LLM_speaker_identification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zosrWhb1G6Yt"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8ZT4GS2Z0pD",
        "outputId": "87f0ba8f-ba4c-4eea-87aa-3aa005e809bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting accelerate\n",
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            "Installing collected packages: xxhash, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, humanfriendly, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, coloredlogs, nvidia-cusolver-cu12, datasets, bitsandbytes, accelerate, optimum\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed accelerate-0.29.3 bitsandbytes-0.43.1 coloredlogs-15.0.1 datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 humanfriendly-10.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 optimum-1.19.1 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers accelerate bitsandbytes optimum\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "GZnqSUswGxIC"
      },
      "outputs": [],
      "source": [
        "import gc\n",
        "\n",
        "import pandas as pd\n",
        "import torch\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "from transformers.pipelines import pipeline\n",
        "from tqdm.notebook import tqdm\n",
        "from tqdm.contrib import tenumerate\n",
        "from transformers import AutoTokenizer, BitsAndBytesConfig\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zZXOms1f6Eq"
      },
      "source": [
        "Helper function for freeing allocation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3Bk8NgoNf9nu"
      },
      "outputs": [],
      "source": [
        "def flush():\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "    torch.cuda.reset_peak_memory_stats()\n",
        "\n",
        "flush()\n",
        "\n",
        "\n",
        "def find_xth_index(tokens, x):\n",
        "  x = int(x)\n",
        "  letter_count = 0\n",
        "  for i, token in enumerate(tokens):\n",
        "    if letter_count + len(token) >= x:\n",
        "      return i\n",
        "    letter_count += len(token)\n",
        "  return len(tokens) - 1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5C4KPGkEIYm_"
      },
      "source": [
        "Load novels from the project dialogism novel corpus.\n",
        "To ensure persistance in this notebook, mount Google drive and upload it there. It's also more convenient."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "vOQPNE4-Ih0r"
      },
      "outputs": [],
      "source": [
        "novel_names = [\"PrideAndPrejudice\", \"Emma\"]\n",
        "data_folder = \".\"\n",
        "p_contexts = []\n",
        "e_contexts = []\n",
        "context_lengths = [1, 2, 4, 8, 16]\n",
        "\n",
        "\n",
        "p_quotes = pd.read_csv(f\"{data_folder}/PrideAndPrejudice.csv\")\n",
        "e_quotes = pd.read_csv(f\"{data_folder}/Emma.csv\")\n",
        "\n",
        "for length in context_lengths:\n",
        "  p_contexts.append(pd.read_csv(f\"{data_folder}/PrideAndPrejudice_context{length}.csv\"))\n",
        "  e_contexts.append(pd.read_csv(f\"{data_folder}/Emma_context{length}.csv\"))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmCE7N63NmEC"
      },
      "source": [
        "Define models to use from Hugging Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "WpiHT_XlNpzG"
      },
      "outputs": [],
      "source": [
        "models = [\n",
        "    \"meta-llama/Llama-2-7b-chat-hf\",\n",
        "    \"meta-llama/Llama-2-13b-chat-hf\",\n",
        "    \"mistralai/Mistral-7B-Instruct-v0.2\",\n",
        "]\n",
        "\n",
        "model_names = [\n",
        "    \"Llama-2-7b-chat-hf\",\n",
        "    \"Llama-2-13b-chat-hf\",\n",
        "    \"Mistral-7B-Instruct-v0.2\",\n",
        "]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7xp-XoRiguN"
      },
      "source": [
        "Define prompts to use"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "es7y0GPxir16"
      },
      "outputs": [],
      "source": [
        "def llama_prompt(quote):\n",
        "  return f\"OUTPUT THE NAME OF THE CHARACTER WHO SAID:\\n'{quote}'\\n\\nOnly give me the speaker’s name and nothing else. Please do NOT include the quote in the response.\"\n",
        "\n",
        "def mistral_prompt(quote):\n",
        "  return f\"[INST]\\n{llama_prompt(quote)}\\n[/INST]\"\n",
        "\n",
        "def llama_context_prompt(quote, left, right):\n",
        "  return f\"CONTEXT:\\n'{left} {quote} {right}'\\n\\nGIVEN CONTEXT, {llama_prompt(quote)}\"\n",
        "\n",
        "def mistral_context_prompt(quote, left, right):\n",
        "  return f\"[INST]\\n{llama_context_prompt(quote, left, right)}\\n[/INST]\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDxwpm9ql0pk"
      },
      "source": [
        "Get the pretrained models from Hugging Face and run the predictions on each novel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "rl6bLkVniofL"
      },
      "outputs": [],
      "source": [
        "def infer(novel, model_name, model, model_prompt, model_context_prompt, quotes, contexts):\n",
        "  # del pipe\n",
        "  flush()\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model)\n",
        "  tokenizer.pad_token = tokenizer.eos_token\n",
        "  pipe = pipeline(\n",
        "      task = \"text-generation\",\n",
        "      model = model,\n",
        "      tokenizer = tokenizer,\n",
        "      device_map=\"auto\",\n",
        "      torch_dtype=torch.bfloat16,\n",
        "      trust_remote_code=True,\n",
        "      model_kwargs = {\n",
        "          \"low_cpu_mem_usage\": True,\n",
        "          },\n",
        "      )\n",
        "\n",
        "  # Infer on no context\n",
        "  results = []\n",
        "  for quote_idx, quote in tqdm(quotes.iterrows(), total = quotes.shape[0], desc = \"No context quotes\"):\n",
        "    # break\n",
        "    prompt_text = model_prompt(quote[\"quoteText\"])\n",
        "    sequences = pipe(\n",
        "        prompt_text,\n",
        "        return_full_text=False,\n",
        "        max_new_tokens=100,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        )\n",
        "\n",
        "    speaker_gen = \"\"\n",
        "    for out in sequences:\n",
        "      speaker_gen += out[\"generated_text\"]\n",
        "    # speaker_gen = sequences[0][\"generated_text\"]\n",
        "    results.append([prompt_text, speaker_gen])\n",
        "\n",
        "  results_df = pd.DataFrame(results, columns=[\"prompt_text\", \"inferred_speaker\"])\n",
        "  save_dir = f\"results/context0/{model_name}\"\n",
        "  if not os.path.exists(save_dir):\n",
        "    os.makedirs(save_dir)\n",
        "  results_df.to_csv(f\"{save_dir}/{novel}.csv\", index=False)\n",
        "\n",
        "  # Infer on contexts\n",
        "  for c_idx, context in enumerate(tqdm(context_lengths, desc=\"Context lengths\")):\n",
        "    results = []\n",
        "    for idx, quote in tqdm(quotes.iterrows(), desc=\"Quotes\", total=quotes.shape[0]):\n",
        "      context_row = contexts[c_idx].iloc[idx]\n",
        "      prompt_text = model_context_prompt(quote[\"quoteText\"], context_row[\"left_context\"], context_row[\"right_context\"])\n",
        "      sequences = pipe(\n",
        "          prompt_text,\n",
        "          return_full_text=False,\n",
        "          max_new_tokens=100,\n",
        "          pad_token_id=tokenizer.eos_token_id,\n",
        "          )\n",
        "\n",
        "      speaker_gen = \"\"\n",
        "      for out in sequences:\n",
        "        speaker_gen += out[\"generated_text\"]\n",
        "      # speaker_gen = sequences[0][\"generated_text\"]\n",
        "      results.append([prompt_text, speaker_gen])\n",
        "\n",
        "      # if idx == 10:\n",
        "      #   break\n",
        "\n",
        "    results_df = pd.DataFrame(results, columns=[\"prompt_text\", \"inferred_speaker\"])\n",
        "    save_dir = f\"results/context{context}/{model_name}\"\n",
        "    if not os.path.exists(save_dir):\n",
        "      os.makedirs(save_dir)\n",
        "    results_df.to_csv(f\"{save_dir}/{novel}.csv\", index=False)\n",
        "  del pipe\n",
        "  flush()\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mistral with INST\n",
        "infer(\"PrideAndPrejudice\", \"Mistral 7b INST\", \"mistralai/Mistral-7B-Instruct-v0.2\", mistral_prompt, mistral_context_prompt, p_quotes, p_contexts)\n",
        "infer(\"Emma\", \"Mistral 7b INST\", \"mistralai/Mistral-7B-Instruct-v0.2\", mistral_prompt, mistral_context_prompt, e_quotes, e_contexts)\n",
        "\n",
        "# # Mistral without INST\n",
        "infer(\"PrideAndPrejudice\", \"Mistral 7b NO INST\", \"mistralai/Mistral-7B-Instruct-v0.2\", llama_prompt, llama_context_prompt, p_quotes, p_contexts)\n",
        "infer(\"Emma\", \"Mistral 7b NO INST\", \"mistralai/Mistral-7B-Instruct-v0.2\", llama_prompt, llama_context_prompt, e_quotes, e_contexts)\n",
        "\n",
        "# Llama 13\n",
        "infer(\"PrideAndPrejudice\", \"Llama 13b\", \"meta-llama/Llama-2-13b-chat-hf\", llama_prompt, llama_context_prompt, p_quotes, p_contexts)\n",
        "infer(\"Emma\", \"Llama 13b\", \"meta-llama/Llama-2-13b-chat-hf\", llama_prompt, llama_context_prompt, e_quotes, e_contexts)\n",
        "\n",
        "# Llama 7\n",
        "infer(\"PrideAndPrejudice\", \"Llama 7b\", \"meta-llama/Llama-2-7b-chat-hf\", llama_prompt, llama_context_prompt, p_quotes, p_contexts)\n",
        "infer(\"Emma\", \"Llama 7b\", \"meta-llama/Llama-2-7b-chat-hf\", llama_prompt, llama_context_prompt, e_quotes, e_contexts)\n",
        "\n"
      ],
      "metadata": {
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        },
        "id": "Ntld0a4CsGLL",
        "outputId": "d60eb08b-df24-45fc-ded4-562eb4d57b04"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41cf94d27d2f4cea8faae192205765b7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00003.safetensors:   0%|          | 0.00/9.95G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97cf7274c5854881a0bbcf28bd2252f1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9982579e21f84ad9bc4ce0a317ccfee8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a153d99d45194c9d99faac3402b73135"
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          "metadata": {}
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        {
          "output_type": "display_data",
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      "source": [
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    },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: content/results/ (stored 0%)\n",
            "updating: content/results/context8/ (stored 0%)\n",
            "updating: content/results/context8/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context8/Llama 7b/Emma.csv (deflated 90%)\n",
            "updating: content/results/context8/Llama 7b/PrideAndPrejudice.csv (deflated 90%)\n",
            "updating: content/results/context4/ (stored 0%)\n",
            "updating: content/results/context4/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context4/Llama 7b/Emma.csv (deflated 87%)\n",
            "updating: content/results/context4/Llama 7b/PrideAndPrejudice.csv (deflated 87%)\n",
            "updating: content/results/context1/ (stored 0%)\n",
            "updating: content/results/context1/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context1/Llama 7b/Emma.csv (deflated 84%)\n",
            "updating: content/results/context1/Llama 7b/PrideAndPrejudice.csv (deflated 84%)\n",
            "updating: content/results/context2/ (stored 0%)\n",
            "updating: content/results/context2/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context2/Llama 7b/Emma.csv (deflated 85%)\n",
            "updating: content/results/context2/Llama 7b/PrideAndPrejudice.csv (deflated 85%)\n",
            "updating: content/results/context16/ (stored 0%)\n",
            "updating: content/results/context16/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context16/Llama 7b/Emma.csv (deflated 94%)\n",
            "updating: content/results/context16/Llama 7b/PrideAndPrejudice.csv (deflated 94%)\n",
            "updating: content/results/context0/ (stored 0%)\n",
            "updating: content/results/context0/Llama 7b/ (stored 0%)\n",
            "updating: content/results/context0/Llama 7b/Emma.csv (deflated 76%)\n",
            "updating: content/results/context0/Llama 7b/PrideAndPrejudice.csv (deflated 77%)\n",
            "  adding: content/results/context8/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context8/Llama 13b/Emma.csv (deflated 90%)\n",
            "  adding: content/results/context8/Llama 13b/PrideAndPrejudice.csv (deflated 90%)\n",
            "  adding: content/results/context4/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context4/Llama 13b/Emma.csv (deflated 87%)\n",
            "  adding: content/results/context4/Llama 13b/PrideAndPrejudice.csv (deflated 87%)\n",
            "  adding: content/results/context1/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context1/Llama 13b/Emma.csv (deflated 83%)\n",
            "  adding: content/results/context1/Llama 13b/PrideAndPrejudice.csv (deflated 83%)\n",
            "  adding: content/results/context2/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context2/Llama 13b/Emma.csv (deflated 85%)\n",
            "  adding: content/results/context2/Llama 13b/PrideAndPrejudice.csv (deflated 84%)\n",
            "  adding: content/results/context16/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context16/Llama 13b/Emma.csv (deflated 94%)\n",
            "  adding: content/results/context16/Llama 13b/PrideAndPrejudice.csv (deflated 94%)\n",
            "  adding: content/results/context0/Llama 13b/ (stored 0%)\n",
            "  adding: content/results/context0/Llama 13b/Emma.csv (deflated 75%)\n",
            "  adding: content/results/context0/Llama 13b/PrideAndPrejudice.csv (deflated 77%)\n"
          ]
        }
      ],
      "source": [
        "!zip -r /content/results.zip /content/results/\n",
        "\n",
        "# from google.colab import files\n",
        "# files.download(\"/content/results.zip\")"
      ]
    }
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