add script

Signed-off-by: Meng, Hengyu <hengyu.meng@intel.com>

evaluation.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates how to perform evaluation on a gpt-j-6B-int8 model."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisite"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install onnx onnxruntime torch transformers datasets accelerate"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run\n",
+    "\n",
+    "### 1. Get lambada acc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "import torch\n",
+    "from datasets import load_dataset\n",
+    "import onnxruntime as ort\n",
+    "from torch.nn.functional import pad\n",
+    "\n",
+    "# load model\n",
+    "model_id = \"EleutherAI/gpt-j-6B\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "\n",
+    "def tokenize_function(examples):\n",
+    "    example = tokenizer(examples['text'])\n",
+    "    return example\n",
+    "\n",
+    "# create dataset\n",
+    "dataset = load_dataset('lambada', split='validation')\n",
+    "dataset = dataset.shuffle(seed=42)\n",
+    "dataset = dataset.map(tokenize_function, batched=True)\n",
+    "dataset.set_format(type='torch', columns=['input_ids'])\n",
+    "\n",
+    "# create session\n",
+    "options = ort.SessionOptions()\n",
+    "options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL\n",
+    "session = ort.InferenceSession('/path/to/model.onnx', options, providers=ort.get_available_providers())\n",
+    "total, hit = 0, 0\n",
+    "index = 1\n",
+    "\n",
+    "# inference\n",
+    "for idx, batch in enumerate(dataset):\n",
+    "    input_ids = batch['input_ids'].unsqueeze(0)\n",
+    "    label = input_ids[:, -1]\n",
+    "    pad_len = 0  ##set to 0\n",
+    "    input_ids = pad(input_ids, (0, pad_len), value=1)\n",
+    "    ort_inputs = {\n",
+    "        'input_ids': input_ids.detach().cpu().numpy(),\n",
+    "        'attention_mask': torch.ones(input_ids.shape).detach().cpu().numpy().astype('int64')\n",
+    "    }\n",
+    "    predictions = session.run(None, ort_inputs)\n",
+    "    outputs = torch.from_numpy(predictions[0]) \n",
+    "    last_token_logits = outputs[:, -2 - pad_len, :]\n",
+    "    pred = last_token_logits.argmax(dim=-1)\n",
+    "    total += label.size(0)\n",
+    "    hit += (pred == label).sum().item()\n",
+    "acc = hit / total\n",
+    "print('acc: ', acc)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Text Generation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import time\n",
+    "import sys\n",
+    "\n",
+    "# create session\n",
+    "sess_options = ort.SessionOptions()\n",
+    "sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL\n",
+    "session = ort.InferenceSession('/path/to/model.onnx', sess_options)\n",
+    "\n",
+    "# input prompt\n",
+    "# 32 tokens input\n",
+    "prompt = \"Once upon a time, there existed a little girl, who liked to have adventures.\" + \\\n",
+    "                 \" She wanted to go to places and meet new people, and have fun.\"\n",
+    "\n",
+    "print(\"prompt: \", prompt)\n",
+    "\n",
+    "# start\n",
+    "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
+    "for i in range(32):\n",
+    "    inp = {'input_ids': input_ids.detach().cpu().numpy(),\n",
+    "            'attention_mask': torch.ones(input_ids.shape).detach().cpu().numpy().astype('int64')}\n",
+    "    output = session.run(None, inp)\n",
+    "    logits = output[0]\n",
+    "    logits = torch.from_numpy(logits)\n",
+    "    next_token_logits = logits[:, -1, :]\n",
+    "    probs = torch.nn.functional.softmax(next_token_logits, dim=-1)\n",
+    "    next_tokens = torch.argmax(probs, dim=-1)\n",
+    "    input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)\n",
+    "print(tokenizer.decode(input_ids[0]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}