testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Set environment variable for OpenMP runtime\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'\n",
    "\n",
    "# Now you can import torch and transformers\n",
    "import torch\n",
    "from transformers import GPT2LMHeadModel, GPT2Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using pad_token, but it is not set yet.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Setup\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "# Load the model and tokenizer\n",
    "model_name = 'distilgpt2'\n",
    "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
    "\n",
    "# Set the pad token if it's not already defined\n",
    "if tokenizer.pad_token is None:\n",
    "    tokenizer.pad_token = tokenizer.eos_token\n",
    "\n",
    "model = GPT2LMHeadModel.from_pretrained(model_name).to(device)\n",
    "\n",
    "# Generate response function with attention mask\n",
    "def generate_response(prompt_text):\n",
    "    encoded_input = tokenizer(prompt_text, return_tensors='pt', padding=True, truncation=True, max_length=512)\n",
    "    input_ids = encoded_input['input_ids'].to(device)\n",
    "    attention_mask = encoded_input['attention_mask'].to(device)\n",
    "\n",
    "    try:\n",
    "        outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=100, num_return_sequences=1, pad_token_id=tokenizer.eos_token_id)\n",
    "        text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "        return text\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "        return None\n",
    "\n",
    "# Entropy calculation function\n",
    "def calculate_entropy(text):\n",
    "    encoded_input = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)\n",
    "    input_ids = encoded_input['input_ids'].to(device)\n",
    "    attention_mask = encoded_input['attention_mask'].to(device)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(input_ids, labels=input_ids, attention_mask=attention_mask)\n",
    "    loss = outputs.loss\n",
    "    return loss.item()\n",
    "\n",
    "prompts = [\"What is the capital of France?\", \"Who wrote 1984?\", \"How does photosynthesis work?\"]\n",
    "responses = []\n",
    "entropies = []\n",
    "# Generate responses for each prompt\n",
    "for prompt in prompts:\n",
    "    response = generate_response(prompt)\n",
    "    responses.append(response)\n",
    "for response in responses:\n",
    "    entropy = calculate_entropy(response)\n",
    "    entropies.append(entropy)\n",
    "\n",
    "# Plotting the results\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Black-box results: Response length\n",
    "plt.subplot(2, 1, 1)\n",
    "sns.barplot(x=prompts, y=[len(resp) for resp in responses])\n",
    "plt.title('Response Lengths for Different Prompts')\n",
    "\n",
    "# White-box results: Entropy of the responses\n",
    "plt.subplot(2, 1, 2)\n",
    "sns.barplot(x=prompts, y=entropies)\n",
    "plt.title('Entropy of the Model Responses')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Set up the device and model\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model_name = 'distilgpt2'\n",
    "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
    "tokenizer.pad_token = tokenizer.eos_token  # Set pad_token\n",
    "model = GPT2LMHeadModel.from_pretrained(model_name).to(device)\n",
    "\n",
    "# rest of your code...\n",
    "\n",
    "\n",
    "# Function to generate a response and calculate perplexity\n",
    "def generate_response_and_perplexity(prompt_text):\n",
    "    # Tokenize and encode the prompt text\n",
    "    encoded_input = tokenizer(prompt_text, return_tensors='pt', padding=True, truncation=True, max_length=1024).to(device)\n",
    "    \n",
    "    # Generate response\n",
    "    output_sequences = model.generate(input_ids=encoded_input['input_ids'], attention_mask=encoded_input['attention_mask'], max_length=100)\n",
    "    \n",
    "    # Decode the output sequence to text\n",
    "    generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)\n",
    "    \n",
    "    # Calculate perplexity\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**encoded_input, labels=encoded_input['input_ids'])\n",
    "        loss = outputs.loss if outputs.loss is not None else torch.tensor(0)\n",
    "    perplexity = torch.exp(loss).item()\n",
    "    \n",
    "    return generated_text, perplexity\n",
    "\n",
    "# Generate responses and calculate perplexities for a range of prompts\n",
    "prompts = [f\"Prompt {i}: The current state of AI is\" for i in range(100)]\n",
    "responses = []\n",
    "perplexities = []\n",
    "lengths = []\n",
    "\n",
    "for prompt in prompts:\n",
    "    response, perplexity = generate_response_and_perplexity(prompt)\n",
    "    response.replace(\": The current state of AI is\", '')\n",
    "    responses.append(response)\n",
    "    perplexities.append(perplexity)\n",
    "    lengths.append(len(response.split()))\n",
    "\n",
    "# Plot perplexity over responses\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(prompts, perplexities, marker='o', color='skyblue')\n",
    "plt.title('Perplexity of Generated Responses')\n",
    "plt.xlabel('Prompts')\n",
    "plt.ylabel('Perplexity')\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Plot length of responses\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(prompts, lengths, marker='o', color='orange')\n",
    "plt.title('Length of Generated Responses')\n",
    "plt.xlabel('Prompts')\n",
    "plt.ylabel('Length of Response (in words)')\n",
    "plt.xticks(rotation=90)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      ".\n",
      "----------------------------------------------------------------------\n",
      "Ran 1 test in 1.378s\n",
      "\n",
      "OK\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.main.TestProgram at 0x2398c9f89d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "class TestModelFunctions(unittest.TestCase):\n",
    "    def setUp(self):\n",
    "        # Setting up before every test (commonly used test variables or states)\n",
    "        self.prompt_text = \"Test prompt: The current state of AI is\"\n",
    "\n",
    "    def test_generate_response_and_perplexity(self):\n",
    "        # Testing if the function returns the expected types and perplexity range\n",
    "        response, perplexity = generate_response_and_perplexity(self.prompt_text)\n",
    "        self.assertIsInstance(response, str, \"The response should be a string.\")\n",
    "        self.assertIsInstance(perplexity, float, \"Perplexity should be a float.\")\n",
    "        self.assertGreater(perplexity, 1, \"Perplexity should be greater than 1.\")\n",
    "\n",
    "unittest.main(argv=[''], exit=False) # This line replaces if __name__ == '__main__':\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      ".Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      ".Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
      "F\n",
      "======================================================================\n",
      "FAIL: test_response_perplexity (__main__.TestModelOutput)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\harsh\\AppData\\Local\\Temp\\ipykernel_16084\\4187649925.py\", line 14, in test_response_perplexity\n",
      "    self.assertTrue(10 <= perplexity <= 50, \"Perplexity should fall within a reasonable range.\")\n",
      "AssertionError: False is not true : Perplexity should fall within a reasonable range.\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 3 tests in 3.396s\n",
      "\n",
      "FAILED (failures=1)\n"
     ]
    }
   ],
   "source": [
    "import unittest\n",
    "\n",
    "class TestModelOutput(unittest.TestCase):\n",
    "    def test_response_content(self):\n",
    "        # Testing if the generated response contains certain expected phrases\n",
    "        prompt_text = \"Test prompt: The future of AI is\"\n",
    "        response, _ = generate_response_and_perplexity(prompt_text)\n",
    "        self.assertIn(\"future of AI\", response, \"The response should discuss the future of AI.\")\n",
    "\n",
    "    def test_response_perplexity(self):\n",
    "        # Testing if the perplexity falls within a plausible range for a known input\n",
    "        prompt_text = \"Test prompt: The future of AI is\"\n",
    "        _, perplexity = generate_response_and_perplexity(prompt_text)\n",
    "        self.assertTrue(10 <= perplexity <= 50, \"Perplexity should fall within a reasonable range.\")\n",
    "\n",
    "# Running the tests\n",
    "if __name__ == '__main__':\n",
    "    unittest.main(argv=['first-arg-is-ignored'], exit=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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