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andstor/andstor-gpt-j-6b-smart-contract-server.ipynb

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"cells": [
{
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"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/andstor/17105942ecf864d1796c36f6e74c5f29/andstor-gpt-j-6b-smart-contract-server.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vp3XPuaTu9jl"
},
"source": [
"\n",
"# GPT-J-6B smart contract code generation server"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KxLvv6UaPa33"
},
"source": [
"### **Introduction**\n",
"This notebook demonstrates how to run a REST server for the [gpt-j-6B-smart-contract](https://huggingface.co/andstor/gpt-j-6B-smart-contract) model. This is a version of the [gpt-j-6B](https://github.com/kingoflolz/mesh-transformer-jax/#GPT-J-6B) model, finetuned for smart contract code generation."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Si4GyYhOQMzi"
},
"source": [
"### **Load model**\n",
"Install the transformers library and load the model."
]
},
{
"cell_type": "code",
"source": [
"!pip install -q git+https://github.com/huggingface/transformers.git"
],
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},
"id": "rZlBkWdHZ_ae",
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},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[K |████████████████████████████████| 6.5 MB 9.2 MB/s \n",
"\u001b[K |████████████████████████████████| 67 kB 5.1 MB/s \n",
"\u001b[K |████████████████████████████████| 895 kB 52.2 MB/s \n",
"\u001b[K |████████████████████████████████| 596 kB 59.8 MB/s \n",
"\u001b[?25h Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"andstor/gpt-j-6B-smart-contract\")\n",
"model = AutoModelForCausalLM.from_pretrained(\"andstor/gpt-j-6B-smart-contract\", low_cpu_mem_usage=True).half().to(device)\n"
],
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{
"cell_type": "markdown",
"source": [
"### **REST Server**\n",
"This sets up a REST API server accessible through ngrok."
],
"metadata": {
"id": "uAwAruuI7ULQ"
}
},
{
"cell_type": "code",
"source": [
"!pip install -q fastapi nest-asyncio pyngrok uvicorn"
],
"metadata": {
"id": "4pDIsNE1ucAX",
"outputId": "60a014d5-59f4-4393-c13c-fce28ab4536d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l\r\u001b[K |██████ | 10 kB 32.7 MB/s eta 0:00:01\r\u001b[K |████████████ | 20 kB 20.6 MB/s eta 0:00:01\r\u001b[K |██████████████████ | 30 kB 16.6 MB/s eta 0:00:01\r\u001b[K |████████████████████████ | 40 kB 14.3 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████ | 51 kB 8.6 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 54 kB 2.3 MB/s \n",
"\u001b[K |████████████████████████████████| 745 kB 15.6 MB/s \n",
"\u001b[K |████████████████████████████████| 53 kB 1.6 MB/s \n",
"\u001b[K |████████████████████████████████| 10.9 MB 53.1 MB/s \n",
"\u001b[K |████████████████████████████████| 58 kB 5.4 MB/s \n",
"\u001b[K |████████████████████████████████| 79 kB 7.1 MB/s \n",
"\u001b[K |████████████████████████████████| 58 kB 5.7 MB/s \n",
"\u001b[?25h Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import uvicorn\n",
"from fastapi import FastAPI, HTTPException, Request\n",
"from pydantic import BaseModel\n",
"from typing import Any\n",
"from transformers import pipeline\n",
"\n",
"\n",
"app = FastAPI()\n",
"device = torch.device(0 if torch.cuda.is_available() else -1)\n",
"pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, device=0)\n",
"\n",
"class ServeModelInfoResult(BaseModel):\n",
" \"\"\"\n",
" Expose model information\n",
" \"\"\"\n",
" infos: dict\n",
"\n",
"class ServeForwardResult(BaseModel):\n",
" \"\"\"\n",
" Forward result model\n",
" \"\"\"\n",
" output: Any\n",
"\n",
"@app.get(\"/\")\n",
"def model_info(self):\n",
" return ServeModelInfoResult(infos=vars(pipe.model.config))\n",
"\n",
"@app.post(\"/forward\")\n",
"async def forward(request: Request):\n",
" \"\"\"\n",
" Forward the provided inputs through the model: - **input**: List of inputs to forward through the model.\n",
" Any other argument will be passed on to the model.\n",
" \"\"\"\n",
" try:\n",
" kwargs = await request.json()\n",
" inputs = kwargs.pop(\"inputs\")\n",
" except Exception as e:\n",
" raise HTTPException(status_code=400, detail=\"inputs field is required\")\n",
" # Check we don't have empty string\n",
" if len(inputs) == 0:\n",
" return ServeForwardResult(output=[])\n",
" try:\n",
" # Forward through the model\n",
" output = pipe(inputs, **kwargs)\n",
" return ServeForwardResult(output=output)\n",
" except Exception as e:\n",
" raise HTTPException(500, {\"error\": str(e)})"
],
"metadata": {
"id": "IlMcyg9YuikY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import nest_asyncio\n",
"from pyngrok import ngrok\n",
"import uvicorn\n",
"\n",
"ngrok.set_auth_token(\"NGROK_AUTH_TOKEN\")\n",
"\n",
"ngrok_tunnel = ngrok.connect(8000)\n",
"print('Public URL:', ngrok_tunnel.public_url)\n",
"nest_asyncio.apply()\n",
"uvicorn.run(app, port=8000)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tFVMmgdjuqe_",
"outputId": "aea8910f-c480-4430-9913-a0a53bf3a6a3"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Public URL: http://f665-35-185-209-69.ngrok.io\n"
]
}
]
}
]
}
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