",
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"print(generated)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6e7b9cf-8ce5-4f87-b4bf-100321ad2dd1",
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"metadata": {},
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"source": [
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"***That's not so impressive, is it? It didn't follow the JSON format at all! Let's try with the structured decoder.***"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96115154-a90a-46cb-9759-573860fc9b79",
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"metadata": {},
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"source": [
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"## JSONFormer LLM Wrapper\n",
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"\n",
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"Let's try that again, now providing a the Action input's JSON Schema to the model."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "30066ee7-9a92-4ae8-91bf-3262bf3c70c2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"decoder_schema = {\n",
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" \"title\": \"Decoding Schema\",\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"action\": {\"type\": \"string\", \"default\": ask_star_coder.name},\n",
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" \"action_input\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": ask_star_coder.args,\n",
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" }\n",
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" }\n",
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"} "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "0f7447fe-22a9-47db-85b9-7adf0f19307d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.experimental.llms import JsonFormer\n",
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"json_former = JsonFormer(json_schema=decoder_schema, pipeline=hf_model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "d865e049-a5c3-4648-92db-8b912b7474ee",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\"action\": \"ask_star_coder\", \"action_input\": {\"query\": \"What's the difference between an iterator and an iter\", \"temperature\": 0.0, \"max_new_tokens\": 50.0}}\n"
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]
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}
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],
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"source": [
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"results = json_former.predict(prompt, stop=[\"Observation:\", \"Human:\"])\n",
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"print(results)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "32077d74-0605-4138-9a10-0ce36637040d",
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"metadata": {
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"tags": []
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},
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"source": [
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"**Voila! Free of parsing errors.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "da63ce31-de79-4462-a1a9-b726b698c5ba",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,208 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fdd7864c-93e6-4eb4-a923-b80d2ae4377d",
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"metadata": {},
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"source": [
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"# Structured Decoding with RELLM\n",
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"\n",
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"[RELLM](https://github.com/r2d4/rellm) is a library that wraps local HuggingFace pipeline models for structured decoding.\n",
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"\n",
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"It works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression.\n",
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"\n",
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"\n",
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"**Warning - this module is still experimental**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "1617e327-d9a2-4ab6-aa9f-30a3167a3393",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install rellm > /dev/null"
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]
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},
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{
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"cell_type": "markdown",
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"id": "66bd89f1-8daa-433d-bb8f-5b0b3ae34b00",
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"metadata": {},
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"source": [
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"### HuggingFace Baseline\n",
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"\n",
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"First, let's establish a qualitative baseline by checking the output of the model without structured decoding."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d4d616ae-4d11-425f-b06c-c706d0386c68",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import logging\n",
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"logging.basicConfig(level=logging.ERROR)\n",
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"prompt = \"\"\"Human: \"What's the capital of the United States?\"\n",
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"AI Assistant:{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"The capital of the United States is Washington D.C.\"\n",
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"}\n",
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"Human: \"What's the capital of Pennsylvania?\"\n",
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"AI Assistant:{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"The capital of Pennsylvania is Harrisburg.\"\n",
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"}\n",
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"Human: \"What 2 + 5?\"\n",
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"AI Assistant:{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"2 + 5 = 7.\"\n",
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"}\n",
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"Human: 'What's the capital of Maryland?'\n",
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"AI Assistant:\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9148e4b8-d370-4c05-a873-c121b65057b5",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"generations=[[Generation(text=' \"What\\'s the capital of Maryland?\"\\n', generation_info=None)]] llm_output=None\n"
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]
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}
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],
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"source": [
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"from transformers import pipeline\n",
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"from langchain.llms import HuggingFacePipeline\n",
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"\n",
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"hf_model = pipeline(\"text-generation\", model=\"cerebras/Cerebras-GPT-590M\", max_new_tokens=200)\n",
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"\n",
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"original_model = HuggingFacePipeline(pipeline=hf_model)\n",
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"\n",
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"generated = original_model.generate([prompt], stop=[\"Human:\"])\n",
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"print(generated)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b6e7b9cf-8ce5-4f87-b4bf-100321ad2dd1",
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"metadata": {},
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"source": [
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"***That's not so impressive, is it? It didn't answer the question and it didn't follow the JSON format at all! Let's try with the structured decoder.***"
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]
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},
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{
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"cell_type": "markdown",
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"id": "96115154-a90a-46cb-9759-573860fc9b79",
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"metadata": {},
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"source": [
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"## RELLM LLM Wrapper\n",
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"\n",
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"Let's try that again, now providing a regex to match the JSON structured format."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "65c12e2a-bd7f-4cf0-8ef8-92cfa31c92ef",
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"metadata": {},
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"outputs": [],
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"source": [
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"import regex # Note this is the regex library NOT python's re stdlib module\n",
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"\n",
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"# We'll choose a regex that matches to a structured json string that looks like:\n",
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"# {\n",
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"# \"action\": \"Final Answer\",\n",
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"# \"action_input\": string or dict\n",
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"# }\n",
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"pattern = regex.compile(r'\\{\\s*\"action\":\\s*\"Final Answer\",\\s*\"action_input\":\\s*(\\{.*\\}|\"[^\"]*\")\\s*\\}\\nHuman:')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "de85b1f8-b405-4291-b6d0-4b2c56e77ad6",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{\"action\": \"Final Answer\",\n",
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" \"action_input\": \"The capital of Maryland is Baltimore.\"\n",
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"}\n",
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"\n"
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]
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}
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],
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"source": [
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"from langchain.experimental.llms import RELLM\n",
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"\n",
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"model = RELLM(pipeline=hf_model, regex=pattern, max_new_tokens=200)\n",
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"\n",
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"generated = model.predict(prompt, stop=[\"Human:\"])\n",
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"print(generated)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "32077d74-0605-4138-9a10-0ce36637040d",
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"metadata": {
|
||||
"tags": []
|
||||
},
|
||||
"source": [
|
||||
"**Voila! Free of parsing errors.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4bd208a1-779c-4c47-97d9-9115d15d441f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,6 @@
|
||||
"""Experimental LLM wrappers."""
|
||||
|
||||
from langchain.experimental.llms.jsonformer_decoder import JsonFormer
|
||||
from langchain.experimental.llms.rellm_decoder import RELLM
|
||||
|
||||
__all__ = ["RELLM", "JsonFormer"]
|
||||
@ -0,0 +1,60 @@
|
||||
"""Experimental implementation of jsonformer wrapped LLM."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from typing import TYPE_CHECKING, List, Optional, cast
|
||||
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import jsonformer
|
||||
|
||||
|
||||
def import_jsonformer() -> jsonformer:
|
||||
"""Lazily import jsonformer."""
|
||||
try:
|
||||
import jsonformer
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import jsonformer python package. "
|
||||
"Please install it with `pip install jsonformer`."
|
||||
)
|
||||
return jsonformer
|
||||
|
||||
|
||||
class JsonFormer(HuggingFacePipeline):
|
||||
json_schema: dict = Field(..., description="The JSON Schema to complete.")
|
||||
max_new_tokens: int = Field(
|
||||
default=200, description="Maximum number of new tokens to generate."
|
||||
)
|
||||
debug: bool = Field(default=False, description="Debug mode.")
|
||||
|
||||
@root_validator
|
||||
def check_jsonformer_installation(cls, values: dict) -> dict:
|
||||
import_jsonformer()
|
||||
return values
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
) -> str:
|
||||
jsonformer = import_jsonformer()
|
||||
from transformers import Text2TextGenerationPipeline
|
||||
|
||||
pipeline = cast(Text2TextGenerationPipeline, self.pipeline)
|
||||
|
||||
model = jsonformer.Jsonformer(
|
||||
model=pipeline.model,
|
||||
tokenizer=pipeline.tokenizer,
|
||||
json_schema=self.json_schema,
|
||||
prompt=prompt,
|
||||
max_number_tokens=self.max_new_tokens,
|
||||
debug=self.debug,
|
||||
)
|
||||
text = model()
|
||||
return json.dumps(text)
|
||||
@ -0,0 +1,67 @@
|
||||
"""Experimental implementation of RELLM wrapped LLM."""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, List, Optional, cast
|
||||
|
||||
from pydantic import Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForLLMRun
|
||||
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import rellm
|
||||
from regex import Pattern as RegexPattern
|
||||
else:
|
||||
try:
|
||||
from regex import Pattern as RegexPattern
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def import_rellm() -> rellm:
|
||||
"""Lazily import rellm."""
|
||||
try:
|
||||
import rellm
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import rellm python package. "
|
||||
"Please install it with `pip install rellm`."
|
||||
)
|
||||
return rellm
|
||||
|
||||
|
||||
class RELLM(HuggingFacePipeline):
|
||||
regex: RegexPattern = Field(..., description="The structured format to complete.")
|
||||
max_new_tokens: int = Field(
|
||||
default=200, description="Maximum number of new tokens to generate."
|
||||
)
|
||||
|
||||
@root_validator
|
||||
def check_rellm_installation(cls, values: dict) -> dict:
|
||||
import_rellm()
|
||||
return values
|
||||
|
||||
def _call(
|
||||
self,
|
||||
prompt: str,
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
) -> str:
|
||||
rellm = import_rellm()
|
||||
from transformers import Text2TextGenerationPipeline
|
||||
|
||||
pipeline = cast(Text2TextGenerationPipeline, self.pipeline)
|
||||
|
||||
text = rellm.complete_re(
|
||||
prompt,
|
||||
self.regex,
|
||||
tokenizer=pipeline.tokenizer,
|
||||
model=pipeline.model,
|
||||
max_new_tokens=self.max_new_tokens,
|
||||
)
|
||||
if stop is not None:
|
||||
# This is a bit hacky, but I can't figure out a better way to enforce
|
||||
# stop tokens when making calls to huggingface_hub.
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
Loading…
Reference in New Issue