{
 "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",
    "import numpy as np\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.cat([torch.ones(input_ids.shape), torch.ones([1, 1])], dim=-1).detach().cpu().numpy().astype('int64')\n",
    "    }\n",
    "    for i in range(28):\n",
    "        ort_inputs[\"past_key_values.{}.key\".format(i)] = np.zeros((1,16,1,256), dtype='float32')\n",
    "        ort_inputs[\"past_key_values.{}.value\".format(i)] = np.zeros((1,16,1,256), dtype='float32')\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",
    "\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",
    "total_time = 0.0\n",
    "num_iter = 10\n",
    "num_warmup = 3\n",
    "\n",
    "# start\n",
    "for idx in range(num_iter):\n",
    "    text = []\n",
    "    tic = time.time()\n",
    "\n",
    "    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
    "\n",
    "    attention_mask = torch.ones(input_ids.shape[1] +1)\n",
    "    attention_mask[0] = 0\n",
    "    attention_mask = attention_mask.unsqueeze(0)\n",
    "\n",
    "    inp = {'input_ids': input_ids.detach().cpu().numpy(),\n",
    "            'attention_mask': attention_mask.detach().cpu().numpy().astype('int64')}\n",
    "    for i in range(28):\n",
    "        inp[\"past_key_values.{}.key\".format(i)] = torch.zeros([1,16,1,256]).detach().cpu().numpy()\n",
    "        inp[\"past_key_values.{}.value\".format(i)] = torch.zeros([1,16,1,256]).detach().cpu().numpy()\n",
    "\n",
    "    for step in range(32):\n",
    "\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",
    "        present_kv = output[1]\n",
    "        for i in range(28):\n",
    "\n",
    "            if step == 0:\n",
    "                inp[\"past_key_values.{}.key\".format(i)] = output[2*i+1][:, :, 1:, :]\n",
    "                inp[\"past_key_values.{}.value\".format(i)] = output[2*i+2][:, :, 1:, :]\n",
    "            else:\n",
    "                inp[\"past_key_values.{}.key\".format(i)] = output[2*i+1]\n",
    "                inp[\"past_key_values.{}.value\".format(i)] = output[2*i+2]\n",
    "\n",
    "        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)\n",
    "        if step == 0:\n",
    "            attention_mask = torch.cat([attention_mask[:, 1:], torch.ones([1, 1])], dim=-1)\n",
    "        else:\n",
    "            attention_mask = torch.cat([attention_mask, torch.ones([1, 1])], dim=-1)\n",
    "\n",
    "        inp['attention_mask'] = attention_mask.detach().cpu().numpy().astype('int64')\n",
    "        inp['input_ids'] = input_ids[:, -1:].detach().cpu().numpy()\n",
    "\n",
    "    print(tokenizer.decode(input_ids[0]))\n",
    "    toc = time.time()\n",
    "    if idx >= num_warmup:\n",
    "        total_time += (toc - tic)\n",
    "print(\"Inference latency: %.3f s.\" % (total_time / (num_iter - num_warmup)))"
   ]
  }
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